# components.parameters.bbox.description
Only features that have a geometry that intersects the bounding box are selected.The bounding box is provided as four or six numbers, depending on whether the coordinate reference system includes a vertical axis (height or depth).

# components.parameters.bbox-crs.description
Indicates the coordinate reference system for the given bbox coordinates.

# components.parameters.bbox-crs-epsg.description
Indicates the EPSG for the given bbox coordinates.

# components.parameters.crs.description
Indicates the coordinate reference system for the results.

# components.parameters.f.description
The optional f parameter indicates the output format which the server shall provide as part of the response document.  The default format is GeoJSON.

# components.parameters.lang.description
The optional lang parameter instructs the server return a response in a certain language, if supported.  If the language is not among the available values, the Accept-Language header language will be used if it is supported. If the header is missing, the default server language is used. Note that providers may only support a single language (or often no language at all), that can be different from the server language.  Language strings can be written in a complex (e.g. "fr-CA,fr;q=0.9,en-US;q=0.8,en;q=0.7"), simple (e.g. "de") or locale-like (e.g. "de-CH" or "fr_BE") fashion.

# components.parameters.offset.description
The optional offset parameter indicates the index within the result set from which the server shall begin presenting results in the response document.  The first element has an index of 0 (default).

# components.parameters.skipGeometry.description
This option can be used to skip response geometries for each feature.

# components.parameters.vendorSpecificParameters.description
Additional "free-form" parameters that are not explicitly defined

# components.responses.Tiles.description
Retrieves the tiles description for this collection

# components.schemas.queryable.properties.description.description
a human-readable narrative describing the queryable

# components.schemas.queryable.properties.language.description
the language used for the title and description

# components.schemas.queryable.properties.queryable.description
the token that may be used in a CQL predicate

# components.schemas.queryable.properties.title.description
a human readable title for the queryable

# components.schemas.queryable.properties.type-ref.description
a reference to the formal definition of the type

# info.contact.name.en
Government of Canada, Environment and Climate Change Canada, Meteorological Service of Canada

# info.contact.name.fr
Gouvernement du Canada, Environnement et Changement climatique Canada, Service météorologique du Canada

# info.contact.x-ogc-serviceContact.name.en
Government of Canada, Environment and Climate Change Canada, Meteorological Service of Canada

# info.contact.x-ogc-serviceContact.name.fr
Gouvernement du Canada, Environnement et Changement climatique Canada, Service météorologique du Canada

# info.description
GeoMet-OGC-API provides public access to the Meteorological Service of Canada (MSC) and Environment and Climate Change Canada (ECCC) data via interoperable web services and application programming interfaces (API). Through open standards, users can freely and quickly access thousands of real-time and archived weather, climate and water datasets and products and integrate them in their domain-specific applications and decision support systems. Users can build mobile apps, create interactive web maps, and display and animate MSC data in desktop software. MSC GeoMet also enables on demand raw data clipping and reprojection, on demand format conversion and custom visualization.

# info.license.name.en
Environment and Climate Change Canada Data Server End-use Licence

# info.license.name.fr
Licence d'utilisation finale pour les serveurs de données d'Environnement et Changement climatique Canada

# paths./collections/ahccd-annual.get.description
Adjusted and Homogenized Canadian Climate Data (AHCCD) are climate station datasets that incorporate adjustments (derived from statistical procedures) to the original historical station data to account for discontinuities from non-climatic factors, such as instrument changes or station relocation. Data are provided for temperature, precipitation, pressure and wind speed. Station trend data are provided when available. Trends are calculated using the Theil-Sen method using the station's full period of available data. The availability of trends will vary by station; if more than 5 consecutive years are missing data or more than 10% of the data within the time series is missing, a trend was not calculated.

# paths./collections/ahccd-annual/items.get.description
Adjusted and Homogenized Canadian Climate Data (AHCCD) are climate station datasets that incorporate adjustments (derived from statistical procedures) to the original historical station data to account for discontinuities from non-climatic factors, such as instrument changes or station relocation. Data are provided for temperature, precipitation, pressure and wind speed. Station trend data are provided when available. Trends are calculated using the Theil-Sen method using the station's full period of available data. The availability of trends will vary by station; if more than 5 consecutive years are missing data or more than 10% of the data within the time series is missing, a trend was not calculated.

# paths./collections/ahccd-annual/items.get.parameters[].description
The properties that should be included for each feature. The parameter value is a comma-separated list of property names.

# paths./collections/ahccd-annual/items.options.description
Adjusted and Homogenized Canadian Climate Data (AHCCD) are climate station datasets that incorporate adjustments (derived from statistical procedures) to the original historical station data to account for discontinuities from non-climatic factors, such as instrument changes or station relocation. Data are provided for temperature, precipitation, pressure and wind speed. Station trend data are provided when available. Trends are calculated using the Theil-Sen method using the station's full period of available data. The availability of trends will vary by station; if more than 5 consecutive years are missing data or more than 10% of the data within the time series is missing, a trend was not calculated.

# paths./collections/ahccd-annual/items/{featureId}.get.description
Adjusted and Homogenized Canadian Climate Data (AHCCD) are climate station datasets that incorporate adjustments (derived from statistical procedures) to the original historical station data to account for discontinuities from non-climatic factors, such as instrument changes or station relocation. Data are provided for temperature, precipitation, pressure and wind speed. Station trend data are provided when available. Trends are calculated using the Theil-Sen method using the station's full period of available data. The availability of trends will vary by station; if more than 5 consecutive years are missing data or more than 10% of the data within the time series is missing, a trend was not calculated.

# paths./collections/ahccd-annual/items/{featureId}.options.description
Adjusted and Homogenized Canadian Climate Data (AHCCD) are climate station datasets that incorporate adjustments (derived from statistical procedures) to the original historical station data to account for discontinuities from non-climatic factors, such as instrument changes or station relocation. Data are provided for temperature, precipitation, pressure and wind speed. Station trend data are provided when available. Trends are calculated using the Theil-Sen method using the station's full period of available data. The availability of trends will vary by station; if more than 5 consecutive years are missing data or more than 10% of the data within the time series is missing, a trend was not calculated.

# paths./collections/ahccd-annual/queryables.get.description
Adjusted and Homogenized Canadian Climate Data (AHCCD) are climate station datasets that incorporate adjustments (derived from statistical procedures) to the original historical station data to account for discontinuities from non-climatic factors, such as instrument changes or station relocation. Data are provided for temperature, precipitation, pressure and wind speed. Station trend data are provided when available. Trends are calculated using the Theil-Sen method using the station's full period of available data. The availability of trends will vary by station; if more than 5 consecutive years are missing data or more than 10% of the data within the time series is missing, a trend was not calculated.

# paths./collections/ahccd-annual/schema.get.description
Adjusted and Homogenized Canadian Climate Data (AHCCD) are climate station datasets that incorporate adjustments (derived from statistical procedures) to the original historical station data to account for discontinuities from non-climatic factors, such as instrument changes or station relocation. Data are provided for temperature, precipitation, pressure and wind speed. Station trend data are provided when available. Trends are calculated using the Theil-Sen method using the station's full period of available data. The availability of trends will vary by station; if more than 5 consecutive years are missing data or more than 10% of the data within the time series is missing, a trend was not calculated.

# paths./collections/ahccd-monthly.get.description
Adjusted and Homogenized Canadian Climate Data (AHCCD) are climate station datasets that incorporate adjustments (derived from statistical procedures) to the original historical station data to account for discontinuities from non-climatic factors, such as instrument changes or station relocation. Data are provided for temperature, precipitation, pressure and wind speed. Station trend data are provided when available. Trends are calculated using the Theil-Sen method using the station's full period of available data. The availability of trends will vary by station; if more than 5 consecutive years are missing data or more than 10% of the data within the time series is missing, a trend was not calculated.

# paths./collections/ahccd-monthly/items.get.description
Adjusted and Homogenized Canadian Climate Data (AHCCD) are climate station datasets that incorporate adjustments (derived from statistical procedures) to the original historical station data to account for discontinuities from non-climatic factors, such as instrument changes or station relocation. Data are provided for temperature, precipitation, pressure and wind speed. Station trend data are provided when available. Trends are calculated using the Theil-Sen method using the station's full period of available data. The availability of trends will vary by station; if more than 5 consecutive years are missing data or more than 10% of the data within the time series is missing, a trend was not calculated.

# paths./collections/ahccd-monthly/items.get.parameters[].description
The properties that should be included for each feature. The parameter value is a comma-separated list of property names.

# paths./collections/ahccd-monthly/items.options.description
Adjusted and Homogenized Canadian Climate Data (AHCCD) are climate station datasets that incorporate adjustments (derived from statistical procedures) to the original historical station data to account for discontinuities from non-climatic factors, such as instrument changes or station relocation. Data are provided for temperature, precipitation, pressure and wind speed. Station trend data are provided when available. Trends are calculated using the Theil-Sen method using the station's full period of available data. The availability of trends will vary by station; if more than 5 consecutive years are missing data or more than 10% of the data within the time series is missing, a trend was not calculated.

# paths./collections/ahccd-monthly/items/{featureId}.get.description
Adjusted and Homogenized Canadian Climate Data (AHCCD) are climate station datasets that incorporate adjustments (derived from statistical procedures) to the original historical station data to account for discontinuities from non-climatic factors, such as instrument changes or station relocation. Data are provided for temperature, precipitation, pressure and wind speed. Station trend data are provided when available. Trends are calculated using the Theil-Sen method using the station's full period of available data. The availability of trends will vary by station; if more than 5 consecutive years are missing data or more than 10% of the data within the time series is missing, a trend was not calculated.

# paths./collections/ahccd-monthly/items/{featureId}.options.description
Adjusted and Homogenized Canadian Climate Data (AHCCD) are climate station datasets that incorporate adjustments (derived from statistical procedures) to the original historical station data to account for discontinuities from non-climatic factors, such as instrument changes or station relocation. Data are provided for temperature, precipitation, pressure and wind speed. Station trend data are provided when available. Trends are calculated using the Theil-Sen method using the station's full period of available data. The availability of trends will vary by station; if more than 5 consecutive years are missing data or more than 10% of the data within the time series is missing, a trend was not calculated.

# paths./collections/ahccd-monthly/queryables.get.description
Adjusted and Homogenized Canadian Climate Data (AHCCD) are climate station datasets that incorporate adjustments (derived from statistical procedures) to the original historical station data to account for discontinuities from non-climatic factors, such as instrument changes or station relocation. Data are provided for temperature, precipitation, pressure and wind speed. Station trend data are provided when available. Trends are calculated using the Theil-Sen method using the station's full period of available data. The availability of trends will vary by station; if more than 5 consecutive years are missing data or more than 10% of the data within the time series is missing, a trend was not calculated.

# paths./collections/ahccd-monthly/schema.get.description
Adjusted and Homogenized Canadian Climate Data (AHCCD) are climate station datasets that incorporate adjustments (derived from statistical procedures) to the original historical station data to account for discontinuities from non-climatic factors, such as instrument changes or station relocation. Data are provided for temperature, precipitation, pressure and wind speed. Station trend data are provided when available. Trends are calculated using the Theil-Sen method using the station's full period of available data. The availability of trends will vary by station; if more than 5 consecutive years are missing data or more than 10% of the data within the time series is missing, a trend was not calculated.

# paths./collections/ahccd-seasonal.get.description
Adjusted and Homogenized Canadian Climate Data (AHCCD) are climate station datasets that incorporate adjustments (derived from statistical procedures) to the original historical station data to account for discontinuities from non-climatic factors, such as instrument changes or station relocation. Data are provided for temperature, precipitation, pressure and wind speed. Station trend data are provided when available. Trends are calculated using the Theil-Sen method using the station's full period of available data. The availability of trends will vary by station; if more than 5 consecutive years are missing data or more than 10% of the data within the time series is missing, a trend was not calculated.

# paths./collections/ahccd-seasonal/items.get.description
Adjusted and Homogenized Canadian Climate Data (AHCCD) are climate station datasets that incorporate adjustments (derived from statistical procedures) to the original historical station data to account for discontinuities from non-climatic factors, such as instrument changes or station relocation. Data are provided for temperature, precipitation, pressure and wind speed. Station trend data are provided when available. Trends are calculated using the Theil-Sen method using the station's full period of available data. The availability of trends will vary by station; if more than 5 consecutive years are missing data or more than 10% of the data within the time series is missing, a trend was not calculated.

# paths./collections/ahccd-seasonal/items.get.parameters[].description
The properties that should be included for each feature. The parameter value is a comma-separated list of property names.

# paths./collections/ahccd-seasonal/items.options.description
Adjusted and Homogenized Canadian Climate Data (AHCCD) are climate station datasets that incorporate adjustments (derived from statistical procedures) to the original historical station data to account for discontinuities from non-climatic factors, such as instrument changes or station relocation. Data are provided for temperature, precipitation, pressure and wind speed. Station trend data are provided when available. Trends are calculated using the Theil-Sen method using the station's full period of available data. The availability of trends will vary by station; if more than 5 consecutive years are missing data or more than 10% of the data within the time series is missing, a trend was not calculated.

# paths./collections/ahccd-seasonal/items/{featureId}.get.description
Adjusted and Homogenized Canadian Climate Data (AHCCD) are climate station datasets that incorporate adjustments (derived from statistical procedures) to the original historical station data to account for discontinuities from non-climatic factors, such as instrument changes or station relocation. Data are provided for temperature, precipitation, pressure and wind speed. Station trend data are provided when available. Trends are calculated using the Theil-Sen method using the station's full period of available data. The availability of trends will vary by station; if more than 5 consecutive years are missing data or more than 10% of the data within the time series is missing, a trend was not calculated.

# paths./collections/ahccd-seasonal/items/{featureId}.options.description
Adjusted and Homogenized Canadian Climate Data (AHCCD) are climate station datasets that incorporate adjustments (derived from statistical procedures) to the original historical station data to account for discontinuities from non-climatic factors, such as instrument changes or station relocation. Data are provided for temperature, precipitation, pressure and wind speed. Station trend data are provided when available. Trends are calculated using the Theil-Sen method using the station's full period of available data. The availability of trends will vary by station; if more than 5 consecutive years are missing data or more than 10% of the data within the time series is missing, a trend was not calculated.

# paths./collections/ahccd-seasonal/queryables.get.description
Adjusted and Homogenized Canadian Climate Data (AHCCD) are climate station datasets that incorporate adjustments (derived from statistical procedures) to the original historical station data to account for discontinuities from non-climatic factors, such as instrument changes or station relocation. Data are provided for temperature, precipitation, pressure and wind speed. Station trend data are provided when available. Trends are calculated using the Theil-Sen method using the station's full period of available data. The availability of trends will vary by station; if more than 5 consecutive years are missing data or more than 10% of the data within the time series is missing, a trend was not calculated.

# paths./collections/ahccd-seasonal/schema.get.description
Adjusted and Homogenized Canadian Climate Data (AHCCD) are climate station datasets that incorporate adjustments (derived from statistical procedures) to the original historical station data to account for discontinuities from non-climatic factors, such as instrument changes or station relocation. Data are provided for temperature, precipitation, pressure and wind speed. Station trend data are provided when available. Trends are calculated using the Theil-Sen method using the station's full period of available data. The availability of trends will vary by station; if more than 5 consecutive years are missing data or more than 10% of the data within the time series is missing, a trend was not calculated.

# paths./collections/ahccd-stations.get.description
Climate station datasets that incorporate adjustments (derived from statistical procedures) to the original historical station data to account for discontinuities from non-climatic factors, such as instrument changes or station relocation.

# paths./collections/ahccd-stations/items.get.description
Climate station datasets that incorporate adjustments (derived from statistical procedures) to the original historical station data to account for discontinuities from non-climatic factors, such as instrument changes or station relocation.

# paths./collections/ahccd-stations/items.get.parameters[].description
The properties that should be included for each feature. The parameter value is a comma-separated list of property names.

# paths./collections/ahccd-stations/items.options.description
Climate station datasets that incorporate adjustments (derived from statistical procedures) to the original historical station data to account for discontinuities from non-climatic factors, such as instrument changes or station relocation.

# paths./collections/ahccd-stations/items/{featureId}.get.description
Climate station datasets that incorporate adjustments (derived from statistical procedures) to the original historical station data to account for discontinuities from non-climatic factors, such as instrument changes or station relocation.

# paths./collections/ahccd-stations/items/{featureId}.options.description
Climate station datasets that incorporate adjustments (derived from statistical procedures) to the original historical station data to account for discontinuities from non-climatic factors, such as instrument changes or station relocation.

# paths./collections/ahccd-stations/queryables.get.description
Climate station datasets that incorporate adjustments (derived from statistical procedures) to the original historical station data to account for discontinuities from non-climatic factors, such as instrument changes or station relocation.

# paths./collections/ahccd-stations/schema.get.description
Climate station datasets that incorporate adjustments (derived from statistical procedures) to the original historical station data to account for discontinuities from non-climatic factors, such as instrument changes or station relocation.

# paths./collections/ahccd-trends.get.description
Adjusted and Homogenized Canadian Climate Data (AHCCD) are climate station datasets that incorporate adjustments (derived from statistical procedures) to the original historical station data to account for discontinuities from non-climatic factors, such as instrument changes or station relocation. Data are provided for temperature, precipitation, pressure and wind speed. Station trend data are provided when available. Trends are calculated using the Theil-Sen method using the station's full period of available data. The availability of trends will vary by station; if more than 5 consecutive years are missing data or more than 10% of the data within the time series is missing, a trend was not calculated.

# paths./collections/ahccd-trends/items.get.description
Adjusted and Homogenized Canadian Climate Data (AHCCD) are climate station datasets that incorporate adjustments (derived from statistical procedures) to the original historical station data to account for discontinuities from non-climatic factors, such as instrument changes or station relocation. Data are provided for temperature, precipitation, pressure and wind speed. Station trend data are provided when available. Trends are calculated using the Theil-Sen method using the station's full period of available data. The availability of trends will vary by station; if more than 5 consecutive years are missing data or more than 10% of the data within the time series is missing, a trend was not calculated.

# paths./collections/ahccd-trends/items.get.parameters[].description
The properties that should be included for each feature. The parameter value is a comma-separated list of property names.

# paths./collections/ahccd-trends/items.options.description
Adjusted and Homogenized Canadian Climate Data (AHCCD) are climate station datasets that incorporate adjustments (derived from statistical procedures) to the original historical station data to account for discontinuities from non-climatic factors, such as instrument changes or station relocation. Data are provided for temperature, precipitation, pressure and wind speed. Station trend data are provided when available. Trends are calculated using the Theil-Sen method using the station's full period of available data. The availability of trends will vary by station; if more than 5 consecutive years are missing data or more than 10% of the data within the time series is missing, a trend was not calculated.

# paths./collections/ahccd-trends/items/{featureId}.get.description
Adjusted and Homogenized Canadian Climate Data (AHCCD) are climate station datasets that incorporate adjustments (derived from statistical procedures) to the original historical station data to account for discontinuities from non-climatic factors, such as instrument changes or station relocation. Data are provided for temperature, precipitation, pressure and wind speed. Station trend data are provided when available. Trends are calculated using the Theil-Sen method using the station's full period of available data. The availability of trends will vary by station; if more than 5 consecutive years are missing data or more than 10% of the data within the time series is missing, a trend was not calculated.

# paths./collections/ahccd-trends/items/{featureId}.options.description
Adjusted and Homogenized Canadian Climate Data (AHCCD) are climate station datasets that incorporate adjustments (derived from statistical procedures) to the original historical station data to account for discontinuities from non-climatic factors, such as instrument changes or station relocation. Data are provided for temperature, precipitation, pressure and wind speed. Station trend data are provided when available. Trends are calculated using the Theil-Sen method using the station's full period of available data. The availability of trends will vary by station; if more than 5 consecutive years are missing data or more than 10% of the data within the time series is missing, a trend was not calculated.

# paths./collections/ahccd-trends/queryables.get.description
Adjusted and Homogenized Canadian Climate Data (AHCCD) are climate station datasets that incorporate adjustments (derived from statistical procedures) to the original historical station data to account for discontinuities from non-climatic factors, such as instrument changes or station relocation. Data are provided for temperature, precipitation, pressure and wind speed. Station trend data are provided when available. Trends are calculated using the Theil-Sen method using the station's full period of available data. The availability of trends will vary by station; if more than 5 consecutive years are missing data or more than 10% of the data within the time series is missing, a trend was not calculated.

# paths./collections/ahccd-trends/schema.get.description
Adjusted and Homogenized Canadian Climate Data (AHCCD) are climate station datasets that incorporate adjustments (derived from statistical procedures) to the original historical station data to account for discontinuities from non-climatic factors, such as instrument changes or station relocation. Data are provided for temperature, precipitation, pressure and wind speed. Station trend data are provided when available. Trends are calculated using the Theil-Sen method using the station's full period of available data. The availability of trends will vary by station; if more than 5 consecutive years are missing data or more than 10% of the data within the time series is missing, a trend was not calculated.

# paths./collections/aqhi-forecasts-realtime.get.description
The Air Quality Health Index (AQHI) is a scale designed to help quantify the quality of the air in a certain region on a scale from 1 to 10. When the amount of air pollution is very high, the number is reported as 10+. It also includes a category that describes the health risk associated with the index reading (e.g. Low, Moderate, High, or Very High Health Risk). The AQHI is calculated based on the relative risks of a combination of common air pollutants that are known to harm human health, including ground-level ozone, particulate matter, and nitrogen dioxide. The AQHI formulation captures only the short term or acute health risk (exposure of hour or days at a maximum). The formulation of the AQHI may change over time to reflect new understanding associated with air pollution health effects. The AQHI is calculated from data observed in real time, without being verified (quality control).

# paths./collections/aqhi-forecasts-realtime/items.get.description
The Air Quality Health Index (AQHI) is a scale designed to help quantify the quality of the air in a certain region on a scale from 1 to 10. When the amount of air pollution is very high, the number is reported as 10+. It also includes a category that describes the health risk associated with the index reading (e.g. Low, Moderate, High, or Very High Health Risk). The AQHI is calculated based on the relative risks of a combination of common air pollutants that are known to harm human health, including ground-level ozone, particulate matter, and nitrogen dioxide. The AQHI formulation captures only the short term or acute health risk (exposure of hour or days at a maximum). The formulation of the AQHI may change over time to reflect new understanding associated with air pollution health effects. The AQHI is calculated from data observed in real time, without being verified (quality control).

# paths./collections/aqhi-forecasts-realtime/items.get.parameters[].description
The properties that should be included for each feature. The parameter value is a comma-separated list of property names.

# paths./collections/aqhi-forecasts-realtime/items.options.description
The Air Quality Health Index (AQHI) is a scale designed to help quantify the quality of the air in a certain region on a scale from 1 to 10. When the amount of air pollution is very high, the number is reported as 10+. It also includes a category that describes the health risk associated with the index reading (e.g. Low, Moderate, High, or Very High Health Risk). The AQHI is calculated based on the relative risks of a combination of common air pollutants that are known to harm human health, including ground-level ozone, particulate matter, and nitrogen dioxide. The AQHI formulation captures only the short term or acute health risk (exposure of hour or days at a maximum). The formulation of the AQHI may change over time to reflect new understanding associated with air pollution health effects. The AQHI is calculated from data observed in real time, without being verified (quality control).

# paths./collections/aqhi-forecasts-realtime/items/{featureId}.get.description
The Air Quality Health Index (AQHI) is a scale designed to help quantify the quality of the air in a certain region on a scale from 1 to 10. When the amount of air pollution is very high, the number is reported as 10+. It also includes a category that describes the health risk associated with the index reading (e.g. Low, Moderate, High, or Very High Health Risk). The AQHI is calculated based on the relative risks of a combination of common air pollutants that are known to harm human health, including ground-level ozone, particulate matter, and nitrogen dioxide. The AQHI formulation captures only the short term or acute health risk (exposure of hour or days at a maximum). The formulation of the AQHI may change over time to reflect new understanding associated with air pollution health effects. The AQHI is calculated from data observed in real time, without being verified (quality control).

# paths./collections/aqhi-forecasts-realtime/items/{featureId}.options.description
The Air Quality Health Index (AQHI) is a scale designed to help quantify the quality of the air in a certain region on a scale from 1 to 10. When the amount of air pollution is very high, the number is reported as 10+. It also includes a category that describes the health risk associated with the index reading (e.g. Low, Moderate, High, or Very High Health Risk). The AQHI is calculated based on the relative risks of a combination of common air pollutants that are known to harm human health, including ground-level ozone, particulate matter, and nitrogen dioxide. The AQHI formulation captures only the short term or acute health risk (exposure of hour or days at a maximum). The formulation of the AQHI may change over time to reflect new understanding associated with air pollution health effects. The AQHI is calculated from data observed in real time, without being verified (quality control).

# paths./collections/aqhi-forecasts-realtime/queryables.get.description
The Air Quality Health Index (AQHI) is a scale designed to help quantify the quality of the air in a certain region on a scale from 1 to 10. When the amount of air pollution is very high, the number is reported as 10+. It also includes a category that describes the health risk associated with the index reading (e.g. Low, Moderate, High, or Very High Health Risk). The AQHI is calculated based on the relative risks of a combination of common air pollutants that are known to harm human health, including ground-level ozone, particulate matter, and nitrogen dioxide. The AQHI formulation captures only the short term or acute health risk (exposure of hour or days at a maximum). The formulation of the AQHI may change over time to reflect new understanding associated with air pollution health effects. The AQHI is calculated from data observed in real time, without being verified (quality control).

# paths./collections/aqhi-forecasts-realtime/schema.get.description
The Air Quality Health Index (AQHI) is a scale designed to help quantify the quality of the air in a certain region on a scale from 1 to 10. When the amount of air pollution is very high, the number is reported as 10+. It also includes a category that describes the health risk associated with the index reading (e.g. Low, Moderate, High, or Very High Health Risk). The AQHI is calculated based on the relative risks of a combination of common air pollutants that are known to harm human health, including ground-level ozone, particulate matter, and nitrogen dioxide. The AQHI formulation captures only the short term or acute health risk (exposure of hour or days at a maximum). The formulation of the AQHI may change over time to reflect new understanding associated with air pollution health effects. The AQHI is calculated from data observed in real time, without being verified (quality control).

# paths./collections/aqhi-observations-realtime.get.description
The Air Quality Health Index (AQHI) is a scale designed to help quantify the quality of the air in a certain region on a scale from 1 to 10. When the amount of air pollution is very high, the number is reported as 10+. It also includes a category that describes the health risk associated with the index reading (e.g. Low, Moderate, High, or Very High Health Risk). The AQHI is calculated based on the relative risks of a combination of common air pollutants that are known to harm human health, including ground-level ozone, particulate matter, and nitrogen dioxide. The AQHI formulation captures only the short term or acute health risk (exposure of hour or days at a maximum). The formulation of the AQHI may change over time to reflect new understanding associated with air pollution health effects. The AQHI is calculated from data observed in real time, without being verified (quality control).

# paths./collections/aqhi-observations-realtime/items.get.description
The Air Quality Health Index (AQHI) is a scale designed to help quantify the quality of the air in a certain region on a scale from 1 to 10. When the amount of air pollution is very high, the number is reported as 10+. It also includes a category that describes the health risk associated with the index reading (e.g. Low, Moderate, High, or Very High Health Risk). The AQHI is calculated based on the relative risks of a combination of common air pollutants that are known to harm human health, including ground-level ozone, particulate matter, and nitrogen dioxide. The AQHI formulation captures only the short term or acute health risk (exposure of hour or days at a maximum). The formulation of the AQHI may change over time to reflect new understanding associated with air pollution health effects. The AQHI is calculated from data observed in real time, without being verified (quality control).

# paths./collections/aqhi-observations-realtime/items.get.parameters[].description
The properties that should be included for each feature. The parameter value is a comma-separated list of property names.

# paths./collections/aqhi-observations-realtime/items.options.description
The Air Quality Health Index (AQHI) is a scale designed to help quantify the quality of the air in a certain region on a scale from 1 to 10. When the amount of air pollution is very high, the number is reported as 10+. It also includes a category that describes the health risk associated with the index reading (e.g. Low, Moderate, High, or Very High Health Risk). The AQHI is calculated based on the relative risks of a combination of common air pollutants that are known to harm human health, including ground-level ozone, particulate matter, and nitrogen dioxide. The AQHI formulation captures only the short term or acute health risk (exposure of hour or days at a maximum). The formulation of the AQHI may change over time to reflect new understanding associated with air pollution health effects. The AQHI is calculated from data observed in real time, without being verified (quality control).

# paths./collections/aqhi-observations-realtime/items/{featureId}.get.description
The Air Quality Health Index (AQHI) is a scale designed to help quantify the quality of the air in a certain region on a scale from 1 to 10. When the amount of air pollution is very high, the number is reported as 10+. It also includes a category that describes the health risk associated with the index reading (e.g. Low, Moderate, High, or Very High Health Risk). The AQHI is calculated based on the relative risks of a combination of common air pollutants that are known to harm human health, including ground-level ozone, particulate matter, and nitrogen dioxide. The AQHI formulation captures only the short term or acute health risk (exposure of hour or days at a maximum). The formulation of the AQHI may change over time to reflect new understanding associated with air pollution health effects. The AQHI is calculated from data observed in real time, without being verified (quality control).

# paths./collections/aqhi-observations-realtime/items/{featureId}.options.description
The Air Quality Health Index (AQHI) is a scale designed to help quantify the quality of the air in a certain region on a scale from 1 to 10. When the amount of air pollution is very high, the number is reported as 10+. It also includes a category that describes the health risk associated with the index reading (e.g. Low, Moderate, High, or Very High Health Risk). The AQHI is calculated based on the relative risks of a combination of common air pollutants that are known to harm human health, including ground-level ozone, particulate matter, and nitrogen dioxide. The AQHI formulation captures only the short term or acute health risk (exposure of hour or days at a maximum). The formulation of the AQHI may change over time to reflect new understanding associated with air pollution health effects. The AQHI is calculated from data observed in real time, without being verified (quality control).

# paths./collections/aqhi-observations-realtime/items/{featureId}.options.summary
Options for AQHI – Observations item by id

# paths./collections/aqhi-observations-realtime/queryables.get.description
The Air Quality Health Index (AQHI) is a scale designed to help quantify the quality of the air in a certain region on a scale from 1 to 10. When the amount of air pollution is very high, the number is reported as 10+. It also includes a category that describes the health risk associated with the index reading (e.g. Low, Moderate, High, or Very High Health Risk). The AQHI is calculated based on the relative risks of a combination of common air pollutants that are known to harm human health, including ground-level ozone, particulate matter, and nitrogen dioxide. The AQHI formulation captures only the short term or acute health risk (exposure of hour or days at a maximum). The formulation of the AQHI may change over time to reflect new understanding associated with air pollution health effects. The AQHI is calculated from data observed in real time, without being verified (quality control).

# paths./collections/aqhi-observations-realtime/schema.get.description
The Air Quality Health Index (AQHI) is a scale designed to help quantify the quality of the air in a certain region on a scale from 1 to 10. When the amount of air pollution is very high, the number is reported as 10+. It also includes a category that describes the health risk associated with the index reading (e.g. Low, Moderate, High, or Very High Health Risk). The AQHI is calculated based on the relative risks of a combination of common air pollutants that are known to harm human health, including ground-level ozone, particulate matter, and nitrogen dioxide. The AQHI formulation captures only the short term or acute health risk (exposure of hour or days at a maximum). The formulation of the AQHI may change over time to reflect new understanding associated with air pollution health effects. The AQHI is calculated from data observed in real time, without being verified (quality control).

# paths./collections/aqhi-stations.get.description
The Air Quality Health Index (AQHI) is a scale designed to help quantify the quality of the air in a certain region on a scale from 1 to 10. When the amount of air pollution is very high, the number is reported as 10+. It also includes a category that describes the health risk associated with the index reading (e.g. Low, Moderate, High, or Very High Health Risk). The AQHI is calculated based on the relative risks of a combination of common air pollutants that are known to harm human health, including ground-level ozone, particulate matter, and nitrogen dioxide. The AQHI formulation captures only the short term or acute health risk (exposure of hour or days at a maximum). The formulation of the AQHI may change over time to reflect new understanding associated with air pollution health effects. The AQHI is calculated from data observed in real time, without being verified (quality control).

# paths./collections/aqhi-stations/items.get.description
The Air Quality Health Index (AQHI) is a scale designed to help quantify the quality of the air in a certain region on a scale from 1 to 10. When the amount of air pollution is very high, the number is reported as 10+. It also includes a category that describes the health risk associated with the index reading (e.g. Low, Moderate, High, or Very High Health Risk). The AQHI is calculated based on the relative risks of a combination of common air pollutants that are known to harm human health, including ground-level ozone, particulate matter, and nitrogen dioxide. The AQHI formulation captures only the short term or acute health risk (exposure of hour or days at a maximum). The formulation of the AQHI may change over time to reflect new understanding associated with air pollution health effects. The AQHI is calculated from data observed in real time, without being verified (quality control).

# paths./collections/aqhi-stations/items.get.parameters[].description
The properties that should be included for each feature. The parameter value is a comma-separated list of property names.

# paths./collections/aqhi-stations/items.options.description
The Air Quality Health Index (AQHI) is a scale designed to help quantify the quality of the air in a certain region on a scale from 1 to 10. When the amount of air pollution is very high, the number is reported as 10+. It also includes a category that describes the health risk associated with the index reading (e.g. Low, Moderate, High, or Very High Health Risk). The AQHI is calculated based on the relative risks of a combination of common air pollutants that are known to harm human health, including ground-level ozone, particulate matter, and nitrogen dioxide. The AQHI formulation captures only the short term or acute health risk (exposure of hour or days at a maximum). The formulation of the AQHI may change over time to reflect new understanding associated with air pollution health effects. The AQHI is calculated from data observed in real time, without being verified (quality control).

# paths./collections/aqhi-stations/items/{featureId}.get.description
The Air Quality Health Index (AQHI) is a scale designed to help quantify the quality of the air in a certain region on a scale from 1 to 10. When the amount of air pollution is very high, the number is reported as 10+. It also includes a category that describes the health risk associated with the index reading (e.g. Low, Moderate, High, or Very High Health Risk). The AQHI is calculated based on the relative risks of a combination of common air pollutants that are known to harm human health, including ground-level ozone, particulate matter, and nitrogen dioxide. The AQHI formulation captures only the short term or acute health risk (exposure of hour or days at a maximum). The formulation of the AQHI may change over time to reflect new understanding associated with air pollution health effects. The AQHI is calculated from data observed in real time, without being verified (quality control).

# paths./collections/aqhi-stations/items/{featureId}.options.description
The Air Quality Health Index (AQHI) is a scale designed to help quantify the quality of the air in a certain region on a scale from 1 to 10. When the amount of air pollution is very high, the number is reported as 10+. It also includes a category that describes the health risk associated with the index reading (e.g. Low, Moderate, High, or Very High Health Risk). The AQHI is calculated based on the relative risks of a combination of common air pollutants that are known to harm human health, including ground-level ozone, particulate matter, and nitrogen dioxide. The AQHI formulation captures only the short term or acute health risk (exposure of hour or days at a maximum). The formulation of the AQHI may change over time to reflect new understanding associated with air pollution health effects. The AQHI is calculated from data observed in real time, without being verified (quality control).

# paths./collections/aqhi-stations/queryables.get.description
The Air Quality Health Index (AQHI) is a scale designed to help quantify the quality of the air in a certain region on a scale from 1 to 10. When the amount of air pollution is very high, the number is reported as 10+. It also includes a category that describes the health risk associated with the index reading (e.g. Low, Moderate, High, or Very High Health Risk). The AQHI is calculated based on the relative risks of a combination of common air pollutants that are known to harm human health, including ground-level ozone, particulate matter, and nitrogen dioxide. The AQHI formulation captures only the short term or acute health risk (exposure of hour or days at a maximum). The formulation of the AQHI may change over time to reflect new understanding associated with air pollution health effects. The AQHI is calculated from data observed in real time, without being verified (quality control).

# paths./collections/aqhi-stations/schema.get.description
The Air Quality Health Index (AQHI) is a scale designed to help quantify the quality of the air in a certain region on a scale from 1 to 10. When the amount of air pollution is very high, the number is reported as 10+. It also includes a category that describes the health risk associated with the index reading (e.g. Low, Moderate, High, or Very High Health Risk). The AQHI is calculated based on the relative risks of a combination of common air pollutants that are known to harm human health, including ground-level ozone, particulate matter, and nitrogen dioxide. The AQHI formulation captures only the short term or acute health risk (exposure of hour or days at a maximum). The formulation of the AQHI may change over time to reflect new understanding associated with air pollution health effects. The AQHI is calculated from data observed in real time, without being verified (quality control).

# paths./collections/bulletins-realtime.get.description
Real-time meteorological bulletins (last 140 days)

# paths./collections/bulletins-realtime/items.get.description
Real-time meteorological bulletins (last 140 days)

# paths./collections/bulletins-realtime/items.get.parameters[].description
The properties that should be included for each feature. The parameter value is a comma-separated list of property names.

# paths./collections/bulletins-realtime/items.options.description
Real-time meteorological bulletins (last 140 days)

# paths./collections/bulletins-realtime/items.options.summary
Options for Meteorological Bulletins items

# paths./collections/bulletins-realtime/items/{featureId}.get.description
Real-time meteorological bulletins (last 140 days)

# paths./collections/bulletins-realtime/items/{featureId}.options.description
Real-time meteorological bulletins (last 140 days)

# paths./collections/bulletins-realtime/items/{featureId}.options.summary
Options for Meteorological Bulletins item by id

# paths./collections/bulletins-realtime/queryables.get.description
Real-time meteorological bulletins (last 140 days)

# paths./collections/bulletins-realtime/schema.get.description
Real-time meteorological bulletins (last 140 days)

# paths./collections/citypageweather-realtime.get.description
Provides current conditions, forecasts, warnings and regional normals for a selection of Canadian cities.

# paths./collections/citypageweather-realtime.get.summary
Get City Page Weather [experimental] metadata

# paths./collections/citypageweather-realtime/items.get.description
Provides current conditions, forecasts, warnings and regional normals for a selection of Canadian cities.

# paths./collections/citypageweather-realtime/items.get.parameters[].description
The properties that should be included for each feature. The parameter value is a comma-separated list of property names.

# paths./collections/citypageweather-realtime/items.get.summary
Get City Page Weather [experimental] items

# paths./collections/citypageweather-realtime/items.options.description
Provides current conditions, forecasts, warnings and regional normals for a selection of Canadian cities.

# paths./collections/citypageweather-realtime/items.options.summary
Options for City Page Weather [experimental] items

# paths./collections/citypageweather-realtime/items/{featureId}.get.description
Provides current conditions, forecasts, warnings and regional normals for a selection of Canadian cities.

# paths./collections/citypageweather-realtime/items/{featureId}.get.summary
Get City Page Weather [experimental] item by id

# paths./collections/citypageweather-realtime/items/{featureId}.options.description
Provides current conditions, forecasts, warnings and regional normals for a selection of Canadian cities.

# paths./collections/citypageweather-realtime/items/{featureId}.options.summary
Options for City Page Weather [experimental] item by id

# paths./collections/citypageweather-realtime/queryables.get.description
Provides current conditions, forecasts, warnings and regional normals for a selection of Canadian cities.

# paths./collections/citypageweather-realtime/queryables.get.summary
Get City Page Weather [experimental] queryables

# paths./collections/citypageweather-realtime/schema.get.description
Provides current conditions, forecasts, warnings and regional normals for a selection of Canadian cities.

# paths./collections/citypageweather-realtime/schema.get.summary
Get City Page Weather [experimental] schema

# paths./collections/climate-daily.get.description
Daily climate observations are derived from two sources of data. The first are Daily Climate Stations producing one or two observations per day of temperature, precipitation. The second are hourly stations that typically produce more weather elements e.g. wind or snow on ground. Only a subset of the total stations is shown due to size limitations. The criteria for station selection are listed as below. The priorities for inclusion are as follows: (1) Station is currently operational, (2) Stations with long periods of record, (3) Stations that are co-located with the categories above and supplement the period of record

# paths./collections/climate-daily.get.summary
Get Climate - Daily Observations metadata

# paths./collections/climate-daily/items.get.description
Daily climate observations are derived from two sources of data. The first are Daily Climate Stations producing one or two observations per day of temperature, precipitation. The second are hourly stations that typically produce more weather elements e.g. wind or snow on ground. Only a subset of the total stations is shown due to size limitations. The criteria for station selection are listed as below. The priorities for inclusion are as follows: (1) Station is currently operational, (2) Stations with long periods of record, (3) Stations that are co-located with the categories above and supplement the period of record

# paths./collections/climate-daily/items.get.parameters[].description
The properties that should be included for each feature. The parameter value is a comma-separated list of property names.

# paths./collections/climate-daily/items.options.description
Daily climate observations are derived from two sources of data. The first are Daily Climate Stations producing one or two observations per day of temperature, precipitation. The second are hourly stations that typically produce more weather elements e.g. wind or snow on ground. Only a subset of the total stations is shown due to size limitations. The criteria for station selection are listed as below. The priorities for inclusion are as follows: (1) Station is currently operational, (2) Stations with long periods of record, (3) Stations that are co-located with the categories above and supplement the period of record

# paths./collections/climate-daily/items.options.summary
Options for Climate - Daily Observations items

# paths./collections/climate-daily/items/{featureId}.get.description
Daily climate observations are derived from two sources of data. The first are Daily Climate Stations producing one or two observations per day of temperature, precipitation. The second are hourly stations that typically produce more weather elements e.g. wind or snow on ground. Only a subset of the total stations is shown due to size limitations. The criteria for station selection are listed as below. The priorities for inclusion are as follows: (1) Station is currently operational, (2) Stations with long periods of record, (3) Stations that are co-located with the categories above and supplement the period of record

# paths./collections/climate-daily/items/{featureId}.get.summary
Get Climate - Daily Observations item by id

# paths./collections/climate-daily/items/{featureId}.options.description
Daily climate observations are derived from two sources of data. The first are Daily Climate Stations producing one or two observations per day of temperature, precipitation. The second are hourly stations that typically produce more weather elements e.g. wind or snow on ground. Only a subset of the total stations is shown due to size limitations. The criteria for station selection are listed as below. The priorities for inclusion are as follows: (1) Station is currently operational, (2) Stations with long periods of record, (3) Stations that are co-located with the categories above and supplement the period of record

# paths./collections/climate-daily/items/{featureId}.options.summary
Options for Climate - Daily Observations item by id

# paths./collections/climate-daily/queryables.get.description
Daily climate observations are derived from two sources of data. The first are Daily Climate Stations producing one or two observations per day of temperature, precipitation. The second are hourly stations that typically produce more weather elements e.g. wind or snow on ground. Only a subset of the total stations is shown due to size limitations. The criteria for station selection are listed as below. The priorities for inclusion are as follows: (1) Station is currently operational, (2) Stations with long periods of record, (3) Stations that are co-located with the categories above and supplement the period of record

# paths./collections/climate-daily/queryables.get.summary
Get Climate - Daily Observations queryables

# paths./collections/climate-daily/schema.get.description
Daily climate observations are derived from two sources of data. The first are Daily Climate Stations producing one or two observations per day of temperature, precipitation. The second are hourly stations that typically produce more weather elements e.g. wind or snow on ground. Only a subset of the total stations is shown due to size limitations. The criteria for station selection are listed as below. The priorities for inclusion are as follows: (1) Station is currently operational, (2) Stations with long periods of record, (3) Stations that are co-located with the categories above and supplement the period of record

# paths./collections/climate-hourly.get.description
Canadian hourly climate data are available for public access from the ECCC/MSC's National Climate Archive. These are surface weather stations that produce hourly meteorological observations, taken each hour of the day. Only a subset of the total stations found on Environment and Climate Change Canada’s Historical Climate Data Page is shown due to size limitations.The priorities for inclusion are as follows: stations in cities with populations of 10000+, stations that are Regional Basic Climatological Network status and stations with 30+ years of data.

# paths./collections/climate-hourly.get.summary
Get Climate - Hourly Observations metadata

# paths./collections/climate-hourly/items.get.description
Canadian hourly climate data are available for public access from the ECCC/MSC's National Climate Archive. These are surface weather stations that produce hourly meteorological observations, taken each hour of the day. Only a subset of the total stations found on Environment and Climate Change Canada’s Historical Climate Data Page is shown due to size limitations.The priorities for inclusion are as follows: stations in cities with populations of 10000+, stations that are Regional Basic Climatological Network status and stations with 30+ years of data.

# paths./collections/climate-hourly/items.get.parameters[].description
The properties that should be included for each feature. The parameter value is a comma-separated list of property names.

# paths./collections/climate-hourly/items.options.description
Canadian hourly climate data are available for public access from the ECCC/MSC's National Climate Archive. These are surface weather stations that produce hourly meteorological observations, taken each hour of the day. Only a subset of the total stations found on Environment and Climate Change Canada’s Historical Climate Data Page is shown due to size limitations.The priorities for inclusion are as follows: stations in cities with populations of 10000+, stations that are Regional Basic Climatological Network status and stations with 30+ years of data.

# paths./collections/climate-hourly/items.options.summary
Options for Climate - Hourly Observations items

# paths./collections/climate-hourly/items/{featureId}.get.description
Canadian hourly climate data are available for public access from the ECCC/MSC's National Climate Archive. These are surface weather stations that produce hourly meteorological observations, taken each hour of the day. Only a subset of the total stations found on Environment and Climate Change Canada’s Historical Climate Data Page is shown due to size limitations.The priorities for inclusion are as follows: stations in cities with populations of 10000+, stations that are Regional Basic Climatological Network status and stations with 30+ years of data.

# paths./collections/climate-hourly/items/{featureId}.get.summary
Get Climate - Hourly Observations item by id

# paths./collections/climate-hourly/items/{featureId}.options.description
Canadian hourly climate data are available for public access from the ECCC/MSC's National Climate Archive. These are surface weather stations that produce hourly meteorological observations, taken each hour of the day. Only a subset of the total stations found on Environment and Climate Change Canada’s Historical Climate Data Page is shown due to size limitations.The priorities for inclusion are as follows: stations in cities with populations of 10000+, stations that are Regional Basic Climatological Network status and stations with 30+ years of data.

# paths./collections/climate-hourly/items/{featureId}.options.summary
Options for Climate - Hourly Observations item by id

# paths./collections/climate-hourly/queryables.get.description
Canadian hourly climate data are available for public access from the ECCC/MSC's National Climate Archive. These are surface weather stations that produce hourly meteorological observations, taken each hour of the day. Only a subset of the total stations found on Environment and Climate Change Canada’s Historical Climate Data Page is shown due to size limitations.The priorities for inclusion are as follows: stations in cities with populations of 10000+, stations that are Regional Basic Climatological Network status and stations with 30+ years of data.

# paths./collections/climate-hourly/queryables.get.summary
Get Climate - Hourly Observations queryables

# paths./collections/climate-hourly/schema.get.description
Canadian hourly climate data are available for public access from the ECCC/MSC's National Climate Archive. These are surface weather stations that produce hourly meteorological observations, taken each hour of the day. Only a subset of the total stations found on Environment and Climate Change Canada’s Historical Climate Data Page is shown due to size limitations.The priorities for inclusion are as follows: stations in cities with populations of 10000+, stations that are Regional Basic Climatological Network status and stations with 30+ years of data.

# paths./collections/climate-hourly/schema.get.summary
Get Climate - Hourly Observations schema

# paths./collections/climate-monthly.get.description
A cross-country summary of the averages and extremes for the month, including precipitation totals, max-min temperatures, and degree days. This data is available from stations that produce daily data.

# paths./collections/climate-monthly.get.summary
Get Climate - Monthly Observation Summaries metadata

# paths./collections/climate-monthly/items.get.description
A cross-country summary of the averages and extremes for the month, including precipitation totals, max-min temperatures, and degree days. This data is available from stations that produce daily data.

# paths./collections/climate-monthly/items.get.parameters[].description
The properties that should be included for each feature. The parameter value is a comma-separated list of property names.

# paths./collections/climate-monthly/items.get.summary
Get Climate - Monthly Observation Summaries items

# paths./collections/climate-monthly/items.options.description
A cross-country summary of the averages and extremes for the month, including precipitation totals, max-min temperatures, and degree days. This data is available from stations that produce daily data.

# paths./collections/climate-monthly/items.options.summary
Options for Climate - Monthly Observation Summaries items

# paths./collections/climate-monthly/items/{featureId}.get.description
A cross-country summary of the averages and extremes for the month, including precipitation totals, max-min temperatures, and degree days. This data is available from stations that produce daily data.

# paths./collections/climate-monthly/items/{featureId}.get.summary
Get Climate - Monthly Observation Summaries item by id

# paths./collections/climate-monthly/items/{featureId}.options.description
A cross-country summary of the averages and extremes for the month, including precipitation totals, max-min temperatures, and degree days. This data is available from stations that produce daily data.

# paths./collections/climate-monthly/items/{featureId}.options.summary
Options for Climate - Monthly Observation Summaries item by id

# paths./collections/climate-monthly/queryables.get.description
A cross-country summary of the averages and extremes for the month, including precipitation totals, max-min temperatures, and degree days. This data is available from stations that produce daily data.

# paths./collections/climate-monthly/queryables.get.summary
Get Climate - Monthly Observation Summaries queryables

# paths./collections/climate-monthly/schema.get.description
A cross-country summary of the averages and extremes for the month, including precipitation totals, max-min temperatures, and degree days. This data is available from stations that produce daily data.

# paths./collections/climate-monthly/schema.get.summary
Get Climate - Monthly Observation Summaries schema

# paths./collections/climate-normals.get.description
Climate Normals are used to summarize or describe the average climatic conditions of a particular location. At the completion of each decade, Environment and Climate Change Canada updates its climate normals for as many locations and as many climatic characteristics as possible. The climate normals offered here are based on Canadian climate stations with at least 15 years of data between 1981 to 2010.

# paths./collections/climate-normals.get.summary
Get Climate - Normals 1981-2010 metadata

# paths./collections/climate-normals/items.get.description
Climate Normals are used to summarize or describe the average climatic conditions of a particular location. At the completion of each decade, Environment and Climate Change Canada updates its climate normals for as many locations and as many climatic characteristics as possible. The climate normals offered here are based on Canadian climate stations with at least 15 years of data between 1981 to 2010.

# paths./collections/climate-normals/items.get.parameters[].description
The properties that should be included for each feature. The parameter value is a comma-separated list of property names.

# paths./collections/climate-normals/items.options.description
Climate Normals are used to summarize or describe the average climatic conditions of a particular location. At the completion of each decade, Environment and Climate Change Canada updates its climate normals for as many locations and as many climatic characteristics as possible. The climate normals offered here are based on Canadian climate stations with at least 15 years of data between 1981 to 2010.

# paths./collections/climate-normals/items.options.summary
Options for Climate - Normals 1981-2010 items

# paths./collections/climate-normals/items/{featureId}.get.description
Climate Normals are used to summarize or describe the average climatic conditions of a particular location. At the completion of each decade, Environment and Climate Change Canada updates its climate normals for as many locations and as many climatic characteristics as possible. The climate normals offered here are based on Canadian climate stations with at least 15 years of data between 1981 to 2010.

# paths./collections/climate-normals/items/{featureId}.get.summary
Get Climate - Normals 1981-2010 item by id

# paths./collections/climate-normals/items/{featureId}.options.description
Climate Normals are used to summarize or describe the average climatic conditions of a particular location. At the completion of each decade, Environment and Climate Change Canada updates its climate normals for as many locations and as many climatic characteristics as possible. The climate normals offered here are based on Canadian climate stations with at least 15 years of data between 1981 to 2010.

# paths./collections/climate-normals/items/{featureId}.options.summary
Options for Climate - Normals 1981-2010 item by id

# paths./collections/climate-normals/queryables.get.description
Climate Normals are used to summarize or describe the average climatic conditions of a particular location. At the completion of each decade, Environment and Climate Change Canada updates its climate normals for as many locations and as many climatic characteristics as possible. The climate normals offered here are based on Canadian climate stations with at least 15 years of data between 1981 to 2010.

# paths./collections/climate-normals/queryables.get.summary
Get Climate - Normals 1981-2010 queryables

# paths./collections/climate-normals/schema.get.description
Climate Normals are used to summarize or describe the average climatic conditions of a particular location. At the completion of each decade, Environment and Climate Change Canada updates its climate normals for as many locations and as many climatic characteristics as possible. The climate normals offered here are based on Canadian climate stations with at least 15 years of data between 1981 to 2010.

# paths./collections/climate-stations.get.description
Climate observations are derived from two sources of data. The first are Daily Climate Stations producing one or two observations per day of temperature, precipitation. The second are hourly stations that typically produce more weather elements e.g. wind or snow on ground.

# paths./collections/climate-stations/items.get.description
Climate observations are derived from two sources of data. The first are Daily Climate Stations producing one or two observations per day of temperature, precipitation. The second are hourly stations that typically produce more weather elements e.g. wind or snow on ground.

# paths./collections/climate-stations/items.get.parameters[].description
The properties that should be included for each feature. The parameter value is a comma-separated list of property names.

# paths./collections/climate-stations/items.options.description
Climate observations are derived from two sources of data. The first are Daily Climate Stations producing one or two observations per day of temperature, precipitation. The second are hourly stations that typically produce more weather elements e.g. wind or snow on ground.

# paths./collections/climate-stations/items/{featureId}.get.description
Climate observations are derived from two sources of data. The first are Daily Climate Stations producing one or two observations per day of temperature, precipitation. The second are hourly stations that typically produce more weather elements e.g. wind or snow on ground.

# paths./collections/climate-stations/items/{featureId}.options.description
Climate observations are derived from two sources of data. The first are Daily Climate Stations producing one or two observations per day of temperature, precipitation. The second are hourly stations that typically produce more weather elements e.g. wind or snow on ground.

# paths./collections/climate-stations/items/{featureId}.options.summary
Options for Climate - Stations item by id

# paths./collections/climate-stations/queryables.get.description
Climate observations are derived from two sources of data. The first are Daily Climate Stations producing one or two observations per day of temperature, precipitation. The second are hourly stations that typically produce more weather elements e.g. wind or snow on ground.

# paths./collections/climate-stations/schema.get.description
Climate observations are derived from two sources of data. The first are Daily Climate Stations producing one or two observations per day of temperature, precipitation. The second are hourly stations that typically produce more weather elements e.g. wind or snow on ground.

# paths./collections/climate:candcsu6:historical:annual:P30Y-Avg.get.description
CMIP6 climate projections are based on both updated global climate models and new emissions scenarios called “Shared Socioeconomic Pathways” (SSPs). Statistically downscaled datasets have been produced from 26 CMIP6 Global Climate Models (GCMs) under three different emission scenarios (i.e., SSP1-2.6, SSP2-4.5, and SSP5-8.5) using the same downscaling method (Bias Correction/Constructed Analogues with Quantile mapping (BCCAQv2)) and downscaling target data (NRCANmet) as the CMIP5-based downscaled scenarios. Downscaled daily maximum and minimum temperatures (°C) and daily precipitation (mm) are available across Canada at 6x10km grid spatial resolution for the 1950-2014 historical period and for the 2015-2100 period following each of the three emission scenarios.

# paths./collections/climate:candcsu6:historical:annual:P30Y-Avg.get.summary
Get CanDCSU6 - 30 Year Averages of Historical Annual Values metadata

# paths./collections/climate:candcsu6:historical:annual:P30Y-Avg/coverag.get.description
CMIP6 climate projections are based on both updated global climate models and new emissions scenarios called “Shared Socioeconomic Pathways” (SSPs). Statistically downscaled datasets have been produced from 26 CMIP6 Global Climate Models (GCMs) under three different emission scenarios (i.e., SSP1-2.6, SSP2-4.5, and SSP5-8.5) using the same downscaling method (Bias Correction/Constructed Analogues with Quantile mapping (BCCAQv2)) and downscaling target data (NRCANmet) as the CMIP5-based downscaled scenarios. Downscaled daily maximum and minimum temperatures (°C) and daily precipitation (mm) are available across Canada at 6x10km grid spatial resolution for the 1950-2014 historical period and for the 2015-2100 period following each of the three emission scenarios.

# paths./collections/climate:candcsu6:historical:annual:P30Y-Avg/coverag.get.summary
Get CanDCSU6 - 30 Year Averages of Historical Annual Values coverage

# paths./collections/climate:candcsu6:historical:annual:absolute.get.description
CMIP6 climate projections are based on both updated global climate models and new emissions scenarios called “Shared Socioeconomic Pathways” (SSPs). Statistically downscaled datasets have been produced from 26 CMIP6 Global Climate Models (GCMs) under three different emission scenarios (i.e., SSP1-2.6, SSP2-4.5, and SSP5-8.5) using the same downscaling method (Bias Correction/Constructed Analogues with Quantile mapping (BCCAQv2)) and downscaling target data (NRCANmet) as the CMIP5-based downscaled scenarios. Downscaled daily maximum and minimum temperatures (°C) and daily precipitation (mm) are available across Canada at 6x10km grid spatial resolution for the 1950-2014 historical period and for the 2015-2100 period following each of the three emission scenarios.

# paths./collections/climate:candcsu6:historical:annual:absolute.get.summary
Get CanDCSU6 - Historical Annual metadata

# paths./collections/climate:candcsu6:historical:annual:absolute/coverag.get.description
CMIP6 climate projections are based on both updated global climate models and new emissions scenarios called “Shared Socioeconomic Pathways” (SSPs). Statistically downscaled datasets have been produced from 26 CMIP6 Global Climate Models (GCMs) under three different emission scenarios (i.e., SSP1-2.6, SSP2-4.5, and SSP5-8.5) using the same downscaling method (Bias Correction/Constructed Analogues with Quantile mapping (BCCAQv2)) and downscaling target data (NRCANmet) as the CMIP5-based downscaled scenarios. Downscaled daily maximum and minimum temperatures (°C) and daily precipitation (mm) are available across Canada at 6x10km grid spatial resolution for the 1950-2014 historical period and for the 2015-2100 period following each of the three emission scenarios.

# paths./collections/climate:candcsu6:historical:annual:absolute/coverag.get.summary
Get CanDCSU6 - Historical Annual coverage

# paths./collections/climate:candcsu6:historical:monthly:absolute.get.description
CMIP6 climate projections are based on both updated global climate models and new emissions scenarios called “Shared Socioeconomic Pathways” (SSPs). Statistically downscaled datasets have been produced from 26 CMIP6 Global Climate Models (GCMs) under three different emission scenarios (i.e., SSP1-2.6, SSP2-4.5, and SSP5-8.5) using the same downscaling method (Bias Correction/Constructed Analogues with Quantile mapping (BCCAQv2)) and downscaling target data (NRCANmet) as the CMIP5-based downscaled scenarios. Downscaled daily maximum and minimum temperatures (°C) and daily precipitation (mm) are available across Canada at 6x10km grid spatial resolution for the 1950-2014 historical period and for the 2015-2100 period following each of the three emission scenarios.

# paths./collections/climate:candcsu6:historical:monthly:absolute.get.summary
Get CanDCSU6 - Historical Monthly metadata

# paths./collections/climate:candcsu6:historical:monthly:absolute/covera.get.description
CMIP6 climate projections are based on both updated global climate models and new emissions scenarios called “Shared Socioeconomic Pathways” (SSPs). Statistically downscaled datasets have been produced from 26 CMIP6 Global Climate Models (GCMs) under three different emission scenarios (i.e., SSP1-2.6, SSP2-4.5, and SSP5-8.5) using the same downscaling method (Bias Correction/Constructed Analogues with Quantile mapping (BCCAQv2)) and downscaling target data (NRCANmet) as the CMIP5-based downscaled scenarios. Downscaled daily maximum and minimum temperatures (°C) and daily precipitation (mm) are available across Canada at 6x10km grid spatial resolution for the 1950-2014 historical period and for the 2015-2100 period following each of the three emission scenarios.

# paths./collections/climate:candcsu6:historical:monthly:absolute/covera.get.summary
Get CanDCSU6 - Historical Monthly coverage

# paths./collections/climate:candcsu6:historical:seasonal:P30Y-Avg.get.description
CMIP6 climate projections are based on both updated global climate models and new emissions scenarios called “Shared Socioeconomic Pathways” (SSPs). Statistically downscaled datasets have been produced from 26 CMIP6 Global Climate Models (GCMs) under three different emission scenarios (i.e., SSP1-2.6, SSP2-4.5, and SSP5-8.5) using the same downscaling method (Bias Correction/Constructed Analogues with Quantile mapping (BCCAQv2)) and downscaling target data (NRCANmet) as the CMIP5-based downscaled scenarios. Downscaled daily maximum and minimum temperatures (°C) and daily precipitation (mm) are available across Canada at 6x10km grid spatial resolution for the 1950-2014 historical period and for the 2015-2100 period following each of the three emission scenarios.

# paths./collections/climate:candcsu6:historical:seasonal:P30Y-Avg.get.summary
Get CanDCSU6 - 30 Year Averages of Historical Seasonal Values metadata

# paths./collections/climate:candcsu6:historical:seasonal:P30Y-Avg/cover.get.description
CMIP6 climate projections are based on both updated global climate models and new emissions scenarios called “Shared Socioeconomic Pathways” (SSPs). Statistically downscaled datasets have been produced from 26 CMIP6 Global Climate Models (GCMs) under three different emission scenarios (i.e., SSP1-2.6, SSP2-4.5, and SSP5-8.5) using the same downscaling method (Bias Correction/Constructed Analogues with Quantile mapping (BCCAQv2)) and downscaling target data (NRCANmet) as the CMIP5-based downscaled scenarios. Downscaled daily maximum and minimum temperatures (°C) and daily precipitation (mm) are available across Canada at 6x10km grid spatial resolution for the 1950-2014 historical period and for the 2015-2100 period following each of the three emission scenarios.

# paths./collections/climate:candcsu6:historical:seasonal:P30Y-Avg/cover.get.summary
Get CanDCSU6 - 30 Year Averages of Historical Seasonal Values coverage

# paths./collections/climate:candcsu6:historical:seasonal:absolute.get.description
CMIP6 climate projections are based on both updated global climate models and new emissions scenarios called “Shared Socioeconomic Pathways” (SSPs). Statistically downscaled datasets have been produced from 26 CMIP6 Global Climate Models (GCMs) under three different emission scenarios (i.e., SSP1-2.6, SSP2-4.5, and SSP5-8.5) using the same downscaling method (Bias Correction/Constructed Analogues with Quantile mapping (BCCAQv2)) and downscaling target data (NRCANmet) as the CMIP5-based downscaled scenarios. Downscaled daily maximum and minimum temperatures (°C) and daily precipitation (mm) are available across Canada at 6x10km grid spatial resolution for the 1950-2014 historical period and for the 2015-2100 period following each of the three emission scenarios.

# paths./collections/climate:candcsu6:historical:seasonal:absolute.get.summary
Get CanDCSU6 - Historical Seasonal metadata

# paths./collections/climate:candcsu6:historical:seasonal:absolute/cover.get.description
CMIP6 climate projections are based on both updated global climate models and new emissions scenarios called “Shared Socioeconomic Pathways” (SSPs). Statistically downscaled datasets have been produced from 26 CMIP6 Global Climate Models (GCMs) under three different emission scenarios (i.e., SSP1-2.6, SSP2-4.5, and SSP5-8.5) using the same downscaling method (Bias Correction/Constructed Analogues with Quantile mapping (BCCAQv2)) and downscaling target data (NRCANmet) as the CMIP5-based downscaled scenarios. Downscaled daily maximum and minimum temperatures (°C) and daily precipitation (mm) are available across Canada at 6x10km grid spatial resolution for the 1950-2014 historical period and for the 2015-2100 period following each of the three emission scenarios.

# paths./collections/climate:candcsu6:historical:seasonal:absolute/cover.get.summary
Get CanDCSU6 - Historical Seasonal coverage

# paths./collections/climate:candcsu6:projected:annual:P30Y-Avg.get.description
CMIP6 climate projections are based on both updated global climate models and new emissions scenarios called “Shared Socioeconomic Pathways” (SSPs). Statistically downscaled datasets have been produced from 26 CMIP6 Global Climate Models (GCMs) under three different emission scenarios (i.e., SSP1-2.6, SSP2-4.5, and SSP5-8.5) using the same downscaling method (Bias Correction/Constructed Analogues with Quantile mapping (BCCAQv2)) and downscaling target data (NRCANmet) as the CMIP5-based downscaled scenarios. Downscaled daily maximum and minimum temperatures (°C) and daily precipitation (mm) are available across Canada at 6x10km grid spatial resolution for the 1950-2014 historical period and for the 2015-2100 period following each of the three emission scenarios.

# paths./collections/climate:candcsu6:projected:annual:P30Y-Avg.get.summary
Get CanDCSU6 - 30 Year Averages of Projected Annual Anomalies metadata

# paths./collections/climate:candcsu6:projected:annual:P30Y-Avg/coverage.get.description
CMIP6 climate projections are based on both updated global climate models and new emissions scenarios called “Shared Socioeconomic Pathways” (SSPs). Statistically downscaled datasets have been produced from 26 CMIP6 Global Climate Models (GCMs) under three different emission scenarios (i.e., SSP1-2.6, SSP2-4.5, and SSP5-8.5) using the same downscaling method (Bias Correction/Constructed Analogues with Quantile mapping (BCCAQv2)) and downscaling target data (NRCANmet) as the CMIP5-based downscaled scenarios. Downscaled daily maximum and minimum temperatures (°C) and daily precipitation (mm) are available across Canada at 6x10km grid spatial resolution for the 1950-2014 historical period and for the 2015-2100 period following each of the three emission scenarios.

# paths./collections/climate:candcsu6:projected:annual:P30Y-Avg/coverage.get.summary
Get CanDCSU6 - 30 Year Averages of Projected Annual Anomalies coverage

# paths./collections/climate:candcsu6:projected:annual:absolute.get.description
CMIP6 climate projections are based on both updated global climate models and new emissions scenarios called “Shared Socioeconomic Pathways” (SSPs). Statistically downscaled datasets have been produced from 26 CMIP6 Global Climate Models (GCMs) under three different emission scenarios (i.e., SSP1-2.6, SSP2-4.5, and SSP5-8.5) using the same downscaling method (Bias Correction/Constructed Analogues with Quantile mapping (BCCAQv2)) and downscaling target data (NRCANmet) as the CMIP5-based downscaled scenarios. Downscaled daily maximum and minimum temperatures (°C) and daily precipitation (mm) are available across Canada at 6x10km grid spatial resolution for the 1950-2014 historical period and for the 2015-2100 period following each of the three emission scenarios.

# paths./collections/climate:candcsu6:projected:annual:absolute.get.summary
Get CanDCSU6 - Annual Projections metadata

# paths./collections/climate:candcsu6:projected:annual:absolute/coverage.get.description
CMIP6 climate projections are based on both updated global climate models and new emissions scenarios called “Shared Socioeconomic Pathways” (SSPs). Statistically downscaled datasets have been produced from 26 CMIP6 Global Climate Models (GCMs) under three different emission scenarios (i.e., SSP1-2.6, SSP2-4.5, and SSP5-8.5) using the same downscaling method (Bias Correction/Constructed Analogues with Quantile mapping (BCCAQv2)) and downscaling target data (NRCANmet) as the CMIP5-based downscaled scenarios. Downscaled daily maximum and minimum temperatures (°C) and daily precipitation (mm) are available across Canada at 6x10km grid spatial resolution for the 1950-2014 historical period and for the 2015-2100 period following each of the three emission scenarios.

# paths./collections/climate:candcsu6:projected:annual:absolute/coverage.get.summary
Get CanDCSU6 - Annual Projections coverage

# paths./collections/climate:candcsu6:projected:monthly:absolute.get.description
CMIP6 climate projections are based on both updated global climate models and new emissions scenarios called “Shared Socioeconomic Pathways” (SSPs). Statistically downscaled datasets have been produced from 26 CMIP6 Global Climate Models (GCMs) under three different emission scenarios (i.e., SSP1-2.6, SSP2-4.5, and SSP5-8.5) using the same downscaling method (Bias Correction/Constructed Analogues with Quantile mapping (BCCAQv2)) and downscaling target data (NRCANmet) as the CMIP5-based downscaled scenarios. Downscaled daily maximum and minimum temperatures (°C) and daily precipitation (mm) are available across Canada at 6x10km grid spatial resolution for the 1950-2014 historical period and for the 2015-2100 period following each of the three emission scenarios.

# paths./collections/climate:candcsu6:projected:monthly:absolute.get.summary
Get CanDCSU6 - Monthly Projections metadata

# paths./collections/climate:candcsu6:projected:monthly:absolute/coverag.get.description
CMIP6 climate projections are based on both updated global climate models and new emissions scenarios called “Shared Socioeconomic Pathways” (SSPs). Statistically downscaled datasets have been produced from 26 CMIP6 Global Climate Models (GCMs) under three different emission scenarios (i.e., SSP1-2.6, SSP2-4.5, and SSP5-8.5) using the same downscaling method (Bias Correction/Constructed Analogues with Quantile mapping (BCCAQv2)) and downscaling target data (NRCANmet) as the CMIP5-based downscaled scenarios. Downscaled daily maximum and minimum temperatures (°C) and daily precipitation (mm) are available across Canada at 6x10km grid spatial resolution for the 1950-2014 historical period and for the 2015-2100 period following each of the three emission scenarios.

# paths./collections/climate:candcsu6:projected:monthly:absolute/coverag.get.summary
Get CanDCSU6 - Monthly Projections coverage

# paths./collections/climate:candcsu6:projected:seasonal:P30Y-Avg.get.description
CMIP6 climate projections are based on both updated global climate models and new emissions scenarios called “Shared Socioeconomic Pathways” (SSPs). Statistically downscaled datasets have been produced from 26 CMIP6 Global Climate Models (GCMs) under three different emission scenarios (i.e., SSP1-2.6, SSP2-4.5, and SSP5-8.5) using the same downscaling method (Bias Correction/Constructed Analogues with Quantile mapping (BCCAQv2)) and downscaling target data (NRCANmet) as the CMIP5-based downscaled scenarios. Downscaled daily maximum and minimum temperatures (°C) and daily precipitation (mm) are available across Canada at 6x10km grid spatial resolution for the 1950-2014 historical period and for the 2015-2100 period following each of the three emission scenarios.

# paths./collections/climate:candcsu6:projected:seasonal:P30Y-Avg.get.summary
Get CanDCSU6 - 30 Year Averages of Projected Seasonal Anomalies metadata

# paths./collections/climate:candcsu6:projected:seasonal:P30Y-Avg/covera.get.description
CMIP6 climate projections are based on both updated global climate models and new emissions scenarios called “Shared Socioeconomic Pathways” (SSPs). Statistically downscaled datasets have been produced from 26 CMIP6 Global Climate Models (GCMs) under three different emission scenarios (i.e., SSP1-2.6, SSP2-4.5, and SSP5-8.5) using the same downscaling method (Bias Correction/Constructed Analogues with Quantile mapping (BCCAQv2)) and downscaling target data (NRCANmet) as the CMIP5-based downscaled scenarios. Downscaled daily maximum and minimum temperatures (°C) and daily precipitation (mm) are available across Canada at 6x10km grid spatial resolution for the 1950-2014 historical period and for the 2015-2100 period following each of the three emission scenarios.

# paths./collections/climate:candcsu6:projected:seasonal:P30Y-Avg/covera.get.summary
Get CanDCSU6 - 30 Year Averages of Projected Seasonal Anomalies coverage

# paths./collections/climate:candcsu6:projected:seasonal:absolute.get.description
CMIP6 climate projections are based on both updated global climate models and new emissions scenarios called “Shared Socioeconomic Pathways” (SSPs). Statistically downscaled datasets have been produced from 26 CMIP6 Global Climate Models (GCMs) under three different emission scenarios (i.e., SSP1-2.6, SSP2-4.5, and SSP5-8.5) using the same downscaling method (Bias Correction/Constructed Analogues with Quantile mapping (BCCAQv2)) and downscaling target data (NRCANmet) as the CMIP5-based downscaled scenarios. Downscaled daily maximum and minimum temperatures (°C) and daily precipitation (mm) are available across Canada at 6x10km grid spatial resolution for the 1950-2014 historical period and for the 2015-2100 period following each of the three emission scenarios.

# paths./collections/climate:candcsu6:projected:seasonal:absolute.get.summary
Get CanDCSU6 - Seasonal Projections metadata

# paths./collections/climate:candcsu6:projected:seasonal:absolute/covera.get.description
CMIP6 climate projections are based on both updated global climate models and new emissions scenarios called “Shared Socioeconomic Pathways” (SSPs). Statistically downscaled datasets have been produced from 26 CMIP6 Global Climate Models (GCMs) under three different emission scenarios (i.e., SSP1-2.6, SSP2-4.5, and SSP5-8.5) using the same downscaling method (Bias Correction/Constructed Analogues with Quantile mapping (BCCAQv2)) and downscaling target data (NRCANmet) as the CMIP5-based downscaled scenarios. Downscaled daily maximum and minimum temperatures (°C) and daily precipitation (mm) are available across Canada at 6x10km grid spatial resolution for the 1950-2014 historical period and for the 2015-2100 period following each of the three emission scenarios.

# paths./collections/climate:candcsu6:projected:seasonal:absolute/covera.get.summary
Get CanDCSU6 - Seasonal Projections coverage

# paths./collections/climate:cangrd:historical:annual:anomaly.get.description
Gridded annual mean temperature anomalies derived from daily minimum, maximum and mean surface air temperatures (degrees Celsius) and anomalies derived from daily total precipitation is available at a 50km resolution across Canada. The Canadian gridded data (CANGRD) are interpolated from homogenized temperature (i.e., AHCCD datasets). Homogenized temperatures incorporate adjustments to the original station data to account for discontinuities from non-climatic factors, such as instrument changes or station relocation. The anomalies are the difference between the temperature for a given year or season and a baseline value (defined as the average over 1961-1990 as the reference period). The yearly and seasonal temperature anomalies were computed for the years 1948 to 2017. The data will continue to be updated every year. For precipitation, the Canadian gridded data (CANGRD) are interpolated from adjusted precipitation (i.e., AHCCD datasets). Adjusted precipitation data incorporate adjustments to the original station data to account for discontinuities from non-climatic factors, such as instrument changes or station relocation. The anomalies are the percentage difference between the value for a given year or season and a baseline value (defined as the average over 1961-1990 as the reference period). The yearly and seasonal relative precipitation anomalies were computed for the years 1948 to 2014. The data will be updated as time permits.

# paths./collections/climate:cangrd:historical:annual:anomaly.get.summary
Get CanGRD - Historical Annual Anomaly metadata

# paths./collections/climate:cangrd:historical:annual:anomaly/coverage.get.description
Gridded annual mean temperature anomalies derived from daily minimum, maximum and mean surface air temperatures (degrees Celsius) and anomalies derived from daily total precipitation is available at a 50km resolution across Canada. The Canadian gridded data (CANGRD) are interpolated from homogenized temperature (i.e., AHCCD datasets). Homogenized temperatures incorporate adjustments to the original station data to account for discontinuities from non-climatic factors, such as instrument changes or station relocation. The anomalies are the difference between the temperature for a given year or season and a baseline value (defined as the average over 1961-1990 as the reference period). The yearly and seasonal temperature anomalies were computed for the years 1948 to 2017. The data will continue to be updated every year. For precipitation, the Canadian gridded data (CANGRD) are interpolated from adjusted precipitation (i.e., AHCCD datasets). Adjusted precipitation data incorporate adjustments to the original station data to account for discontinuities from non-climatic factors, such as instrument changes or station relocation. The anomalies are the percentage difference between the value for a given year or season and a baseline value (defined as the average over 1961-1990 as the reference period). The yearly and seasonal relative precipitation anomalies were computed for the years 1948 to 2014. The data will be updated as time permits.

# paths./collections/climate:cangrd:historical:annual:anomaly/coverage.get.summary
Get CanGRD - Historical Annual Anomaly coverage

# paths./collections/climate:cangrd:historical:annual:trend.get.description
CANGRD data are interpolated from adjusted and homogenized climate station data (i.e., AHCCD datasets). Homogenized climate data incorporate adjustments to the original station data to account for discontinuities from non-climatic factors, such as instrument changes or station relocation. Annual trends of relative total precipitation change (%) for 1948-2012 based on Canadian gridded data (CANGRD) are available, at a 50km resolution across Canada. The relative trends reflect the percent change in total precipitation over a period from the baseline value (defined as the average over 1961-1990 as the reference period). Annual trends of mean surface air temperature change (degrees Celsius) for 1948-2016 based on Canadian gridded data (CANGRD) are available at a 50km resolution across Canada. Temperature trends represent the departure from a mean reference period (1961-1990).

# paths./collections/climate:cangrd:historical:annual:trend.get.summary
Get CanGRD - Historical Annual Trend metadata

# paths./collections/climate:cangrd:historical:annual:trend/coverage.get.description
CANGRD data are interpolated from adjusted and homogenized climate station data (i.e., AHCCD datasets). Homogenized climate data incorporate adjustments to the original station data to account for discontinuities from non-climatic factors, such as instrument changes or station relocation. Annual trends of relative total precipitation change (%) for 1948-2012 based on Canadian gridded data (CANGRD) are available, at a 50km resolution across Canada. The relative trends reflect the percent change in total precipitation over a period from the baseline value (defined as the average over 1961-1990 as the reference period). Annual trends of mean surface air temperature change (degrees Celsius) for 1948-2016 based on Canadian gridded data (CANGRD) are available at a 50km resolution across Canada. Temperature trends represent the departure from a mean reference period (1961-1990).

# paths./collections/climate:cangrd:historical:annual:trend/coverage.get.summary
Get CanGRD - Historical Annual Trend coverage

# paths./collections/climate:cangrd:historical:monthly:anomaly.get.description
Gridded monthly mean temperature anomalies derived from daily minimum, maximum and mean surface air temperatures (degrees Celsius) and anomalies derived from daily total precipitation is available at a 50km resolution across Canada. The Canadian gridded data (CANGRD) are interpolated from homogenized temperature (i.e., AHCCD datasets). Homogenized temperatures incorporate adjustments to the original station data to account for discontinuities from non-climatic factors, such as instrument changes or station relocation. The anomalies are the difference between the temperature for a given year or season and a baseline value (defined as the average over 1961-1990 as the reference period). The yearly and seasonal temperature anomalies were computed for the years 1948 to 2017. The data will continue to be updated every year. For precipitation, the Canadian gridded data (CANGRD) are interpolated from adjusted precipitation (i.e., AHCCD datasets). Adjusted precipitation data incorporate adjustments to the original station data to account for discontinuities from non-climatic factors, such as instrument changes or station relocation. The anomalies are the percentage difference between the value for a given year or season and a baseline value (defined as the average over 1961-1990 as the reference period). The yearly and seasonal relative precipitation anomalies were computed for the years 1948 to 2014. The data will be updated as time permits.

# paths./collections/climate:cangrd:historical:monthly:anomaly.get.summary
Get CanGRD - Historical Monthly Anomaly metadata

# paths./collections/climate:cangrd:historical:monthly:anomaly/coverage.get.description
Gridded monthly mean temperature anomalies derived from daily minimum, maximum and mean surface air temperatures (degrees Celsius) and anomalies derived from daily total precipitation is available at a 50km resolution across Canada. The Canadian gridded data (CANGRD) are interpolated from homogenized temperature (i.e., AHCCD datasets). Homogenized temperatures incorporate adjustments to the original station data to account for discontinuities from non-climatic factors, such as instrument changes or station relocation. The anomalies are the difference between the temperature for a given year or season and a baseline value (defined as the average over 1961-1990 as the reference period). The yearly and seasonal temperature anomalies were computed for the years 1948 to 2017. The data will continue to be updated every year. For precipitation, the Canadian gridded data (CANGRD) are interpolated from adjusted precipitation (i.e., AHCCD datasets). Adjusted precipitation data incorporate adjustments to the original station data to account for discontinuities from non-climatic factors, such as instrument changes or station relocation. The anomalies are the percentage difference between the value for a given year or season and a baseline value (defined as the average over 1961-1990 as the reference period). The yearly and seasonal relative precipitation anomalies were computed for the years 1948 to 2014. The data will be updated as time permits.

# paths./collections/climate:cangrd:historical:monthly:anomaly/coverage.get.summary
Get CanGRD - Historical Monthly Anomaly coverage

# paths./collections/climate:cangrd:historical:seasonal:anomaly.get.description
Gridded seasonal mean temperature anomalies derived from daily minimum, maximum and mean surface air temperatures (degrees Celsius) and anomalies derived from daily total precipitation is available at a 50km resolution across Canada. The Canadian gridded data (CANGRD) are interpolated from homogenized temperature (i.e., AHCCD datasets). Homogenized temperatures incorporate adjustments to the original station data to account for discontinuities from non-climatic factors, such as instrument changes or station relocation. The anomalies are the difference between the temperature for a given year or season and a baseline value (defined as the average over 1961-1990 as the reference period). The yearly and seasonal temperature anomalies were computed for the years 1948 to 2017. The data will continue to be updated every year. For precipitation, the Canadian gridded data (CANGRD) are interpolated from adjusted precipitation (i.e., AHCCD datasets). Adjusted precipitation data incorporate adjustments to the original station data to account for discontinuities from non-climatic factors, such as instrument changes or station relocation. The anomalies are the percentage difference between the value for a given year or season and a baseline value (defined as the average over 1961-1990 as the reference period). The yearly and seasonal relative precipitation anomalies were computed for the years 1948 to 2014. The data will be updated as time permits.

# paths./collections/climate:cangrd:historical:seasonal:anomaly.get.summary
Get CanGRD - Historical Seasonal Anomaly metadata

# paths./collections/climate:cangrd:historical:seasonal:anomaly/coverage.get.description
Gridded seasonal mean temperature anomalies derived from daily minimum, maximum and mean surface air temperatures (degrees Celsius) and anomalies derived from daily total precipitation is available at a 50km resolution across Canada. The Canadian gridded data (CANGRD) are interpolated from homogenized temperature (i.e., AHCCD datasets). Homogenized temperatures incorporate adjustments to the original station data to account for discontinuities from non-climatic factors, such as instrument changes or station relocation. The anomalies are the difference between the temperature for a given year or season and a baseline value (defined as the average over 1961-1990 as the reference period). The yearly and seasonal temperature anomalies were computed for the years 1948 to 2017. The data will continue to be updated every year. For precipitation, the Canadian gridded data (CANGRD) are interpolated from adjusted precipitation (i.e., AHCCD datasets). Adjusted precipitation data incorporate adjustments to the original station data to account for discontinuities from non-climatic factors, such as instrument changes or station relocation. The anomalies are the percentage difference between the value for a given year or season and a baseline value (defined as the average over 1961-1990 as the reference period). The yearly and seasonal relative precipitation anomalies were computed for the years 1948 to 2014. The data will be updated as time permits.

# paths./collections/climate:cangrd:historical:seasonal:anomaly/coverage.get.summary
Get CanGRD - Historical Seasonal Anomaly coverage

# paths./collections/climate:cangrd:historical:seasonal:trend.get.description
CANGRD data are interpolated from adjusted and homogenized climate station data (i.e., AHCCD datasets). Homogenized climate data incorporate adjustments to the original station data to account for discontinuities from non-climatic factors, such as instrument changes or station relocation. Seasonal trends of relative total precipitation change (%) for 1948-2012 based on Canadian gridded data (CANGRD) are available, at a 50km resolution across Canada. The relative trends reflect the percent change in total precipitation over a period from the baseline value (defined as the average over 1961-1990 as the reference period). Seasonal trends of mean surface air temperature change (degrees Celsius) for 1948-2016 based on Canadian gridded data (CANGRD) are available at a 50km resolution across Canada. Temperature trends represent the departure from a mean reference period (1961-1990).

# paths./collections/climate:cangrd:historical:seasonal:trend.get.summary
Get CanGRD - Historical Seasonal Trend metadata

# paths./collections/climate:cangrd:historical:seasonal:trend/coverage.get.description
CANGRD data are interpolated from adjusted and homogenized climate station data (i.e., AHCCD datasets). Homogenized climate data incorporate adjustments to the original station data to account for discontinuities from non-climatic factors, such as instrument changes or station relocation. Seasonal trends of relative total precipitation change (%) for 1948-2012 based on Canadian gridded data (CANGRD) are available, at a 50km resolution across Canada. The relative trends reflect the percent change in total precipitation over a period from the baseline value (defined as the average over 1961-1990 as the reference period). Seasonal trends of mean surface air temperature change (degrees Celsius) for 1948-2016 based on Canadian gridded data (CANGRD) are available at a 50km resolution across Canada. Temperature trends represent the departure from a mean reference period (1961-1990).

# paths./collections/climate:cangrd:historical:seasonal:trend/coverage.get.summary
Get CanGRD - Historical Seasonal Trend coverage

# paths./collections/climate:cmip5:historical:annual:absolute.get.description
The Global climate model scenarios dataset is based on an ensemble of global climate model projections from the Coupled Model Intercomparison Project Phase 5 (CMIP5). Multi-model ensembles of modelled output (actual value) and projected change (anomaly) are available for historical simulations and three emission scenarios at a 1x1 degree grid resolution. Projected changes are expressed as anomalies with respect to the reference period of 1986-2005. A range of percentiles across the multi-model ensembles are available for download.

# paths./collections/climate:cmip5:historical:annual:absolute/coverage.get.description
The Global climate model scenarios dataset is based on an ensemble of global climate model projections from the Coupled Model Intercomparison Project Phase 5 (CMIP5). Multi-model ensembles of modelled output (actual value) and projected change (anomaly) are available for historical simulations and three emission scenarios at a 1x1 degree grid resolution. Projected changes are expressed as anomalies with respect to the reference period of 1986-2005. A range of percentiles across the multi-model ensembles are available for download.

# paths./collections/climate:cmip5:historical:annual:anomaly.get.description
The Global climate model scenarios dataset is based on an ensemble of global climate model projections from the Coupled Model Intercomparison Project Phase 5 (CMIP5). Multi-model ensembles of modelled output (actual value) and projected change (anomaly) are available for historical simulations and three emission scenarios at a 1x1 degree grid resolution. Projected changes are expressed as anomalies with respect to the reference period of 1986-2005. A range of percentiles across the multi-model ensembles are available for download.

# paths./collections/climate:cmip5:historical:annual:anomaly.get.summary
Get CMIP5 - Historical Annual Anomaly metadata

# paths./collections/climate:cmip5:historical:annual:anomaly/coverage.get.description
The Global climate model scenarios dataset is based on an ensemble of global climate model projections from the Coupled Model Intercomparison Project Phase 5 (CMIP5). Multi-model ensembles of modelled output (actual value) and projected change (anomaly) are available for historical simulations and three emission scenarios at a 1x1 degree grid resolution. Projected changes are expressed as anomalies with respect to the reference period of 1986-2005. A range of percentiles across the multi-model ensembles are available for download.

# paths./collections/climate:cmip5:historical:annual:anomaly/coverage.get.summary
Get CMIP5 - Historical Annual Anomaly coverage

# paths./collections/climate:cmip5:historical:monthly:absolute.get.description
The Global climate model scenarios dataset is based on an ensemble of global climate model projections from the Coupled Model Intercomparison Project Phase 5 (CMIP5). Multi-model ensembles of modelled output (actual value) and projected change (anomaly) are available for historical simulations and three emission scenarios at a 1x1 degree grid resolution. Projected changes are expressed as anomalies with respect to the reference period of 1986-2005. A range of percentiles across the multi-model ensembles are available for download.

# paths./collections/climate:cmip5:historical:monthly:absolute/coverage.get.description
The Global climate model scenarios dataset is based on an ensemble of global climate model projections from the Coupled Model Intercomparison Project Phase 5 (CMIP5). Multi-model ensembles of modelled output (actual value) and projected change (anomaly) are available for historical simulations and three emission scenarios at a 1x1 degree grid resolution. Projected changes are expressed as anomalies with respect to the reference period of 1986-2005. A range of percentiles across the multi-model ensembles are available for download.

# paths./collections/climate:cmip5:historical:monthly:anomaly.get.description
The Global climate model scenarios dataset is based on an ensemble of global climate model projections from the Coupled Model Intercomparison Project Phase 5 (CMIP5). Multi-model ensembles of modelled output (actual value) and projected change (anomaly) are available for historical simulations and three emission scenarios at a 1x1 degree grid resolution. Projected changes are expressed as anomalies with respect to the reference period of 1986-2005. A range of percentiles across the multi-model ensembles are available for download.

# paths./collections/climate:cmip5:historical:monthly:anomaly.get.summary
Get CMIP5 - Historical Monthly Anomaly metadata

# paths./collections/climate:cmip5:historical:monthly:anomaly/coverage.get.description
The Global climate model scenarios dataset is based on an ensemble of global climate model projections from the Coupled Model Intercomparison Project Phase 5 (CMIP5). Multi-model ensembles of modelled output (actual value) and projected change (anomaly) are available for historical simulations and three emission scenarios at a 1x1 degree grid resolution. Projected changes are expressed as anomalies with respect to the reference period of 1986-2005. A range of percentiles across the multi-model ensembles are available for download.

# paths./collections/climate:cmip5:historical:monthly:anomaly/coverage.get.summary
Get CMIP5 - Historical Monthly Anomaly coverage

# paths./collections/climate:cmip5:historical:seasonal:absolute.get.description
The Global climate model scenarios dataset is based on an ensemble of global climate model projections from the Coupled Model Intercomparison Project Phase 5 (CMIP5). Multi-model ensembles of modelled output (actual value) and projected change (anomaly) are available for historical simulations and three emission scenarios at a 1x1 degree grid resolution. Projected changes are expressed as anomalies with respect to the reference period of 1986-2005. A range of percentiles across the multi-model ensembles are available for download.

# paths./collections/climate:cmip5:historical:seasonal:absolute.get.summary
Get CMIP5 - Historical Seasonal metadata

# paths./collections/climate:cmip5:historical:seasonal:absolute/coverage.get.description
The Global climate model scenarios dataset is based on an ensemble of global climate model projections from the Coupled Model Intercomparison Project Phase 5 (CMIP5). Multi-model ensembles of modelled output (actual value) and projected change (anomaly) are available for historical simulations and three emission scenarios at a 1x1 degree grid resolution. Projected changes are expressed as anomalies with respect to the reference period of 1986-2005. A range of percentiles across the multi-model ensembles are available for download.

# paths./collections/climate:cmip5:historical:seasonal:absolute/coverage.get.summary
Get CMIP5 - Historical Seasonal coverage

# paths./collections/climate:cmip5:historical:seasonal:anomaly.get.description
The Global climate model scenarios dataset is based on an ensemble of global climate model projections from the Coupled Model Intercomparison Project Phase 5 (CMIP5). Multi-model ensembles of modelled output (actual value) and projected change (anomaly) are available for historical simulations and three emission scenarios at a 1x1 degree grid resolution. Projected changes are expressed as anomalies with respect to the reference period of 1986-2005. A range of percentiles across the multi-model ensembles are available for download.

# paths./collections/climate:cmip5:historical:seasonal:anomaly.get.summary
Get CMIP5 - Historical Seasonal Anomaly metadata

# paths./collections/climate:cmip5:historical:seasonal:anomaly/coverage.get.description
The Global climate model scenarios dataset is based on an ensemble of global climate model projections from the Coupled Model Intercomparison Project Phase 5 (CMIP5). Multi-model ensembles of modelled output (actual value) and projected change (anomaly) are available for historical simulations and three emission scenarios at a 1x1 degree grid resolution. Projected changes are expressed as anomalies with respect to the reference period of 1986-2005. A range of percentiles across the multi-model ensembles are available for download.

# paths./collections/climate:cmip5:historical:seasonal:anomaly/coverage.get.summary
Get CMIP5 - Historical Seasonal Anomaly coverage

# paths./collections/climate:cmip5:projected:annual:P20Y-Avg.get.description
The Global climate model scenarios dataset is based on an ensemble of global climate model projections from the Coupled Model Intercomparison Project Phase 5 (CMIP5). Multi-model ensembles of modelled output (actual value) and projected change (anomaly) are available for historical simulations and three emission scenarios at a 1x1 degree grid resolution. Projected changes are expressed as anomalies with respect to the reference period of 1986-2005. A range of percentiles across the multi-model ensembles are available for download.

# paths./collections/climate:cmip5:projected:annual:P20Y-Avg.get.summary
Get CMIP5- Projected Annual Anomaly for 20 Years Average metadata

# paths./collections/climate:cmip5:projected:annual:P20Y-Avg/coverage.get.description
The Global climate model scenarios dataset is based on an ensemble of global climate model projections from the Coupled Model Intercomparison Project Phase 5 (CMIP5). Multi-model ensembles of modelled output (actual value) and projected change (anomaly) are available for historical simulations and three emission scenarios at a 1x1 degree grid resolution. Projected changes are expressed as anomalies with respect to the reference period of 1986-2005. A range of percentiles across the multi-model ensembles are available for download.

# paths./collections/climate:cmip5:projected:annual:P20Y-Avg/coverage.get.summary
Get CMIP5- Projected Annual Anomaly for 20 Years Average coverage

# paths./collections/climate:cmip5:projected:annual:absolute.get.description
The Global climate model scenarios dataset is based on an ensemble of global climate model projections from the Coupled Model Intercomparison Project Phase 5 (CMIP5). Multi-model ensembles of modelled output (actual value) and projected change (anomaly) are available for historical simulations and three emission scenarios at a 1x1 degree grid resolution. Projected changes are expressed as anomalies with respect to the reference period of 1986-2005. A range of percentiles across the multi-model ensembles are available for download.

# paths./collections/climate:cmip5:projected:annual:absolute/coverage.get.description
The Global climate model scenarios dataset is based on an ensemble of global climate model projections from the Coupled Model Intercomparison Project Phase 5 (CMIP5). Multi-model ensembles of modelled output (actual value) and projected change (anomaly) are available for historical simulations and three emission scenarios at a 1x1 degree grid resolution. Projected changes are expressed as anomalies with respect to the reference period of 1986-2005. A range of percentiles across the multi-model ensembles are available for download.

# paths./collections/climate:cmip5:projected:annual:anomaly.get.description
The Global climate model scenarios dataset is based on an ensemble of global climate model projections from the Coupled Model Intercomparison Project Phase 5 (CMIP5). Multi-model ensembles of modelled output (actual value) and projected change (anomaly) are available for historical simulations and three emission scenarios at a 1x1 degree grid resolution. Projected changes are expressed as anomalies with respect to the reference period of 1986-2005.

# paths./collections/climate:cmip5:projected:annual:anomaly.get.summary
Get CMIP5 - Projected Annual Anomaly metadata

# paths./collections/climate:cmip5:projected:annual:anomaly/coverage.get.description
The Global climate model scenarios dataset is based on an ensemble of global climate model projections from the Coupled Model Intercomparison Project Phase 5 (CMIP5). Multi-model ensembles of modelled output (actual value) and projected change (anomaly) are available for historical simulations and three emission scenarios at a 1x1 degree grid resolution. Projected changes are expressed as anomalies with respect to the reference period of 1986-2005.

# paths./collections/climate:cmip5:projected:annual:anomaly/coverage.get.summary
Get CMIP5 - Projected Annual Anomaly coverage

# paths./collections/climate:cmip5:projected:monthly:absolute.get.description
The Global climate model scenarios dataset is based on an ensemble of global climate model projections from the Coupled Model Intercomparison Project Phase 5 (CMIP5). Multi-model ensembles of modelled output (actual value) and projected change (anomaly) are available for historical simulations and three emission scenarios at a 1x1 degree grid resolution. Projected changes are expressed as anomalies with respect to the reference period of 1986-2005. A range of percentiles across the multi-model ensembles are available for download.

# paths./collections/climate:cmip5:projected:monthly:absolute/coverage.get.description
The Global climate model scenarios dataset is based on an ensemble of global climate model projections from the Coupled Model Intercomparison Project Phase 5 (CMIP5). Multi-model ensembles of modelled output (actual value) and projected change (anomaly) are available for historical simulations and three emission scenarios at a 1x1 degree grid resolution. Projected changes are expressed as anomalies with respect to the reference period of 1986-2005. A range of percentiles across the multi-model ensembles are available for download.

# paths./collections/climate:cmip5:projected:monthly:anomaly.get.description
The Global climate model scenarios dataset is based on an ensemble of global climate model projections from the Coupled Model Intercomparison Project Phase 5 (CMIP5). Multi-model ensembles of modelled output (actual value) and projected change (anomaly) are available for historical simulations and three emission scenarios at a 1x1 degree grid resolution. Projected changes are expressed as anomalies with respect to the reference period of 1986-2005. A range of percentiles across the multi-model ensembles are available for download.

# paths./collections/climate:cmip5:projected:monthly:anomaly.get.summary
Get CMIP5 - Projected Monthly Anomaly metadata

# paths./collections/climate:cmip5:projected:monthly:anomaly/coverage.get.description
The Global climate model scenarios dataset is based on an ensemble of global climate model projections from the Coupled Model Intercomparison Project Phase 5 (CMIP5). Multi-model ensembles of modelled output (actual value) and projected change (anomaly) are available for historical simulations and three emission scenarios at a 1x1 degree grid resolution. Projected changes are expressed as anomalies with respect to the reference period of 1986-2005. A range of percentiles across the multi-model ensembles are available for download.

# paths./collections/climate:cmip5:projected:monthly:anomaly/coverage.get.summary
Get CMIP5 - Projected Monthly Anomaly coverage

# paths./collections/climate:cmip5:projected:seasonal:P20Y-Avg.get.description
The Global climate model scenarios dataset is based on an ensemble of global climate model projections from the Coupled Model Intercomparison Project Phase 5 (CMIP5). Multi-model ensembles of modelled output (actual value) and projected change (anomaly) are available for historical simulations and three emission scenarios at a 1x1 degree grid resolution. Projected changes are expressed as anomalies with respect to the reference period of 1986-2005. A range of percentiles across the multi-model ensembles are available for download.

# paths./collections/climate:cmip5:projected:seasonal:P20Y-Avg.get.summary
Get CMIP5 - Projected Seasonal Anomaly for 20 Years Average metadata

# paths./collections/climate:cmip5:projected:seasonal:P20Y-Avg/coverage.get.description
The Global climate model scenarios dataset is based on an ensemble of global climate model projections from the Coupled Model Intercomparison Project Phase 5 (CMIP5). Multi-model ensembles of modelled output (actual value) and projected change (anomaly) are available for historical simulations and three emission scenarios at a 1x1 degree grid resolution. Projected changes are expressed as anomalies with respect to the reference period of 1986-2005. A range of percentiles across the multi-model ensembles are available for download.

# paths./collections/climate:cmip5:projected:seasonal:P20Y-Avg/coverage.get.summary
Get CMIP5 - Projected Seasonal Anomaly for 20 Years Average coverage

# paths./collections/climate:cmip5:projected:seasonal:absolute.get.description
The Global climate model scenarios dataset is based on an ensemble of global climate model projections from the Coupled Model Intercomparison Project Phase 5 (CMIP5). Multi-model ensembles of modelled output (actual value) and projected change (anomaly) are available for historical simulations and three emission scenarios at a 1x1 degree grid resolution. Projected changes are expressed as anomalies with respect to the reference period of 1986-2005. A range of percentiles across the multi-model ensembles are available for download.

# paths./collections/climate:cmip5:projected:seasonal:absolute/coverage.get.description
The Global climate model scenarios dataset is based on an ensemble of global climate model projections from the Coupled Model Intercomparison Project Phase 5 (CMIP5). Multi-model ensembles of modelled output (actual value) and projected change (anomaly) are available for historical simulations and three emission scenarios at a 1x1 degree grid resolution. Projected changes are expressed as anomalies with respect to the reference period of 1986-2005. A range of percentiles across the multi-model ensembles are available for download.

# paths./collections/climate:cmip5:projected:seasonal:anomaly.get.description
The Global climate model scenarios dataset is based on an ensemble of global climate model projections from the Coupled Model Intercomparison Project Phase 5 (CMIP5). Multi-model ensembles of modelled output (actual value) and projected change (anomaly) are available for historical simulations and three emission scenarios at a 1x1 degree grid resolution. Projected changes are expressed as anomalies with respect to the reference period of 1986-2005. A range of percentiles across the multi-model ensembles are available for download.

# paths./collections/climate:cmip5:projected:seasonal:anomaly.get.summary
Get CMIP5 - Projected Seasonal Anomaly metadata

# paths./collections/climate:cmip5:projected:seasonal:anomaly/coverage.get.description
The Global climate model scenarios dataset is based on an ensemble of global climate model projections from the Coupled Model Intercomparison Project Phase 5 (CMIP5). Multi-model ensembles of modelled output (actual value) and projected change (anomaly) are available for historical simulations and three emission scenarios at a 1x1 degree grid resolution. Projected changes are expressed as anomalies with respect to the reference period of 1986-2005. A range of percentiles across the multi-model ensembles are available for download.

# paths./collections/climate:cmip5:projected:seasonal:anomaly/coverage.get.summary
Get CMIP5 - Projected Seasonal Anomaly coverage

# paths./collections/climate:dcs:historical:annual:absolute.get.description
The statistically downscaled climate scenarios dataset provides multi-model ensembles of modelled output (actual value) and projected change (anomaly) are available for historical simulations and three emission scenarios, RCP2.6, RCP4.5, RCP8.5, at a 10km resolution. Projected changes are expressed as anomalies with respect to the reference period of 1986-2005. Downscaled data are based on global climate model projections from the Coupled Model Intercomparison Project Phase 5 (CMIP5). A range of percentiles across the multi-model ensemble are available for download.

# paths./collections/climate:dcs:historical:annual:absolute/coverage.get.description
The statistically downscaled climate scenarios dataset provides multi-model ensembles of modelled output (actual value) and projected change (anomaly) are available for historical simulations and three emission scenarios, RCP2.6, RCP4.5, RCP8.5, at a 10km resolution. Projected changes are expressed as anomalies with respect to the reference period of 1986-2005. Downscaled data are based on global climate model projections from the Coupled Model Intercomparison Project Phase 5 (CMIP5). A range of percentiles across the multi-model ensemble are available for download.

# paths./collections/climate:dcs:historical:annual:anomaly.get.description
The statistically downscaled climate scenarios dataset provides multi-model ensembles of modelled output (actual value) and projected change (anomaly) are available for historical simulations and three emission scenarios, RCP2.6, RCP4.5, RCP8.5, at a 10km resolution. Projected changes are expressed as anomalies with respect to the reference period of 1986-2005. Downscaled data are based on global climate model projections from the Coupled Model Intercomparison Project Phase 5 (CMIP5). A range of percentiles across the multi-model ensemble are available for download.

# paths./collections/climate:dcs:historical:annual:anomaly.get.summary
Get DCS - Historical Annual Anomaly metadata

# paths./collections/climate:dcs:historical:annual:anomaly/coverage.get.description
The statistically downscaled climate scenarios dataset provides multi-model ensembles of modelled output (actual value) and projected change (anomaly) are available for historical simulations and three emission scenarios, RCP2.6, RCP4.5, RCP8.5, at a 10km resolution. Projected changes are expressed as anomalies with respect to the reference period of 1986-2005. Downscaled data are based on global climate model projections from the Coupled Model Intercomparison Project Phase 5 (CMIP5). A range of percentiles across the multi-model ensemble are available for download.

# paths./collections/climate:dcs:historical:annual:anomaly/coverage.get.summary
Get DCS - Historical Annual Anomaly coverage

# paths./collections/climate:dcs:historical:monthly:absolute.get.description
The statistically downscaled climate scenarios dataset provides multi-model ensembles of modelled output (actual value) and projected change (anomaly) are available for historical simulations and three emission scenarios, RCP2.6, RCP4.5, RCP8.5, at a 10km resolution. Projected changes are expressed as anomalies with respect to the reference period of 1986-2005. Downscaled data are based on global climate model projections from the Coupled Model Intercomparison Project Phase 5 (CMIP5). A range of percentiles across the multi-model ensemble are available for download.

# paths./collections/climate:dcs:historical:monthly:absolute/coverage.get.description
The statistically downscaled climate scenarios dataset provides multi-model ensembles of modelled output (actual value) and projected change (anomaly) are available for historical simulations and three emission scenarios, RCP2.6, RCP4.5, RCP8.5, at a 10km resolution. Projected changes are expressed as anomalies with respect to the reference period of 1986-2005. Downscaled data are based on global climate model projections from the Coupled Model Intercomparison Project Phase 5 (CMIP5). A range of percentiles across the multi-model ensemble are available for download.

# paths./collections/climate:dcs:historical:seasonal:absolute.get.description
The statistically downscaled climate scenarios dataset provides multi-model ensembles of modelled output (actual value) and projected change (anomaly) are available for historical simulations and three emission scenarios, RCP2.6, RCP4.5, RCP8.5, at a 10km resolution. Projected changes are expressed as anomalies with respect to the reference period of 1986-2005. Downscaled data are based on global climate model projections from the Coupled Model Intercomparison Project Phase 5 (CMIP5). A range of percentiles across the multi-model ensemble are available for download.

# paths./collections/climate:dcs:historical:seasonal:absolute/coverage.get.description
The statistically downscaled climate scenarios dataset provides multi-model ensembles of modelled output (actual value) and projected change (anomaly) are available for historical simulations and three emission scenarios, RCP2.6, RCP4.5, RCP8.5, at a 10km resolution. Projected changes are expressed as anomalies with respect to the reference period of 1986-2005. Downscaled data are based on global climate model projections from the Coupled Model Intercomparison Project Phase 5 (CMIP5). A range of percentiles across the multi-model ensemble are available for download.

# paths./collections/climate:dcs:historical:seasonal:anomaly.get.description
The statistically downscaled climate scenarios dataset provides multi-model ensembles of modelled output (actual value) and projected change (anomaly) are available for historical simulations and three emission scenarios, RCP2.6, RCP4.5, RCP8.5, at a 10km resolution. Projected changes are expressed as anomalies with respect to the reference period of 1986-2005. Downscaled data are based on global climate model projections from the Coupled Model Intercomparison Project Phase 5 (CMIP5). A range of percentiles across the multi-model ensemble are available for download.

# paths./collections/climate:dcs:historical:seasonal:anomaly.get.summary
Get DCS - Historical Seasonal Anomaly metadata

# paths./collections/climate:dcs:historical:seasonal:anomaly/coverage.get.description
The statistically downscaled climate scenarios dataset provides multi-model ensembles of modelled output (actual value) and projected change (anomaly) are available for historical simulations and three emission scenarios, RCP2.6, RCP4.5, RCP8.5, at a 10km resolution. Projected changes are expressed as anomalies with respect to the reference period of 1986-2005. Downscaled data are based on global climate model projections from the Coupled Model Intercomparison Project Phase 5 (CMIP5). A range of percentiles across the multi-model ensemble are available for download.

# paths./collections/climate:dcs:historical:seasonal:anomaly/coverage.get.summary
Get DCS - Historical Seasonal Anomaly coverage

# paths./collections/climate:dcs:projected:annual:P20Y-Avg.get.description
The statistically downscaled climate scenarios dataset provides multi-model ensembles of modelled output (actual value) and projected change (anomaly) are available for historical simulations and three emission scenarios, RCP2.6, RCP4.5, RCP8.5, at a 10km resolution. Projected changes are expressed as anomalies with respect to the reference period of 1986-2005. Downscaled data are based on global climate model projections from the Coupled Model Intercomparison Project Phase 5 (CMIP5). A range of percentiles across the multi-model ensemble are available for download.

# paths./collections/climate:dcs:projected:annual:P20Y-Avg.get.summary
Get DCS - Projected Annual Anomaly for 20 Years Average metadata

# paths./collections/climate:dcs:projected:annual:P20Y-Avg/coverage.get.description
The statistically downscaled climate scenarios dataset provides multi-model ensembles of modelled output (actual value) and projected change (anomaly) are available for historical simulations and three emission scenarios, RCP2.6, RCP4.5, RCP8.5, at a 10km resolution. Projected changes are expressed as anomalies with respect to the reference period of 1986-2005. Downscaled data are based on global climate model projections from the Coupled Model Intercomparison Project Phase 5 (CMIP5). A range of percentiles across the multi-model ensemble are available for download.

# paths./collections/climate:dcs:projected:annual:P20Y-Avg/coverage.get.summary
Get DCS - Projected Annual Anomaly for 20 Years Average coverage

# paths./collections/climate:dcs:projected:annual:absolute.get.description
The statistically downscaled climate scenarios dataset provides multi-model ensembles of modelled output (actual value) and projected change (anomaly) are available for historical simulations and three emission scenarios, RCP2.6, RCP4.5, RCP8.5, at a 10km resolution. Projected changes are expressed as anomalies with respect to the reference period of 1986-2005. Downscaled data are based on global climate model projections from the Coupled Model Intercomparison Project Phase 5 (CMIP5). A range of percentiles across the multi-model ensemble are available for download.

# paths./collections/climate:dcs:projected:annual:absolute/coverage.get.description
The statistically downscaled climate scenarios dataset provides multi-model ensembles of modelled output (actual value) and projected change (anomaly) are available for historical simulations and three emission scenarios, RCP2.6, RCP4.5, RCP8.5, at a 10km resolution. Projected changes are expressed as anomalies with respect to the reference period of 1986-2005. Downscaled data are based on global climate model projections from the Coupled Model Intercomparison Project Phase 5 (CMIP5). A range of percentiles across the multi-model ensemble are available for download.

# paths./collections/climate:dcs:projected:annual:anomaly.get.description
The statistically downscaled climate scenarios dataset provides multi-model ensembles of modelled output (actual value) and projected change (anomaly) are available for historical simulations and three emission scenarios, RCP2.6, RCP4.5, RCP8.5, at a 10km resolution. Projected changes are expressed as anomalies with respect to the reference period of 1986-2005. Downscaled data are based on global climate model projections from the Coupled Model Intercomparison Project Phase 5 (CMIP5). A range of percentiles across the multi-model ensemble are available for download.

# paths./collections/climate:dcs:projected:annual:anomaly.get.summary
Get DCS - Projected Annual Anomaly metadata

# paths./collections/climate:dcs:projected:annual:anomaly/coverage.get.description
The statistically downscaled climate scenarios dataset provides multi-model ensembles of modelled output (actual value) and projected change (anomaly) are available for historical simulations and three emission scenarios, RCP2.6, RCP4.5, RCP8.5, at a 10km resolution. Projected changes are expressed as anomalies with respect to the reference period of 1986-2005. Downscaled data are based on global climate model projections from the Coupled Model Intercomparison Project Phase 5 (CMIP5). A range of percentiles across the multi-model ensemble are available for download.

# paths./collections/climate:dcs:projected:annual:anomaly/coverage.get.summary
Get DCS - Projected Annual Anomaly coverage

# paths./collections/climate:dcs:projected:monthly:absolute.get.description
The statistically downscaled climate scenarios dataset provides multi-model ensembles of modelled output (actual value) and projected change (anomaly) are available for historical simulations and three emission scenarios, RCP2.6, RCP4.5, RCP8.5, at a 10km resolution. Projected changes are expressed as anomalies with respect to the reference period of 1986-2005. Downscaled data are based on global climate model projections from the Coupled Model Intercomparison Project Phase 5 (CMIP5). A range of percentiles across the multi-model ensemble are available for download.

# paths./collections/climate:dcs:projected:monthly:absolute/coverage.get.description
The statistically downscaled climate scenarios dataset provides multi-model ensembles of modelled output (actual value) and projected change (anomaly) are available for historical simulations and three emission scenarios, RCP2.6, RCP4.5, RCP8.5, at a 10km resolution. Projected changes are expressed as anomalies with respect to the reference period of 1986-2005. Downscaled data are based on global climate model projections from the Coupled Model Intercomparison Project Phase 5 (CMIP5). A range of percentiles across the multi-model ensemble are available for download.

# paths./collections/climate:dcs:projected:seasonal:P20Y-Avg.get.description
The statistically downscaled climate scenarios dataset provides multi-model ensembles of modelled output (actual value) and projected change (anomaly) are available for historical simulations and three emission scenarios, RCP2.6, RCP4.5, RCP8.5, at a 10km resolution. Projected changes are expressed as anomalies with respect to the reference period of 1986-2005. Downscaled data are based on global climate model projections from the Coupled Model Intercomparison Project Phase 5 (CMIP5). A range of percentiles across the multi-model ensemble are available for download.

# paths./collections/climate:dcs:projected:seasonal:P20Y-Avg.get.summary
Get DCS - Projected Seasonal Anomaly for 20 Years Average metadata

# paths./collections/climate:dcs:projected:seasonal:P20Y-Avg/coverage.get.description
The statistically downscaled climate scenarios dataset provides multi-model ensembles of modelled output (actual value) and projected change (anomaly) are available for historical simulations and three emission scenarios, RCP2.6, RCP4.5, RCP8.5, at a 10km resolution. Projected changes are expressed as anomalies with respect to the reference period of 1986-2005. Downscaled data are based on global climate model projections from the Coupled Model Intercomparison Project Phase 5 (CMIP5). A range of percentiles across the multi-model ensemble are available for download.

# paths./collections/climate:dcs:projected:seasonal:P20Y-Avg/coverage.get.summary
Get DCS - Projected Seasonal Anomaly for 20 Years Average coverage

# paths./collections/climate:dcs:projected:seasonal:absolute.get.description
The statistically downscaled climate scenarios dataset provides multi-model ensembles of modelled output (actual value) and projected change (anomaly) are available for historical simulations and three emission scenarios, RCP2.6, RCP4.5, RCP8.5, at a 10km resolution. Projected changes are expressed as anomalies with respect to the reference period of 1986-2005. Downscaled data are based on global climate model projections from the Coupled Model Intercomparison Project Phase 5 (CMIP5). A range of percentiles across the multi-model ensemble are available for download.

# paths./collections/climate:dcs:projected:seasonal:absolute/coverage.get.description
The statistically downscaled climate scenarios dataset provides multi-model ensembles of modelled output (actual value) and projected change (anomaly) are available for historical simulations and three emission scenarios, RCP2.6, RCP4.5, RCP8.5, at a 10km resolution. Projected changes are expressed as anomalies with respect to the reference period of 1986-2005. Downscaled data are based on global climate model projections from the Coupled Model Intercomparison Project Phase 5 (CMIP5). A range of percentiles across the multi-model ensemble are available for download.

# paths./collections/climate:dcs:projected:seasonal:anomaly.get.description
The statistically downscaled climate scenarios dataset provides multi-model ensembles of modelled output (actual value) and projected change (anomaly) are available for historical simulations and three emission scenarios, RCP2.6, RCP4.5, RCP8.5, at a 10km resolution. Projected changes are expressed as anomalies with respect to the reference period of 1986-2005. Downscaled data are based on global climate model projections from the Coupled Model Intercomparison Project Phase 5 (CMIP5). A range of percentiles across the multi-model ensemble are available for download.

# paths./collections/climate:dcs:projected:seasonal:anomaly.get.summary
Get DCS - Projected Seasonal Anomaly metadata

# paths./collections/climate:dcs:projected:seasonal:anomaly/coverage.get.description
The statistically downscaled climate scenarios dataset provides multi-model ensembles of modelled output (actual value) and projected change (anomaly) are available for historical simulations and three emission scenarios, RCP2.6, RCP4.5, RCP8.5, at a 10km resolution. Projected changes are expressed as anomalies with respect to the reference period of 1986-2005. Downscaled data are based on global climate model projections from the Coupled Model Intercomparison Project Phase 5 (CMIP5). A range of percentiles across the multi-model ensemble are available for download.

# paths./collections/climate:dcs:projected:seasonal:anomaly/coverage.get.summary
Get DCS - Projected Seasonal Anomaly coverage

# paths./collections/climate:indices:historical.get.description
High-resolution statistically downscaled climate indices based on model projections from 24 global climate models (GCMs) that participated in the Coupled Model Intercomparison Project Phase 5 (CMIP5) are available. The climate indices are grouped into temperature-related, precipitation-related and agroclimatic categories.

# paths./collections/climate:indices:historical.get.summary
Get Statistically Downscaled Climate Indices - Historical metadata

# paths./collections/climate:indices:historical/coverage.get.description
High-resolution statistically downscaled climate indices based on model projections from 24 global climate models (GCMs) that participated in the Coupled Model Intercomparison Project Phase 5 (CMIP5) are available. The climate indices are grouped into temperature-related, precipitation-related and agroclimatic categories.

# paths./collections/climate:indices:historical/coverage.get.summary
Get Statistically Downscaled Climate Indices - Historical coverage

# paths./collections/climate:indices:projected.get.description
High-resolution statistically downscaled climate indices based on model projections from 24 global climate models (GCMs) that participated in the Coupled Model Intercomparison Project Phase 5 (CMIP5) are available. The climate indices are grouped into temperature-related, precipitation-related and agroclimatic categories.

# paths./collections/climate:indices:projected.get.summary
Get Statistically Downscaled Climate Indices - Projected metadata

# paths./collections/climate:indices:projected/coverage.get.description
High-resolution statistically downscaled climate indices based on model projections from 24 global climate models (GCMs) that participated in the Coupled Model Intercomparison Project Phase 5 (CMIP5) are available. The climate indices are grouped into temperature-related, precipitation-related and agroclimatic categories.

# paths./collections/climate:indices:projected/coverage.get.summary
Get Statistically Downscaled Climate Indices - Projected coverage

# paths./collections/climate:spei-12:historical.get.description
Standardized Precipitation Evapotranspiration Index (SPEI) calculated using global climate model projections from the Coupled Model Intercomparison Project Phase 5 (CMIP5) are provided. SPEI is a multiscalar index frequently used to quantify drought and is based on a climate water balance.

# paths./collections/climate:spei-12:historical/coverage.get.description
Standardized Precipitation Evapotranspiration Index (SPEI) calculated using global climate model projections from the Coupled Model Intercomparison Project Phase 5 (CMIP5) are provided. SPEI is a multiscalar index frequently used to quantify drought and is based on a climate water balance.

# paths./collections/climate:spei-12:projected.get.description
Standardized Precipitation Evapotranspiration Index (SPEI) calculated using global climate model projections from the Coupled Model Intercomparison Project Phase 5 (CMIP5) are provided. SPEI is a multiscalar index frequently used to quantify drought and is based on a climate water balance.

# paths./collections/climate:spei-12:projected/coverage.get.description
Standardized Precipitation Evapotranspiration Index (SPEI) calculated using global climate model projections from the Coupled Model Intercomparison Project Phase 5 (CMIP5) are provided. SPEI is a multiscalar index frequently used to quantify drought and is based on a climate water balance.

# paths./collections/climate:spei-1:historical.get.description
Standardized Precipitation Evapotranspiration Index (SPEI) calculated using global climate model projections from the Coupled Model Intercomparison Project Phase 5 (CMIP5) are provided. SPEI is a multiscalar index frequently used to quantify drought and is based on a climate water balance.

# paths./collections/climate:spei-1:historical/coverage.get.description
Standardized Precipitation Evapotranspiration Index (SPEI) calculated using global climate model projections from the Coupled Model Intercomparison Project Phase 5 (CMIP5) are provided. SPEI is a multiscalar index frequently used to quantify drought and is based on a climate water balance.

# paths./collections/climate:spei-1:projected.get.description
Standardized Precipitation Evapotranspiration Index (SPEI) calculated using global climate model projections from the Coupled Model Intercomparison Project Phase 5 (CMIP5) are provided. SPEI is a multiscalar index frequently used to quantify drought and is based on a climate water balance.

# paths./collections/climate:spei-1:projected/coverage.get.description
Standardized Precipitation Evapotranspiration Index (SPEI) calculated using global climate model projections from the Coupled Model Intercomparison Project Phase 5 (CMIP5) are provided. SPEI is a multiscalar index frequently used to quantify drought and is based on a climate water balance.

# paths./collections/climate:spei-3:historical.get.description
Standardized Precipitation Evapotranspiration Index (SPEI) calculated using global climate model projections from the Coupled Model Intercomparison Project Phase 5 (CMIP5) are provided. SPEI is a multiscalar index frequently used to quantify drought and is based on a climate water balance.

# paths./collections/climate:spei-3:historical/coverage.get.description
Standardized Precipitation Evapotranspiration Index (SPEI) calculated using global climate model projections from the Coupled Model Intercomparison Project Phase 5 (CMIP5) are provided. SPEI is a multiscalar index frequently used to quantify drought and is based on a climate water balance.

# paths./collections/climate:spei-3:projected.get.description
Standardized Precipitation Evapotranspiration Index (SPEI) calculated using global climate model projections from the Coupled Model Intercomparison Project Phase 5 (CMIP5) are provided. SPEI is a multiscalar index frequently used to quantify drought and is based on a climate water balance.

# paths./collections/climate:spei-3:projected/coverage.get.description
Standardized Precipitation Evapotranspiration Index (SPEI) calculated using global climate model projections from the Coupled Model Intercomparison Project Phase 5 (CMIP5) are provided. SPEI is a multiscalar index frequently used to quantify drought and is based on a climate water balance.

# paths./collections/coastal_flood_risk_index.get.description
The Coastal Flooding Risk Index is a geo- and time-referenced polygon product issued by the Meteorological Service of Canada (MSC) to articulate the coastal flooding risk, impact and probability. Products are issued daily by Storm Prediction Centres and are intended to provide early notification, out to 5 days, of coastal flooding due to astronomical tide, storm surge and wave impacts.

# paths./collections/coastal_flood_risk_index.get.summary
Get Coastal Flooding Risk Index [experimental] metadata

# paths./collections/coastal_flood_risk_index/items.get.description
The Coastal Flooding Risk Index is a geo- and time-referenced polygon product issued by the Meteorological Service of Canada (MSC) to articulate the coastal flooding risk, impact and probability. Products are issued daily by Storm Prediction Centres and are intended to provide early notification, out to 5 days, of coastal flooding due to astronomical tide, storm surge and wave impacts.

# paths./collections/coastal_flood_risk_index/items.get.parameters[].description
The properties that should be included for each feature. The parameter value is a comma-separated list of property names.

# paths./collections/coastal_flood_risk_index/items.get.summary
Get Coastal Flooding Risk Index [experimental] items

# paths./collections/coastal_flood_risk_index/items.options.description
The Coastal Flooding Risk Index is a geo- and time-referenced polygon product issued by the Meteorological Service of Canada (MSC) to articulate the coastal flooding risk, impact and probability. Products are issued daily by Storm Prediction Centres and are intended to provide early notification, out to 5 days, of coastal flooding due to astronomical tide, storm surge and wave impacts.

# paths./collections/coastal_flood_risk_index/items.options.summary
Options for Coastal Flooding Risk Index [experimental] items

# paths./collections/coastal_flood_risk_index/items/{featureId}.get.description
The Coastal Flooding Risk Index is a geo- and time-referenced polygon product issued by the Meteorological Service of Canada (MSC) to articulate the coastal flooding risk, impact and probability. Products are issued daily by Storm Prediction Centres and are intended to provide early notification, out to 5 days, of coastal flooding due to astronomical tide, storm surge and wave impacts.

# paths./collections/coastal_flood_risk_index/items/{featureId}.get.summary
Get Coastal Flooding Risk Index [experimental] item by id

# paths./collections/coastal_flood_risk_index/items/{featureId}.options.description
The Coastal Flooding Risk Index is a geo- and time-referenced polygon product issued by the Meteorological Service of Canada (MSC) to articulate the coastal flooding risk, impact and probability. Products are issued daily by Storm Prediction Centres and are intended to provide early notification, out to 5 days, of coastal flooding due to astronomical tide, storm surge and wave impacts.

# paths./collections/coastal_flood_risk_index/items/{featureId}.options.summary
Options for Coastal Flooding Risk Index [experimental] item by id

# paths./collections/coastal_flood_risk_index/queryables.get.description
The Coastal Flooding Risk Index is a geo- and time-referenced polygon product issued by the Meteorological Service of Canada (MSC) to articulate the coastal flooding risk, impact and probability. Products are issued daily by Storm Prediction Centres and are intended to provide early notification, out to 5 days, of coastal flooding due to astronomical tide, storm surge and wave impacts.

# paths./collections/coastal_flood_risk_index/queryables.get.summary
Get Coastal Flooding Risk Index [experimental] queryables

# paths./collections/coastal_flood_risk_index/schema.get.description
The Coastal Flooding Risk Index is a geo- and time-referenced polygon product issued by the Meteorological Service of Canada (MSC) to articulate the coastal flooding risk, impact and probability. Products are issued daily by Storm Prediction Centres and are intended to provide early notification, out to 5 days, of coastal flooding due to astronomical tide, storm surge and wave impacts.

# paths./collections/coastal_flood_risk_index/schema.get.summary
Get Coastal Flooding Risk Index [experimental] schema

# paths./collections/datasets-footprints/items.get.parameters[].description
The properties that should be included for each feature. The parameter value is a comma-separated list of property names.

# paths./collections/datasets-footprints/items.options.summary
Options for MSC Datasets Footprints items

# paths./collections/datasets-footprints/items/{featureId}.options.summary
Options for MSC Datasets Footprints item by id

# paths./collections/hurricanes-cyclone-realtime.get.description
Hurricane tracks and response zone are issued by the Canadian Hurricane Centre as part of the regular hurricane season forecast.

# paths./collections/hurricanes-cyclone-realtime.get.summary
Get Hurricane Tracks - Cyclone [experimental] metadata

# paths./collections/hurricanes-cyclone-realtime/items.get.description
Hurricane tracks and response zone are issued by the Canadian Hurricane Centre as part of the regular hurricane season forecast.

# paths./collections/hurricanes-cyclone-realtime/items.get.parameters[].description
The properties that should be included for each feature. The parameter value is a comma-separated list of property names.

# paths./collections/hurricanes-cyclone-realtime/items.get.summary
Get Hurricane Tracks - Cyclone [experimental] items

# paths./collections/hurricanes-cyclone-realtime/items.options.description
Hurricane tracks and response zone are issued by the Canadian Hurricane Centre as part of the regular hurricane season forecast.

# paths./collections/hurricanes-cyclone-realtime/items.options.summary
Options for Hurricane Tracks - Cyclone [experimental] items

# paths./collections/hurricanes-cyclone-realtime/items/{featureId}.get.description
Hurricane tracks and response zone are issued by the Canadian Hurricane Centre as part of the regular hurricane season forecast.

# paths./collections/hurricanes-cyclone-realtime/items/{featureId}.get.summary
Get Hurricane Tracks - Cyclone [experimental] item by id

# paths./collections/hurricanes-cyclone-realtime/items/{featureId}.options.description
Hurricane tracks and response zone are issued by the Canadian Hurricane Centre as part of the regular hurricane season forecast.

# paths./collections/hurricanes-cyclone-realtime/items/{featureId}.options.summary
Options for Hurricane Tracks - Cyclone [experimental] item by id

# paths./collections/hurricanes-cyclone-realtime/queryables.get.description
Hurricane tracks and response zone are issued by the Canadian Hurricane Centre as part of the regular hurricane season forecast.

# paths./collections/hurricanes-cyclone-realtime/queryables.get.summary
Get Hurricane Tracks - Cyclone [experimental] queryables

# paths./collections/hurricanes-cyclone-realtime/schema.get.description
Hurricane tracks and response zone are issued by the Canadian Hurricane Centre as part of the regular hurricane season forecast.

# paths./collections/hurricanes-cyclone-realtime/schema.get.summary
Get Hurricane Tracks - Cyclone [experimental] schema

# paths./collections/hurricanes-error_cone-realtime.get.description
Hurricane tracks and response zone are issued by the Canadian Hurricane Centre as part of the regular hurricane season forecast.

# paths./collections/hurricanes-error_cone-realtime.get.summary
Get Hurricane Tracks - Error Cone [experimental] metadata

# paths./collections/hurricanes-error_cone-realtime/items.get.description
Hurricane tracks and response zone are issued by the Canadian Hurricane Centre as part of the regular hurricane season forecast.

# paths./collections/hurricanes-error_cone-realtime/items.get.parameters[].description
The properties that should be included for each feature. The parameter value is a comma-separated list of property names.

# paths./collections/hurricanes-error_cone-realtime/items.get.summary
Get Hurricane Tracks - Error Cone [experimental] items

# paths./collections/hurricanes-error_cone-realtime/items.options.description
Hurricane tracks and response zone are issued by the Canadian Hurricane Centre as part of the regular hurricane season forecast.

# paths./collections/hurricanes-error_cone-realtime/items.options.summary
Options for Hurricane Tracks - Error Cone [experimental] items

# paths./collections/hurricanes-error_cone-realtime/items/{featureId}.get.description
Hurricane tracks and response zone are issued by the Canadian Hurricane Centre as part of the regular hurricane season forecast.

# paths./collections/hurricanes-error_cone-realtime/items/{featureId}.get.summary
Get Hurricane Tracks - Error Cone [experimental] item by id

# paths./collections/hurricanes-error_cone-realtime/items/{featureId}.options.description
Hurricane tracks and response zone are issued by the Canadian Hurricane Centre as part of the regular hurricane season forecast.

# paths./collections/hurricanes-error_cone-realtime/items/{featureId}.options.summary
Options for Hurricane Tracks - Error Cone [experimental] item by id

# paths./collections/hurricanes-error_cone-realtime/queryables.get.description
Hurricane tracks and response zone are issued by the Canadian Hurricane Centre as part of the regular hurricane season forecast.

# paths./collections/hurricanes-error_cone-realtime/queryables.get.summary
Get Hurricane Tracks - Error Cone [experimental] queryables

# paths./collections/hurricanes-error_cone-realtime/schema.get.description
Hurricane tracks and response zone are issued by the Canadian Hurricane Centre as part of the regular hurricane season forecast.

# paths./collections/hurricanes-error_cone-realtime/schema.get.summary
Get Hurricane Tracks - Error Cone [experimental] schema

# paths./collections/hurricanes-track-realtime.get.description
Hurricane tracks and response zone are issued by the Canadian Hurricane Centre as part of the regular hurricane season forecast.

# paths./collections/hurricanes-track-realtime.get.summary
Get Hurricane Tracks - Track [experimental] metadata

# paths./collections/hurricanes-track-realtime/items.get.description
Hurricane tracks and response zone are issued by the Canadian Hurricane Centre as part of the regular hurricane season forecast.

# paths./collections/hurricanes-track-realtime/items.get.parameters[].description
The properties that should be included for each feature. The parameter value is a comma-separated list of property names.

# paths./collections/hurricanes-track-realtime/items.get.summary
Get Hurricane Tracks - Track [experimental] items

# paths./collections/hurricanes-track-realtime/items.options.description
Hurricane tracks and response zone are issued by the Canadian Hurricane Centre as part of the regular hurricane season forecast.

# paths./collections/hurricanes-track-realtime/items.options.summary
Options for Hurricane Tracks - Track [experimental] items

# paths./collections/hurricanes-track-realtime/items/{featureId}.get.description
Hurricane tracks and response zone are issued by the Canadian Hurricane Centre as part of the regular hurricane season forecast.

# paths./collections/hurricanes-track-realtime/items/{featureId}.get.summary
Get Hurricane Tracks - Track [experimental] item by id

# paths./collections/hurricanes-track-realtime/items/{featureId}.options.description
Hurricane tracks and response zone are issued by the Canadian Hurricane Centre as part of the regular hurricane season forecast.

# paths./collections/hurricanes-track-realtime/items/{featureId}.options.summary
Options for Hurricane Tracks - Track [experimental] item by id

# paths./collections/hurricanes-track-realtime/queryables.get.description
Hurricane tracks and response zone are issued by the Canadian Hurricane Centre as part of the regular hurricane season forecast.

# paths./collections/hurricanes-track-realtime/queryables.get.summary
Get Hurricane Tracks - Track [experimental] queryables

# paths./collections/hurricanes-track-realtime/schema.get.description
Hurricane tracks and response zone are issued by the Canadian Hurricane Centre as part of the regular hurricane season forecast.

# paths./collections/hurricanes-track-realtime/schema.get.summary
Get Hurricane Tracks - Track [experimental] schema

# paths./collections/hurricanes-wind_radii-realtime.get.description
Hurricane tracks and response zone are issued by the Canadian Hurricane Centre as part of the regular hurricane season forecast.

# paths./collections/hurricanes-wind_radii-realtime.get.summary
Get Hurricane Tracks - Wind Radii [experimental] metadata

# paths./collections/hurricanes-wind_radii-realtime/items.get.description
Hurricane tracks and response zone are issued by the Canadian Hurricane Centre as part of the regular hurricane season forecast.

# paths./collections/hurricanes-wind_radii-realtime/items.get.parameters[].description
The properties that should be included for each feature. The parameter value is a comma-separated list of property names.

# paths./collections/hurricanes-wind_radii-realtime/items.get.summary
Get Hurricane Tracks - Wind Radii [experimental] items

# paths./collections/hurricanes-wind_radii-realtime/items.options.description
Hurricane tracks and response zone are issued by the Canadian Hurricane Centre as part of the regular hurricane season forecast.

# paths./collections/hurricanes-wind_radii-realtime/items.options.summary
Options for Hurricane Tracks - Wind Radii [experimental] items

# paths./collections/hurricanes-wind_radii-realtime/items/{featureId}.get.description
Hurricane tracks and response zone are issued by the Canadian Hurricane Centre as part of the regular hurricane season forecast.

# paths./collections/hurricanes-wind_radii-realtime/items/{featureId}.get.summary
Get Hurricane Tracks - Wind Radii [experimental] item by id

# paths./collections/hurricanes-wind_radii-realtime/items/{featureId}.options.description
Hurricane tracks and response zone are issued by the Canadian Hurricane Centre as part of the regular hurricane season forecast.

# paths./collections/hurricanes-wind_radii-realtime/items/{featureId}.options.summary
Options for Hurricane Tracks - Wind Radii [experimental] item by id

# paths./collections/hurricanes-wind_radii-realtime/queryables.get.description
Hurricane tracks and response zone are issued by the Canadian Hurricane Centre as part of the regular hurricane season forecast.

# paths./collections/hurricanes-wind_radii-realtime/queryables.get.summary
Get Hurricane Tracks - Wind Radii [experimental] queryables

# paths./collections/hurricanes-wind_radii-realtime/schema.get.description
Hurricane tracks and response zone are issued by the Canadian Hurricane Centre as part of the regular hurricane season forecast.

# paths./collections/hurricanes-wind_radii-realtime/schema.get.summary
Get Hurricane Tracks - Wind Radii [experimental] schema

# paths./collections/hydrometric-annual-peaks.get.description
The annual maximum and minimum instantaneous data are the maximum and minimum instantaneous values for a given year.

# paths./collections/hydrometric-annual-peaks.get.summary
Get Hydrometric Historical Data (HYDAT) - Annual Maximum and Minimum Instantaneous Water Level or Flow metadata

# paths./collections/hydrometric-annual-peaks/items.get.description
The annual maximum and minimum instantaneous data are the maximum and minimum instantaneous values for a given year.

# paths./collections/hydrometric-annual-peaks/items.get.parameters[].description
The properties that should be included for each feature. The parameter value is a comma-separated list of property names.

# paths./collections/hydrometric-annual-peaks/items.get.summary
Get Hydrometric Historical Data (HYDAT) - Annual Maximum and Minimum Instantaneous Water Level or Flow items

# paths./collections/hydrometric-annual-peaks/items.options.description
The annual maximum and minimum instantaneous data are the maximum and minimum instantaneous values for a given year.

# paths./collections/hydrometric-annual-peaks/items.options.summary
Options for Hydrometric Historical Data (HYDAT) - Annual Maximum and Minimum Instantaneous Water Level or Flow items

# paths./collections/hydrometric-annual-peaks/items/{featureId}.get.description
The annual maximum and minimum instantaneous data are the maximum and minimum instantaneous values for a given year.

# paths./collections/hydrometric-annual-peaks/items/{featureId}.get.summary
Get Hydrometric Historical Data (HYDAT) - Annual Maximum and Minimum Instantaneous Water Level or Flow item by id

# paths./collections/hydrometric-annual-peaks/items/{featureId}.options.description
The annual maximum and minimum instantaneous data are the maximum and minimum instantaneous values for a given year.

# paths./collections/hydrometric-annual-peaks/items/{featureId}.options.summary
Options for Hydrometric Historical Data (HYDAT) - Annual Maximum and Minimum Instantaneous Water Level or Flow item by id

# paths./collections/hydrometric-annual-peaks/queryables.get.description
The annual maximum and minimum instantaneous data are the maximum and minimum instantaneous values for a given year.

# paths./collections/hydrometric-annual-peaks/queryables.get.summary
Get Hydrometric Historical Data (HYDAT) - Annual Maximum and Minimum Instantaneous Water Level or Flow queryables

# paths./collections/hydrometric-annual-peaks/schema.get.description
The annual maximum and minimum instantaneous data are the maximum and minimum instantaneous values for a given year.

# paths./collections/hydrometric-annual-peaks/schema.get.summary
Get Hydrometric Historical Data (HYDAT) - Annual Maximum and Minimum Instantaneous Water Level or Flow schema

# paths./collections/hydrometric-annual-statistics.get.description
The annual maximum and minimum daily data are the maximum and minimum daily mean values for a given year.

# paths./collections/hydrometric-annual-statistics.get.summary
Get Hydrometric Historical Data (HYDAT) - Annual Maximum and Minimum Daily Water Level or Flow metadata

# paths./collections/hydrometric-annual-statistics/items.get.description
The annual maximum and minimum daily data are the maximum and minimum daily mean values for a given year.

# paths./collections/hydrometric-annual-statistics/items.get.parameters[].description
The properties that should be included for each feature. The parameter value is a comma-separated list of property names.

# paths./collections/hydrometric-annual-statistics/items.get.summary
Get Hydrometric Historical Data (HYDAT) - Annual Maximum and Minimum Daily Water Level or Flow items

# paths./collections/hydrometric-annual-statistics/items.options.description
The annual maximum and minimum daily data are the maximum and minimum daily mean values for a given year.

# paths./collections/hydrometric-annual-statistics/items.options.summary
Options for Hydrometric Historical Data (HYDAT) - Annual Maximum and Minimum Daily Water Level or Flow items

# paths./collections/hydrometric-annual-statistics/items/{featureId}.get.description
The annual maximum and minimum daily data are the maximum and minimum daily mean values for a given year.

# paths./collections/hydrometric-annual-statistics/items/{featureId}.get.summary
Get Hydrometric Historical Data (HYDAT) - Annual Maximum and Minimum Daily Water Level or Flow item by id

# paths./collections/hydrometric-annual-statistics/items/{featureId}.options.description
The annual maximum and minimum daily data are the maximum and minimum daily mean values for a given year.

# paths./collections/hydrometric-annual-statistics/items/{featureId}.options.summary
Options for Hydrometric Historical Data (HYDAT) - Annual Maximum and Minimum Daily Water Level or Flow item by id

# paths./collections/hydrometric-annual-statistics/queryables.get.description
The annual maximum and minimum daily data are the maximum and minimum daily mean values for a given year.

# paths./collections/hydrometric-annual-statistics/queryables.get.summary
Get Hydrometric Historical Data (HYDAT) - Annual Maximum and Minimum Daily Water Level or Flow queryables

# paths./collections/hydrometric-annual-statistics/schema.get.description
The annual maximum and minimum daily data are the maximum and minimum daily mean values for a given year.

# paths./collections/hydrometric-annual-statistics/schema.get.summary
Get Hydrometric Historical Data (HYDAT) - Annual Maximum and Minimum Daily Water Level or Flow schema

# paths./collections/hydrometric-daily-mean.get.description
The daily mean is the average of all unit values for a given day.

# paths./collections/hydrometric-daily-mean.get.summary
Get Hydrometric Historical Data (HYDAT) - Daily Mean of Water Level or Flow metadata

# paths./collections/hydrometric-daily-mean/items.get.description
The daily mean is the average of all unit values for a given day.

# paths./collections/hydrometric-daily-mean/items.get.parameters[].description
The properties that should be included for each feature. The parameter value is a comma-separated list of property names.

# paths./collections/hydrometric-daily-mean/items.get.summary
Get Hydrometric Historical Data (HYDAT) - Daily Mean of Water Level or Flow items

# paths./collections/hydrometric-daily-mean/items.options.description
The daily mean is the average of all unit values for a given day.

# paths./collections/hydrometric-daily-mean/items.options.summary
Options for Hydrometric Historical Data (HYDAT) - Daily Mean of Water Level or Flow items

# paths./collections/hydrometric-daily-mean/items/{featureId}.get.description
The daily mean is the average of all unit values for a given day.

# paths./collections/hydrometric-daily-mean/items/{featureId}.get.summary
Get Hydrometric Historical Data (HYDAT) - Daily Mean of Water Level or Flow item by id

# paths./collections/hydrometric-daily-mean/items/{featureId}.options.description
The daily mean is the average of all unit values for a given day.

# paths./collections/hydrometric-daily-mean/items/{featureId}.options.summary
Options for Hydrometric Historical Data (HYDAT) - Daily Mean of Water Level or Flow item by id

# paths./collections/hydrometric-daily-mean/queryables.get.description
The daily mean is the average of all unit values for a given day.

# paths./collections/hydrometric-daily-mean/queryables.get.summary
Get Hydrometric Historical Data (HYDAT) - Daily Mean of Water Level or Flow queryables

# paths./collections/hydrometric-daily-mean/schema.get.description
The daily mean is the average of all unit values for a given day.

# paths./collections/hydrometric-daily-mean/schema.get.summary
Get Hydrometric Historical Data (HYDAT) - Daily Mean of Water Level or Flow schema

# paths./collections/hydrometric-monthly-mean.get.description
The monthly mean is the average of daily mean values for a given month.

# paths./collections/hydrometric-monthly-mean.get.summary
Get Hydrometric Historical Data (HYDAT) - Monthly Mean of Water Level or Flow metadata

# paths./collections/hydrometric-monthly-mean/items.get.description
The monthly mean is the average of daily mean values for a given month.

# paths./collections/hydrometric-monthly-mean/items.get.parameters[].description
The properties that should be included for each feature. The parameter value is a comma-separated list of property names.

# paths./collections/hydrometric-monthly-mean/items.get.summary
Get Hydrometric Historical Data (HYDAT) - Monthly Mean of Water Level or Flow items

# paths./collections/hydrometric-monthly-mean/items.options.description
The monthly mean is the average of daily mean values for a given month.

# paths./collections/hydrometric-monthly-mean/items.options.summary
Options for Hydrometric Historical Data (HYDAT) - Monthly Mean of Water Level or Flow items

# paths./collections/hydrometric-monthly-mean/items/{featureId}.get.description
The monthly mean is the average of daily mean values for a given month.

# paths./collections/hydrometric-monthly-mean/items/{featureId}.get.summary
Get Hydrometric Historical Data (HYDAT) - Monthly Mean of Water Level or Flow item by id

# paths./collections/hydrometric-monthly-mean/items/{featureId}.options.description
The monthly mean is the average of daily mean values for a given month.

# paths./collections/hydrometric-monthly-mean/items/{featureId}.options.summary
Options for Hydrometric Historical Data (HYDAT) - Monthly Mean of Water Level or Flow item by id

# paths./collections/hydrometric-monthly-mean/queryables.get.description
The monthly mean is the average of daily mean values for a given month.

# paths./collections/hydrometric-monthly-mean/queryables.get.summary
Get Hydrometric Historical Data (HYDAT) - Monthly Mean of Water Level or Flow queryables

# paths./collections/hydrometric-monthly-mean/schema.get.description
The monthly mean is the average of daily mean values for a given month.

# paths./collections/hydrometric-monthly-mean/schema.get.summary
Get Hydrometric Historical Data (HYDAT) - Monthly Mean of Water Level or Flow schema

# paths./collections/hydrometric-realtime.get.description
Real-time water level and flow (discharge) data collected at over 2100 hydrometric stations across Canada (last 30 days).

# paths./collections/hydrometric-realtime.get.summary
Get Hydrometric Data - Real-time metadata

# paths./collections/hydrometric-realtime/items.get.description
Real-time water level and flow (discharge) data collected at over 2100 hydrometric stations across Canada (last 30 days).

# paths./collections/hydrometric-realtime/items.get.parameters[].description
The properties that should be included for each feature. The parameter value is a comma-separated list of property names.

# paths./collections/hydrometric-realtime/items.options.description
Real-time water level and flow (discharge) data collected at over 2100 hydrometric stations across Canada (last 30 days).

# paths./collections/hydrometric-realtime/items.options.summary
Options for Hydrometric Data - Real-time items

# paths./collections/hydrometric-realtime/items/{featureId}.get.description
Real-time water level and flow (discharge) data collected at over 2100 hydrometric stations across Canada (last 30 days).

# paths./collections/hydrometric-realtime/items/{featureId}.get.summary
Get Hydrometric Data - Real-time item by id

# paths./collections/hydrometric-realtime/items/{featureId}.options.description
Real-time water level and flow (discharge) data collected at over 2100 hydrometric stations across Canada (last 30 days).

# paths./collections/hydrometric-realtime/items/{featureId}.options.summary
Options for Hydrometric Data - Real-time item by id

# paths./collections/hydrometric-realtime/queryables.get.description
Real-time water level and flow (discharge) data collected at over 2100 hydrometric stations across Canada (last 30 days).

# paths./collections/hydrometric-realtime/queryables.get.summary
Get Hydrometric Data - Real-time queryables

# paths./collections/hydrometric-realtime/schema.get.description
Real-time water level and flow (discharge) data collected at over 2100 hydrometric stations across Canada (last 30 days).

# paths./collections/hydrometric-stations.get.description
A station is a site on a river or lake where water quantity (water level and flow) are collected and recorded.

# paths./collections/hydrometric-stations.get.summary
Get Hydrometric Data - Monitoring Stations metadata

# paths./collections/hydrometric-stations/items.get.description
A station is a site on a river or lake where water quantity (water level and flow) are collected and recorded.

# paths./collections/hydrometric-stations/items.get.parameters[].description
The properties that should be included for each feature. The parameter value is a comma-separated list of property names.

# paths./collections/hydrometric-stations/items.get.summary
Get Hydrometric Data - Monitoring Stations items

# paths./collections/hydrometric-stations/items.options.description
A station is a site on a river or lake where water quantity (water level and flow) are collected and recorded.

# paths./collections/hydrometric-stations/items.options.summary
Options for Hydrometric Data - Monitoring Stations items

# paths./collections/hydrometric-stations/items/{featureId}.get.description
A station is a site on a river or lake where water quantity (water level and flow) are collected and recorded.

# paths./collections/hydrometric-stations/items/{featureId}.get.summary
Get Hydrometric Data - Monitoring Stations item by id

# paths./collections/hydrometric-stations/items/{featureId}.options.description
A station is a site on a river or lake where water quantity (water level and flow) are collected and recorded.

# paths./collections/hydrometric-stations/items/{featureId}.options.summary
Options for Hydrometric Data - Monitoring Stations item by id

# paths./collections/hydrometric-stations/queryables.get.description
A station is a site on a river or lake where water quantity (water level and flow) are collected and recorded.

# paths./collections/hydrometric-stations/queryables.get.summary
Get Hydrometric Data - Monitoring Stations queryables

# paths./collections/hydrometric-stations/schema.get.description
A station is a site on a river or lake where water quantity (water level and flow) are collected and recorded.

# paths./collections/hydrometric-stations/schema.get.summary
Get Hydrometric Data - Monitoring Stations schema

# paths./collections/ltce-precipitation.get.description
The daily climate records database, also known as Long Term Climate Extremes (LTCE), was developed to address the fragmentation of climate information due to station changes (opening, closing, relocation, etc.) over time. For approximately 750 locations in Canada, "virtual" climate stations have been developed by joining (threading) climate data for an urban location, from nearby stations to make long-term records. Each long-term record consists of the extremes (record values) of daily maximum/minimum temperatures, total precipitation and snowfall for each day of the year. Many of the longest data sets of extremes date as far back as the 1800s. This data provides the daily extremes of record for Precipitation for each day of the year. Daily elements include: Greatest Precipitation.

# paths./collections/ltce-precipitation/items.get.description
The daily climate records database, also known as Long Term Climate Extremes (LTCE), was developed to address the fragmentation of climate information due to station changes (opening, closing, relocation, etc.) over time. For approximately 750 locations in Canada, "virtual" climate stations have been developed by joining (threading) climate data for an urban location, from nearby stations to make long-term records. Each long-term record consists of the extremes (record values) of daily maximum/minimum temperatures, total precipitation and snowfall for each day of the year. Many of the longest data sets of extremes date as far back as the 1800s. This data provides the daily extremes of record for Precipitation for each day of the year. Daily elements include: Greatest Precipitation.

# paths./collections/ltce-precipitation/items.get.parameters[].description
The properties that should be included for each feature. The parameter value is a comma-separated list of property names.

# paths./collections/ltce-precipitation/items.options.description
The daily climate records database, also known as Long Term Climate Extremes (LTCE), was developed to address the fragmentation of climate information due to station changes (opening, closing, relocation, etc.) over time. For approximately 750 locations in Canada, "virtual" climate stations have been developed by joining (threading) climate data for an urban location, from nearby stations to make long-term records. Each long-term record consists of the extremes (record values) of daily maximum/minimum temperatures, total precipitation and snowfall for each day of the year. Many of the longest data sets of extremes date as far back as the 1800s. This data provides the daily extremes of record for Precipitation for each day of the year. Daily elements include: Greatest Precipitation.

# paths./collections/ltce-precipitation/items/{featureId}.get.description
The daily climate records database, also known as Long Term Climate Extremes (LTCE), was developed to address the fragmentation of climate information due to station changes (opening, closing, relocation, etc.) over time. For approximately 750 locations in Canada, "virtual" climate stations have been developed by joining (threading) climate data for an urban location, from nearby stations to make long-term records. Each long-term record consists of the extremes (record values) of daily maximum/minimum temperatures, total precipitation and snowfall for each day of the year. Many of the longest data sets of extremes date as far back as the 1800s. This data provides the daily extremes of record for Precipitation for each day of the year. Daily elements include: Greatest Precipitation.

# paths./collections/ltce-precipitation/items/{featureId}.options.description
The daily climate records database, also known as Long Term Climate Extremes (LTCE), was developed to address the fragmentation of climate information due to station changes (opening, closing, relocation, etc.) over time. For approximately 750 locations in Canada, "virtual" climate stations have been developed by joining (threading) climate data for an urban location, from nearby stations to make long-term records. Each long-term record consists of the extremes (record values) of daily maximum/minimum temperatures, total precipitation and snowfall for each day of the year. Many of the longest data sets of extremes date as far back as the 1800s. This data provides the daily extremes of record for Precipitation for each day of the year. Daily elements include: Greatest Precipitation.

# paths./collections/ltce-precipitation/items/{featureId}.options.summary
Options for LTCE – Precipitation item by id

# paths./collections/ltce-precipitation/queryables.get.description
The daily climate records database, also known as Long Term Climate Extremes (LTCE), was developed to address the fragmentation of climate information due to station changes (opening, closing, relocation, etc.) over time. For approximately 750 locations in Canada, "virtual" climate stations have been developed by joining (threading) climate data for an urban location, from nearby stations to make long-term records. Each long-term record consists of the extremes (record values) of daily maximum/minimum temperatures, total precipitation and snowfall for each day of the year. Many of the longest data sets of extremes date as far back as the 1800s. This data provides the daily extremes of record for Precipitation for each day of the year. Daily elements include: Greatest Precipitation.

# paths./collections/ltce-precipitation/schema.get.description
The daily climate records database, also known as Long Term Climate Extremes (LTCE), was developed to address the fragmentation of climate information due to station changes (opening, closing, relocation, etc.) over time. For approximately 750 locations in Canada, "virtual" climate stations have been developed by joining (threading) climate data for an urban location, from nearby stations to make long-term records. Each long-term record consists of the extremes (record values) of daily maximum/minimum temperatures, total precipitation and snowfall for each day of the year. Many of the longest data sets of extremes date as far back as the 1800s. This data provides the daily extremes of record for Precipitation for each day of the year. Daily elements include: Greatest Precipitation.

# paths./collections/ltce-snowfall.get.description
The daily climate records database, also known as Long Term Climate Extremes (LTCE), was developed to address the fragmentation of climate information due to station changes (opening, closing, relocation, etc.) over time. For approximately 750 locations in Canada, "virtual" climate stations have been developed by joining (threading) climate data for an urban location, from nearby stations to make long-term records. Each long-term record consists of the extremes (record values) of daily maximum/minimum temperatures, total precipitation and snowfall for each day of the year. Many of the longest data sets of extremes date as far back as the 1800s. This data provides the daily extremes of record for Snowfall for each day of the year. Daily elements include: Greatest Snowfall.

# paths./collections/ltce-snowfall/items.get.description
The daily climate records database, also known as Long Term Climate Extremes (LTCE), was developed to address the fragmentation of climate information due to station changes (opening, closing, relocation, etc.) over time. For approximately 750 locations in Canada, "virtual" climate stations have been developed by joining (threading) climate data for an urban location, from nearby stations to make long-term records. Each long-term record consists of the extremes (record values) of daily maximum/minimum temperatures, total precipitation and snowfall for each day of the year. Many of the longest data sets of extremes date as far back as the 1800s. This data provides the daily extremes of record for Snowfall for each day of the year. Daily elements include: Greatest Snowfall.

# paths./collections/ltce-snowfall/items.get.parameters[].description
The properties that should be included for each feature. The parameter value is a comma-separated list of property names.

# paths./collections/ltce-snowfall/items.options.description
The daily climate records database, also known as Long Term Climate Extremes (LTCE), was developed to address the fragmentation of climate information due to station changes (opening, closing, relocation, etc.) over time. For approximately 750 locations in Canada, "virtual" climate stations have been developed by joining (threading) climate data for an urban location, from nearby stations to make long-term records. Each long-term record consists of the extremes (record values) of daily maximum/minimum temperatures, total precipitation and snowfall for each day of the year. Many of the longest data sets of extremes date as far back as the 1800s. This data provides the daily extremes of record for Snowfall for each day of the year. Daily elements include: Greatest Snowfall.

# paths./collections/ltce-snowfall/items/{featureId}.get.description
The daily climate records database, also known as Long Term Climate Extremes (LTCE), was developed to address the fragmentation of climate information due to station changes (opening, closing, relocation, etc.) over time. For approximately 750 locations in Canada, "virtual" climate stations have been developed by joining (threading) climate data for an urban location, from nearby stations to make long-term records. Each long-term record consists of the extremes (record values) of daily maximum/minimum temperatures, total precipitation and snowfall for each day of the year. Many of the longest data sets of extremes date as far back as the 1800s. This data provides the daily extremes of record for Snowfall for each day of the year. Daily elements include: Greatest Snowfall.

# paths./collections/ltce-snowfall/items/{featureId}.options.description
The daily climate records database, also known as Long Term Climate Extremes (LTCE), was developed to address the fragmentation of climate information due to station changes (opening, closing, relocation, etc.) over time. For approximately 750 locations in Canada, "virtual" climate stations have been developed by joining (threading) climate data for an urban location, from nearby stations to make long-term records. Each long-term record consists of the extremes (record values) of daily maximum/minimum temperatures, total precipitation and snowfall for each day of the year. Many of the longest data sets of extremes date as far back as the 1800s. This data provides the daily extremes of record for Snowfall for each day of the year. Daily elements include: Greatest Snowfall.

# paths./collections/ltce-snowfall/queryables.get.description
The daily climate records database, also known as Long Term Climate Extremes (LTCE), was developed to address the fragmentation of climate information due to station changes (opening, closing, relocation, etc.) over time. For approximately 750 locations in Canada, "virtual" climate stations have been developed by joining (threading) climate data for an urban location, from nearby stations to make long-term records. Each long-term record consists of the extremes (record values) of daily maximum/minimum temperatures, total precipitation and snowfall for each day of the year. Many of the longest data sets of extremes date as far back as the 1800s. This data provides the daily extremes of record for Snowfall for each day of the year. Daily elements include: Greatest Snowfall.

# paths./collections/ltce-snowfall/schema.get.description
The daily climate records database, also known as Long Term Climate Extremes (LTCE), was developed to address the fragmentation of climate information due to station changes (opening, closing, relocation, etc.) over time. For approximately 750 locations in Canada, "virtual" climate stations have been developed by joining (threading) climate data for an urban location, from nearby stations to make long-term records. Each long-term record consists of the extremes (record values) of daily maximum/minimum temperatures, total precipitation and snowfall for each day of the year. Many of the longest data sets of extremes date as far back as the 1800s. This data provides the daily extremes of record for Snowfall for each day of the year. Daily elements include: Greatest Snowfall.

# paths./collections/ltce-stations.get.description
A Virtual Climate station is the result of threading together climate data from proximate current and historical stations to construct a long term threaded data set. For the purpose of identifying and tabulating daily extremes of record for temperature, precipitation and snowfall, the Meteorological Service of Canada has threaded or put together data from closely related stations to compile a long time series of data for about 750 locations in Canada to monitor for record-breaking weather. The length of the time series of virtual stations is often greater than 100 years. A Virtual Climate station is always named for an “Area” rather than a point, e.g. Winnipeg Area, to indicate that the data are drawn from that area(within a 20km radius from the urban center) rather than a single precise location.

# paths./collections/ltce-stations.get.summary
Get LTCE - Virtual Climate Stations metadata

# paths./collections/ltce-stations/items.get.description
A Virtual Climate station is the result of threading together climate data from proximate current and historical stations to construct a long term threaded data set. For the purpose of identifying and tabulating daily extremes of record for temperature, precipitation and snowfall, the Meteorological Service of Canada has threaded or put together data from closely related stations to compile a long time series of data for about 750 locations in Canada to monitor for record-breaking weather. The length of the time series of virtual stations is often greater than 100 years. A Virtual Climate station is always named for an “Area” rather than a point, e.g. Winnipeg Area, to indicate that the data are drawn from that area(within a 20km radius from the urban center) rather than a single precise location.

# paths./collections/ltce-stations/items.get.parameters[].description
The properties that should be included for each feature. The parameter value is a comma-separated list of property names.

# paths./collections/ltce-stations/items.get.summary
Get LTCE - Virtual Climate Stations items

# paths./collections/ltce-stations/items.options.description
A Virtual Climate station is the result of threading together climate data from proximate current and historical stations to construct a long term threaded data set. For the purpose of identifying and tabulating daily extremes of record for temperature, precipitation and snowfall, the Meteorological Service of Canada has threaded or put together data from closely related stations to compile a long time series of data for about 750 locations in Canada to monitor for record-breaking weather. The length of the time series of virtual stations is often greater than 100 years. A Virtual Climate station is always named for an “Area” rather than a point, e.g. Winnipeg Area, to indicate that the data are drawn from that area(within a 20km radius from the urban center) rather than a single precise location.

# paths./collections/ltce-stations/items.options.summary
Options for LTCE - Virtual Climate Stations items

# paths./collections/ltce-stations/items/{featureId}.get.description
A Virtual Climate station is the result of threading together climate data from proximate current and historical stations to construct a long term threaded data set. For the purpose of identifying and tabulating daily extremes of record for temperature, precipitation and snowfall, the Meteorological Service of Canada has threaded or put together data from closely related stations to compile a long time series of data for about 750 locations in Canada to monitor for record-breaking weather. The length of the time series of virtual stations is often greater than 100 years. A Virtual Climate station is always named for an “Area” rather than a point, e.g. Winnipeg Area, to indicate that the data are drawn from that area(within a 20km radius from the urban center) rather than a single precise location.

# paths./collections/ltce-stations/items/{featureId}.get.summary
Get LTCE - Virtual Climate Stations item by id

# paths./collections/ltce-stations/items/{featureId}.options.description
A Virtual Climate station is the result of threading together climate data from proximate current and historical stations to construct a long term threaded data set. For the purpose of identifying and tabulating daily extremes of record for temperature, precipitation and snowfall, the Meteorological Service of Canada has threaded or put together data from closely related stations to compile a long time series of data for about 750 locations in Canada to monitor for record-breaking weather. The length of the time series of virtual stations is often greater than 100 years. A Virtual Climate station is always named for an “Area” rather than a point, e.g. Winnipeg Area, to indicate that the data are drawn from that area(within a 20km radius from the urban center) rather than a single precise location.

# paths./collections/ltce-stations/items/{featureId}.options.summary
Options for LTCE - Virtual Climate Stations item by id

# paths./collections/ltce-stations/queryables.get.description
A Virtual Climate station is the result of threading together climate data from proximate current and historical stations to construct a long term threaded data set. For the purpose of identifying and tabulating daily extremes of record for temperature, precipitation and snowfall, the Meteorological Service of Canada has threaded or put together data from closely related stations to compile a long time series of data for about 750 locations in Canada to monitor for record-breaking weather. The length of the time series of virtual stations is often greater than 100 years. A Virtual Climate station is always named for an “Area” rather than a point, e.g. Winnipeg Area, to indicate that the data are drawn from that area(within a 20km radius from the urban center) rather than a single precise location.

# paths./collections/ltce-stations/queryables.get.summary
Get LTCE - Virtual Climate Stations queryables

# paths./collections/ltce-stations/schema.get.description
A Virtual Climate station is the result of threading together climate data from proximate current and historical stations to construct a long term threaded data set. For the purpose of identifying and tabulating daily extremes of record for temperature, precipitation and snowfall, the Meteorological Service of Canada has threaded or put together data from closely related stations to compile a long time series of data for about 750 locations in Canada to monitor for record-breaking weather. The length of the time series of virtual stations is often greater than 100 years. A Virtual Climate station is always named for an “Area” rather than a point, e.g. Winnipeg Area, to indicate that the data are drawn from that area(within a 20km radius from the urban center) rather than a single precise location.

# paths./collections/ltce-stations/schema.get.summary
Get LTCE - Virtual Climate Stations schema

# paths./collections/ltce-temperature.get.description
The daily climate records database, also known as Long Term Climate Extremes (LTCE), was developed to address the fragmentation of climate information due to station changes (opening, closing, relocation, etc.) over time. For approximately 750 locations in Canada, "virtual" climate stations have been developed by joining (threading) climate data for an urban location, from nearby stations to make long-term records. Each long-term record consists of the extremes (record values) of daily maximum/minimum temperatures, total precipitation and snowfall for each day of the year. Many of the longest data sets of extremes date as far back as the 1800s. This data provides the daily extremes of record for Temperature for each day of the year. Daily elements include: High Maximum, Low Maximum, High Minimum, Low Minimum.

# paths./collections/ltce-temperature/items.get.description
The daily climate records database, also known as Long Term Climate Extremes (LTCE), was developed to address the fragmentation of climate information due to station changes (opening, closing, relocation, etc.) over time. For approximately 750 locations in Canada, "virtual" climate stations have been developed by joining (threading) climate data for an urban location, from nearby stations to make long-term records. Each long-term record consists of the extremes (record values) of daily maximum/minimum temperatures, total precipitation and snowfall for each day of the year. Many of the longest data sets of extremes date as far back as the 1800s. This data provides the daily extremes of record for Temperature for each day of the year. Daily elements include: High Maximum, Low Maximum, High Minimum, Low Minimum.

# paths./collections/ltce-temperature/items.get.parameters[].description
The properties that should be included for each feature. The parameter value is a comma-separated list of property names.

# paths./collections/ltce-temperature/items.options.description
The daily climate records database, also known as Long Term Climate Extremes (LTCE), was developed to address the fragmentation of climate information due to station changes (opening, closing, relocation, etc.) over time. For approximately 750 locations in Canada, "virtual" climate stations have been developed by joining (threading) climate data for an urban location, from nearby stations to make long-term records. Each long-term record consists of the extremes (record values) of daily maximum/minimum temperatures, total precipitation and snowfall for each day of the year. Many of the longest data sets of extremes date as far back as the 1800s. This data provides the daily extremes of record for Temperature for each day of the year. Daily elements include: High Maximum, Low Maximum, High Minimum, Low Minimum.

# paths./collections/ltce-temperature/items/{featureId}.get.description
The daily climate records database, also known as Long Term Climate Extremes (LTCE), was developed to address the fragmentation of climate information due to station changes (opening, closing, relocation, etc.) over time. For approximately 750 locations in Canada, "virtual" climate stations have been developed by joining (threading) climate data for an urban location, from nearby stations to make long-term records. Each long-term record consists of the extremes (record values) of daily maximum/minimum temperatures, total precipitation and snowfall for each day of the year. Many of the longest data sets of extremes date as far back as the 1800s. This data provides the daily extremes of record for Temperature for each day of the year. Daily elements include: High Maximum, Low Maximum, High Minimum, Low Minimum.

# paths./collections/ltce-temperature/items/{featureId}.options.description
The daily climate records database, also known as Long Term Climate Extremes (LTCE), was developed to address the fragmentation of climate information due to station changes (opening, closing, relocation, etc.) over time. For approximately 750 locations in Canada, "virtual" climate stations have been developed by joining (threading) climate data for an urban location, from nearby stations to make long-term records. Each long-term record consists of the extremes (record values) of daily maximum/minimum temperatures, total precipitation and snowfall for each day of the year. Many of the longest data sets of extremes date as far back as the 1800s. This data provides the daily extremes of record for Temperature for each day of the year. Daily elements include: High Maximum, Low Maximum, High Minimum, Low Minimum.

# paths./collections/ltce-temperature/items/{featureId}.options.summary
Options for LTCE – Temperature item by id

# paths./collections/ltce-temperature/queryables.get.description
The daily climate records database, also known as Long Term Climate Extremes (LTCE), was developed to address the fragmentation of climate information due to station changes (opening, closing, relocation, etc.) over time. For approximately 750 locations in Canada, "virtual" climate stations have been developed by joining (threading) climate data for an urban location, from nearby stations to make long-term records. Each long-term record consists of the extremes (record values) of daily maximum/minimum temperatures, total precipitation and snowfall for each day of the year. Many of the longest data sets of extremes date as far back as the 1800s. This data provides the daily extremes of record for Temperature for each day of the year. Daily elements include: High Maximum, Low Maximum, High Minimum, Low Minimum.

# paths./collections/ltce-temperature/schema.get.description
The daily climate records database, also known as Long Term Climate Extremes (LTCE), was developed to address the fragmentation of climate information due to station changes (opening, closing, relocation, etc.) over time. For approximately 750 locations in Canada, "virtual" climate stations have been developed by joining (threading) climate data for an urban location, from nearby stations to make long-term records. Each long-term record consists of the extremes (record values) of daily maximum/minimum temperatures, total precipitation and snowfall for each day of the year. Many of the longest data sets of extremes date as far back as the 1800s. This data provides the daily extremes of record for Temperature for each day of the year. Daily elements include: High Maximum, Low Maximum, High Minimum, Low Minimum.

# paths./collections/marine-standard-forecast-zones.get.description
The Marine Standard Forecast Zones layer is a collection of forecast location polygons that represents bounded (zone) locations at the Marine Program Standard level. The Marine Program standard level is used in most forecasts, warnings, watches, advisories and special marine weather statements. This layer is comprised mainly of water based Marine regions, Met Areas, Lakes, Great Lakes and many of the larger Inland water bodies. The layer is further comprised of four depictions, cartographic detailed (with proper shorelines and high resolution polygons), cartographic coarse (with generalized shorelines and low resolution polygons), and digital exaggerated (with exaggerated shorelines and low resolution polygons) and the hybrid (mix of cartographic detail and exaggerated). The digital layer (exaggerated) is a collection of polygons where shoreline boundaries are stretched inland. In this "Exaggerated" part of the layer, the boundaries are made smooth to reduce the number of polygon vertices. Boundaries of the forecast regions of this hybrid set are derived from the polygon boundaries of both the exaggerated digital, and the cartographic detailed set. Polygon boundaries along the shoreline of this set follow the exaggerated boundaries while the inland boundaries follow the detailed lines. The Marine Standard Forecast Zones layer is also presented in the polygon package in two coordinate systems, "Projected" (Lambert Conic Conformal) and "Unprojected" (Geographical Coordinated System).

# paths./collections/marine-standard-forecast-zones.get.summary
Get Standard Forecast Zones - Marine (Hybrid) metadata

# paths./collections/marine-standard-forecast-zones/items.get.description
The Marine Standard Forecast Zones layer is a collection of forecast location polygons that represents bounded (zone) locations at the Marine Program Standard level. The Marine Program standard level is used in most forecasts, warnings, watches, advisories and special marine weather statements. This layer is comprised mainly of water based Marine regions, Met Areas, Lakes, Great Lakes and many of the larger Inland water bodies. The layer is further comprised of four depictions, cartographic detailed (with proper shorelines and high resolution polygons), cartographic coarse (with generalized shorelines and low resolution polygons), and digital exaggerated (with exaggerated shorelines and low resolution polygons) and the hybrid (mix of cartographic detail and exaggerated). The digital layer (exaggerated) is a collection of polygons where shoreline boundaries are stretched inland. In this "Exaggerated" part of the layer, the boundaries are made smooth to reduce the number of polygon vertices. Boundaries of the forecast regions of this hybrid set are derived from the polygon boundaries of both the exaggerated digital, and the cartographic detailed set. Polygon boundaries along the shoreline of this set follow the exaggerated boundaries while the inland boundaries follow the detailed lines. The Marine Standard Forecast Zones layer is also presented in the polygon package in two coordinate systems, "Projected" (Lambert Conic Conformal) and "Unprojected" (Geographical Coordinated System).

# paths./collections/marine-standard-forecast-zones/items.get.parameters[].description
The properties that should be included for each feature. The parameter value is a comma-separated list of property names.

# paths./collections/marine-standard-forecast-zones/items.get.summary
Get Standard Forecast Zones - Marine (Hybrid) items

# paths./collections/marine-standard-forecast-zones/items.options.description
The Marine Standard Forecast Zones layer is a collection of forecast location polygons that represents bounded (zone) locations at the Marine Program Standard level. The Marine Program standard level is used in most forecasts, warnings, watches, advisories and special marine weather statements. This layer is comprised mainly of water based Marine regions, Met Areas, Lakes, Great Lakes and many of the larger Inland water bodies. The layer is further comprised of four depictions, cartographic detailed (with proper shorelines and high resolution polygons), cartographic coarse (with generalized shorelines and low resolution polygons), and digital exaggerated (with exaggerated shorelines and low resolution polygons) and the hybrid (mix of cartographic detail and exaggerated). The digital layer (exaggerated) is a collection of polygons where shoreline boundaries are stretched inland. In this "Exaggerated" part of the layer, the boundaries are made smooth to reduce the number of polygon vertices. Boundaries of the forecast regions of this hybrid set are derived from the polygon boundaries of both the exaggerated digital, and the cartographic detailed set. Polygon boundaries along the shoreline of this set follow the exaggerated boundaries while the inland boundaries follow the detailed lines. The Marine Standard Forecast Zones layer is also presented in the polygon package in two coordinate systems, "Projected" (Lambert Conic Conformal) and "Unprojected" (Geographical Coordinated System).

# paths./collections/marine-standard-forecast-zones/items.options.summary
Options for Standard Forecast Zones - Marine (Hybrid) items

# paths./collections/marine-standard-forecast-zones/items/{featureId}.get.description
The Marine Standard Forecast Zones layer is a collection of forecast location polygons that represents bounded (zone) locations at the Marine Program Standard level. The Marine Program standard level is used in most forecasts, warnings, watches, advisories and special marine weather statements. This layer is comprised mainly of water based Marine regions, Met Areas, Lakes, Great Lakes and many of the larger Inland water bodies. The layer is further comprised of four depictions, cartographic detailed (with proper shorelines and high resolution polygons), cartographic coarse (with generalized shorelines and low resolution polygons), and digital exaggerated (with exaggerated shorelines and low resolution polygons) and the hybrid (mix of cartographic detail and exaggerated). The digital layer (exaggerated) is a collection of polygons where shoreline boundaries are stretched inland. In this "Exaggerated" part of the layer, the boundaries are made smooth to reduce the number of polygon vertices. Boundaries of the forecast regions of this hybrid set are derived from the polygon boundaries of both the exaggerated digital, and the cartographic detailed set. Polygon boundaries along the shoreline of this set follow the exaggerated boundaries while the inland boundaries follow the detailed lines. The Marine Standard Forecast Zones layer is also presented in the polygon package in two coordinate systems, "Projected" (Lambert Conic Conformal) and "Unprojected" (Geographical Coordinated System).

# paths./collections/marine-standard-forecast-zones/items/{featureId}.get.summary
Get Standard Forecast Zones - Marine (Hybrid) item by id

# paths./collections/marine-standard-forecast-zones/items/{featureId}.options.description
The Marine Standard Forecast Zones layer is a collection of forecast location polygons that represents bounded (zone) locations at the Marine Program Standard level. The Marine Program standard level is used in most forecasts, warnings, watches, advisories and special marine weather statements. This layer is comprised mainly of water based Marine regions, Met Areas, Lakes, Great Lakes and many of the larger Inland water bodies. The layer is further comprised of four depictions, cartographic detailed (with proper shorelines and high resolution polygons), cartographic coarse (with generalized shorelines and low resolution polygons), and digital exaggerated (with exaggerated shorelines and low resolution polygons) and the hybrid (mix of cartographic detail and exaggerated). The digital layer (exaggerated) is a collection of polygons where shoreline boundaries are stretched inland. In this "Exaggerated" part of the layer, the boundaries are made smooth to reduce the number of polygon vertices. Boundaries of the forecast regions of this hybrid set are derived from the polygon boundaries of both the exaggerated digital, and the cartographic detailed set. Polygon boundaries along the shoreline of this set follow the exaggerated boundaries while the inland boundaries follow the detailed lines. The Marine Standard Forecast Zones layer is also presented in the polygon package in two coordinate systems, "Projected" (Lambert Conic Conformal) and "Unprojected" (Geographical Coordinated System).

# paths./collections/marine-standard-forecast-zones/items/{featureId}.options.summary
Options for Standard Forecast Zones - Marine (Hybrid) item by id

# paths./collections/marine-standard-forecast-zones/queryables.get.description
The Marine Standard Forecast Zones layer is a collection of forecast location polygons that represents bounded (zone) locations at the Marine Program Standard level. The Marine Program standard level is used in most forecasts, warnings, watches, advisories and special marine weather statements. This layer is comprised mainly of water based Marine regions, Met Areas, Lakes, Great Lakes and many of the larger Inland water bodies. The layer is further comprised of four depictions, cartographic detailed (with proper shorelines and high resolution polygons), cartographic coarse (with generalized shorelines and low resolution polygons), and digital exaggerated (with exaggerated shorelines and low resolution polygons) and the hybrid (mix of cartographic detail and exaggerated). The digital layer (exaggerated) is a collection of polygons where shoreline boundaries are stretched inland. In this "Exaggerated" part of the layer, the boundaries are made smooth to reduce the number of polygon vertices. Boundaries of the forecast regions of this hybrid set are derived from the polygon boundaries of both the exaggerated digital, and the cartographic detailed set. Polygon boundaries along the shoreline of this set follow the exaggerated boundaries while the inland boundaries follow the detailed lines. The Marine Standard Forecast Zones layer is also presented in the polygon package in two coordinate systems, "Projected" (Lambert Conic Conformal) and "Unprojected" (Geographical Coordinated System).

# paths./collections/marine-standard-forecast-zones/queryables.get.summary
Get Standard Forecast Zones - Marine (Hybrid) queryables

# paths./collections/marine-standard-forecast-zones/schema.get.description
The Marine Standard Forecast Zones layer is a collection of forecast location polygons that represents bounded (zone) locations at the Marine Program Standard level. The Marine Program standard level is used in most forecasts, warnings, watches, advisories and special marine weather statements. This layer is comprised mainly of water based Marine regions, Met Areas, Lakes, Great Lakes and many of the larger Inland water bodies. The layer is further comprised of four depictions, cartographic detailed (with proper shorelines and high resolution polygons), cartographic coarse (with generalized shorelines and low resolution polygons), and digital exaggerated (with exaggerated shorelines and low resolution polygons) and the hybrid (mix of cartographic detail and exaggerated). The digital layer (exaggerated) is a collection of polygons where shoreline boundaries are stretched inland. In this "Exaggerated" part of the layer, the boundaries are made smooth to reduce the number of polygon vertices. Boundaries of the forecast regions of this hybrid set are derived from the polygon boundaries of both the exaggerated digital, and the cartographic detailed set. Polygon boundaries along the shoreline of this set follow the exaggerated boundaries while the inland boundaries follow the detailed lines. The Marine Standard Forecast Zones layer is also presented in the polygon package in two coordinate systems, "Projected" (Lambert Conic Conformal) and "Unprojected" (Geographical Coordinated System).

# paths./collections/marine-standard-forecast-zones/schema.get.summary
Get Standard Forecast Zones - Marine (Hybrid) schema

# paths./collections/marineweather-realtime.get.description
Environment Canada (EC) provides a marine forecasting service to mariners and marine interests. This enables them to make informed decisions about marine weather conditions that may impact their safety, security or operations. EC also provides marine forecasts in order to meet Canada’s treaty obligations under the International Maritime Organization’s Safety Of Life At Sea (SOLAS) Convention.

# paths./collections/marineweather-realtime.get.summary
Get Marine forecasts and warnings [experimental] metadata

# paths./collections/marineweather-realtime/items.get.description
Environment Canada (EC) provides a marine forecasting service to mariners and marine interests. This enables them to make informed decisions about marine weather conditions that may impact their safety, security or operations. EC also provides marine forecasts in order to meet Canada’s treaty obligations under the International Maritime Organization’s Safety Of Life At Sea (SOLAS) Convention.

# paths./collections/marineweather-realtime/items.get.parameters[].description
The properties that should be included for each feature. The parameter value is a comma-separated list of property names.

# paths./collections/marineweather-realtime/items.get.summary
Get Marine forecasts and warnings [experimental] items

# paths./collections/marineweather-realtime/items.options.description
Environment Canada (EC) provides a marine forecasting service to mariners and marine interests. This enables them to make informed decisions about marine weather conditions that may impact their safety, security or operations. EC also provides marine forecasts in order to meet Canada’s treaty obligations under the International Maritime Organization’s Safety Of Life At Sea (SOLAS) Convention.

# paths./collections/marineweather-realtime/items.options.summary
Options for Marine forecasts and warnings [experimental] items

# paths./collections/marineweather-realtime/items/{featureId}.get.description
Environment Canada (EC) provides a marine forecasting service to mariners and marine interests. This enables them to make informed decisions about marine weather conditions that may impact their safety, security or operations. EC also provides marine forecasts in order to meet Canada’s treaty obligations under the International Maritime Organization’s Safety Of Life At Sea (SOLAS) Convention.

# paths./collections/marineweather-realtime/items/{featureId}.get.summary
Get Marine forecasts and warnings [experimental] item by id

# paths./collections/marineweather-realtime/items/{featureId}.options.description
Environment Canada (EC) provides a marine forecasting service to mariners and marine interests. This enables them to make informed decisions about marine weather conditions that may impact their safety, security or operations. EC also provides marine forecasts in order to meet Canada’s treaty obligations under the International Maritime Organization’s Safety Of Life At Sea (SOLAS) Convention.

# paths./collections/marineweather-realtime/items/{featureId}.options.summary
Options for Marine forecasts and warnings [experimental] item by id

# paths./collections/marineweather-realtime/queryables.get.description
Environment Canada (EC) provides a marine forecasting service to mariners and marine interests. This enables them to make informed decisions about marine weather conditions that may impact their safety, security or operations. EC also provides marine forecasts in order to meet Canada’s treaty obligations under the International Maritime Organization’s Safety Of Life At Sea (SOLAS) Convention.

# paths./collections/marineweather-realtime/queryables.get.summary
Get Marine forecasts and warnings [experimental] queryables

# paths./collections/marineweather-realtime/schema.get.description
Environment Canada (EC) provides a marine forecasting service to mariners and marine interests. This enables them to make informed decisions about marine weather conditions that may impact their safety, security or operations. EC also provides marine forecasts in order to meet Canada’s treaty obligations under the International Maritime Organization’s Safety Of Life At Sea (SOLAS) Convention.

# paths./collections/marineweather-realtime/schema.get.summary
Get Marine forecasts and warnings [experimental] schema

# paths./collections/metnotes.get.description
MetNotes are a geo- and time-referenced, free form polygon product issued by MSC that complement today's location-based dissemination systems. The concise text of a MetNote (similar to a Tweet) is consistent with communication today where people are seeking information at a glance. Meteorologists will issue a MetNote to add contextual and/or impact information to complement the public forecast that is valid over a specific area, for a specific time range.

# paths./collections/metnotes/items.get.description
MetNotes are a geo- and time-referenced, free form polygon product issued by MSC that complement today's location-based dissemination systems. The concise text of a MetNote (similar to a Tweet) is consistent with communication today where people are seeking information at a glance. Meteorologists will issue a MetNote to add contextual and/or impact information to complement the public forecast that is valid over a specific area, for a specific time range.

# paths./collections/metnotes/items.get.parameters[].description
The properties that should be included for each feature. The parameter value is a comma-separated list of property names.

# paths./collections/metnotes/items.options.description
MetNotes are a geo- and time-referenced, free form polygon product issued by MSC that complement today's location-based dissemination systems. The concise text of a MetNote (similar to a Tweet) is consistent with communication today where people are seeking information at a glance. Meteorologists will issue a MetNote to add contextual and/or impact information to complement the public forecast that is valid over a specific area, for a specific time range.

# paths./collections/metnotes/items/{featureId}.get.description
MetNotes are a geo- and time-referenced, free form polygon product issued by MSC that complement today's location-based dissemination systems. The concise text of a MetNote (similar to a Tweet) is consistent with communication today where people are seeking information at a glance. Meteorologists will issue a MetNote to add contextual and/or impact information to complement the public forecast that is valid over a specific area, for a specific time range.

# paths./collections/metnotes/items/{featureId}.options.description
MetNotes are a geo- and time-referenced, free form polygon product issued by MSC that complement today's location-based dissemination systems. The concise text of a MetNote (similar to a Tweet) is consistent with communication today where people are seeking information at a glance. Meteorologists will issue a MetNote to add contextual and/or impact information to complement the public forecast that is valid over a specific area, for a specific time range.

# paths./collections/metnotes/queryables.get.description
MetNotes are a geo- and time-referenced, free form polygon product issued by MSC that complement today's location-based dissemination systems. The concise text of a MetNote (similar to a Tweet) is consistent with communication today where people are seeking information at a glance. Meteorologists will issue a MetNote to add contextual and/or impact information to complement the public forecast that is valid over a specific area, for a specific time range.

# paths./collections/metnotes/schema.get.description
MetNotes are a geo- and time-referenced, free form polygon product issued by MSC that complement today's location-based dissemination systems. The concise text of a MetNote (similar to a Tweet) is consistent with communication today where people are seeking information at a glance. Meteorologists will issue a MetNote to add contextual and/or impact information to complement the public forecast that is valid over a specific area, for a specific time range.

# paths./collections/prognos-gdps-realtime.get.description
Statistical post-processing of forecasts from numerical weather and environmental models, including the Global Deterministic Prediction System (GDPS), helps reduce systematic biases and error variance in raw forecasts. This is achieved by establishing optimized statistical relationships between observations recorded at stations and numerical model outputs at nearby grid points. The PROGNOS system is a software package developed by Environment and Climate Change Canada that enables this. Relationships are built using the 'Model Output Statistics' (MOS) method and multiple linear regression (MLR). Currently, only the air temperature at 1.5 meters above the Earth's surface is statistically post-processed. The absence of statistically post-processed forecasts can be due to the unavailability of a statistical model caused by insufficient observation quality or quantity. The geographical region covered by this data includes the territory covered by Canadian meteorological stations and some American buoys. For the GDPS, statistically post-processed forecasts are available at the same issuance frequency as the raw forecasts produced by numerical models (e.g., 00Z, 12Z) and at hourly forecast lead times, from 00 to 84 hours, and then every 3 hours, from 87 to 168 hours.

# paths./collections/prognos-gdps-realtime.get.summary
Get PROGNOS - Global Deterministic Prediction System (GDPS) statistically post-processed by PROGNOS-MLR metadata

# paths./collections/prognos-gdps-realtime/items.get.description
Statistical post-processing of forecasts from numerical weather and environmental models, including the Global Deterministic Prediction System (GDPS), helps reduce systematic biases and error variance in raw forecasts. This is achieved by establishing optimized statistical relationships between observations recorded at stations and numerical model outputs at nearby grid points. The PROGNOS system is a software package developed by Environment and Climate Change Canada that enables this. Relationships are built using the 'Model Output Statistics' (MOS) method and multiple linear regression (MLR). Currently, only the air temperature at 1.5 meters above the Earth's surface is statistically post-processed. The absence of statistically post-processed forecasts can be due to the unavailability of a statistical model caused by insufficient observation quality or quantity. The geographical region covered by this data includes the territory covered by Canadian meteorological stations and some American buoys. For the GDPS, statistically post-processed forecasts are available at the same issuance frequency as the raw forecasts produced by numerical models (e.g., 00Z, 12Z) and at hourly forecast lead times, from 00 to 84 hours, and then every 3 hours, from 87 to 168 hours.

# paths./collections/prognos-gdps-realtime/items.get.parameters[].description
The properties that should be included for each feature. The parameter value is a comma-separated list of property names.

# paths./collections/prognos-gdps-realtime/items.get.summary
Get PROGNOS - Global Deterministic Prediction System (GDPS) statistically post-processed by PROGNOS-MLR items

# paths./collections/prognos-gdps-realtime/items.options.description
Statistical post-processing of forecasts from numerical weather and environmental models, including the Global Deterministic Prediction System (GDPS), helps reduce systematic biases and error variance in raw forecasts. This is achieved by establishing optimized statistical relationships between observations recorded at stations and numerical model outputs at nearby grid points. The PROGNOS system is a software package developed by Environment and Climate Change Canada that enables this. Relationships are built using the 'Model Output Statistics' (MOS) method and multiple linear regression (MLR). Currently, only the air temperature at 1.5 meters above the Earth's surface is statistically post-processed. The absence of statistically post-processed forecasts can be due to the unavailability of a statistical model caused by insufficient observation quality or quantity. The geographical region covered by this data includes the territory covered by Canadian meteorological stations and some American buoys. For the GDPS, statistically post-processed forecasts are available at the same issuance frequency as the raw forecasts produced by numerical models (e.g., 00Z, 12Z) and at hourly forecast lead times, from 00 to 84 hours, and then every 3 hours, from 87 to 168 hours.

# paths./collections/prognos-gdps-realtime/items.options.summary
Options for PROGNOS - Global Deterministic Prediction System (GDPS) statistically post-processed by PROGNOS-MLR items

# paths./collections/prognos-gdps-realtime/items/{featureId}.get.description
Statistical post-processing of forecasts from numerical weather and environmental models, including the Global Deterministic Prediction System (GDPS), helps reduce systematic biases and error variance in raw forecasts. This is achieved by establishing optimized statistical relationships between observations recorded at stations and numerical model outputs at nearby grid points. The PROGNOS system is a software package developed by Environment and Climate Change Canada that enables this. Relationships are built using the 'Model Output Statistics' (MOS) method and multiple linear regression (MLR). Currently, only the air temperature at 1.5 meters above the Earth's surface is statistically post-processed. The absence of statistically post-processed forecasts can be due to the unavailability of a statistical model caused by insufficient observation quality or quantity. The geographical region covered by this data includes the territory covered by Canadian meteorological stations and some American buoys. For the GDPS, statistically post-processed forecasts are available at the same issuance frequency as the raw forecasts produced by numerical models (e.g., 00Z, 12Z) and at hourly forecast lead times, from 00 to 84 hours, and then every 3 hours, from 87 to 168 hours.

# paths./collections/prognos-gdps-realtime/items/{featureId}.get.summary
Get PROGNOS - Global Deterministic Prediction System (GDPS) statistically post-processed by PROGNOS-MLR item by id

# paths./collections/prognos-gdps-realtime/items/{featureId}.options.description
Statistical post-processing of forecasts from numerical weather and environmental models, including the Global Deterministic Prediction System (GDPS), helps reduce systematic biases and error variance in raw forecasts. This is achieved by establishing optimized statistical relationships between observations recorded at stations and numerical model outputs at nearby grid points. The PROGNOS system is a software package developed by Environment and Climate Change Canada that enables this. Relationships are built using the 'Model Output Statistics' (MOS) method and multiple linear regression (MLR). Currently, only the air temperature at 1.5 meters above the Earth's surface is statistically post-processed. The absence of statistically post-processed forecasts can be due to the unavailability of a statistical model caused by insufficient observation quality or quantity. The geographical region covered by this data includes the territory covered by Canadian meteorological stations and some American buoys. For the GDPS, statistically post-processed forecasts are available at the same issuance frequency as the raw forecasts produced by numerical models (e.g., 00Z, 12Z) and at hourly forecast lead times, from 00 to 84 hours, and then every 3 hours, from 87 to 168 hours.

# paths./collections/prognos-gdps-realtime/items/{featureId}.options.summary
Options for PROGNOS - Global Deterministic Prediction System (GDPS) statistically post-processed by PROGNOS-MLR item by id

# paths./collections/prognos-gdps-realtime/queryables.get.description
Statistical post-processing of forecasts from numerical weather and environmental models, including the Global Deterministic Prediction System (GDPS), helps reduce systematic biases and error variance in raw forecasts. This is achieved by establishing optimized statistical relationships between observations recorded at stations and numerical model outputs at nearby grid points. The PROGNOS system is a software package developed by Environment and Climate Change Canada that enables this. Relationships are built using the 'Model Output Statistics' (MOS) method and multiple linear regression (MLR). Currently, only the air temperature at 1.5 meters above the Earth's surface is statistically post-processed. The absence of statistically post-processed forecasts can be due to the unavailability of a statistical model caused by insufficient observation quality or quantity. The geographical region covered by this data includes the territory covered by Canadian meteorological stations and some American buoys. For the GDPS, statistically post-processed forecasts are available at the same issuance frequency as the raw forecasts produced by numerical models (e.g., 00Z, 12Z) and at hourly forecast lead times, from 00 to 84 hours, and then every 3 hours, from 87 to 168 hours.

# paths./collections/prognos-gdps-realtime/queryables.get.summary
Get PROGNOS - Global Deterministic Prediction System (GDPS) statistically post-processed by PROGNOS-MLR queryables

# paths./collections/prognos-gdps-realtime/schema.get.description
Statistical post-processing of forecasts from numerical weather and environmental models, including the Global Deterministic Prediction System (GDPS), helps reduce systematic biases and error variance in raw forecasts. This is achieved by establishing optimized statistical relationships between observations recorded at stations and numerical model outputs at nearby grid points. The PROGNOS system is a software package developed by Environment and Climate Change Canada that enables this. Relationships are built using the 'Model Output Statistics' (MOS) method and multiple linear regression (MLR). Currently, only the air temperature at 1.5 meters above the Earth's surface is statistically post-processed. The absence of statistically post-processed forecasts can be due to the unavailability of a statistical model caused by insufficient observation quality or quantity. The geographical region covered by this data includes the territory covered by Canadian meteorological stations and some American buoys. For the GDPS, statistically post-processed forecasts are available at the same issuance frequency as the raw forecasts produced by numerical models (e.g., 00Z, 12Z) and at hourly forecast lead times, from 00 to 84 hours, and then every 3 hours, from 87 to 168 hours.

# paths./collections/prognos-gdps-realtime/schema.get.summary
Get PROGNOS - Global Deterministic Prediction System (GDPS) statistically post-processed by PROGNOS-MLR schema

# paths./collections/prognos-hrdps-realtime.get.description
Statistical post-processing of forecasts from numerical weather and environmental models, including the High Resolution Deterministic Prediction System (HRDPS), helps reduce systematic biases and error variance in raw forecasts. This is achieved by establishing optimized statistical relationships between observations recorded at stations and numerical model outputs at nearby grid points. The PROGNOS system is a software package developed by Environment and Climate Change Canada that enables this. Relationships are built using the 'Model Output Statistics' (MOS) method and multiple linear regression (MLR). Currently, only the air temperature at 1.5 meters above the Earth's surface is statistically post-processed. The absence of statistically post-processed forecasts can be due to the unavailability of a statistical model caused by insufficient observation quality or quantity. The geographical region covered by this data includes the territory covered by Canadian meteorological stations and some American buoys. For the HRDPS, statistically post-processed forecasts are available at the same issuance frequency as the raw forecasts produced by numerical models (e.g., 00Z, 06Z, 12Z, 18Z) and at hourly forecast lead times, from 00 to 48 hours.

# paths./collections/prognos-hrdps-realtime.get.summary
Get PROGNOS - High Resolution Deterministic Prediction System (HRDPS) statistically post-processed by PROGNOS-MLR metadata

# paths./collections/prognos-hrdps-realtime/items.get.description
Statistical post-processing of forecasts from numerical weather and environmental models, including the High Resolution Deterministic Prediction System (HRDPS), helps reduce systematic biases and error variance in raw forecasts. This is achieved by establishing optimized statistical relationships between observations recorded at stations and numerical model outputs at nearby grid points. The PROGNOS system is a software package developed by Environment and Climate Change Canada that enables this. Relationships are built using the 'Model Output Statistics' (MOS) method and multiple linear regression (MLR). Currently, only the air temperature at 1.5 meters above the Earth's surface is statistically post-processed. The absence of statistically post-processed forecasts can be due to the unavailability of a statistical model caused by insufficient observation quality or quantity. The geographical region covered by this data includes the territory covered by Canadian meteorological stations and some American buoys. For the HRDPS, statistically post-processed forecasts are available at the same issuance frequency as the raw forecasts produced by numerical models (e.g., 00Z, 06Z, 12Z, 18Z) and at hourly forecast lead times, from 00 to 48 hours.

# paths./collections/prognos-hrdps-realtime/items.get.parameters[].description
The properties that should be included for each feature. The parameter value is a comma-separated list of property names.

# paths./collections/prognos-hrdps-realtime/items.get.summary
Get PROGNOS - High Resolution Deterministic Prediction System (HRDPS) statistically post-processed by PROGNOS-MLR items

# paths./collections/prognos-hrdps-realtime/items.options.description
Statistical post-processing of forecasts from numerical weather and environmental models, including the High Resolution Deterministic Prediction System (HRDPS), helps reduce systematic biases and error variance in raw forecasts. This is achieved by establishing optimized statistical relationships between observations recorded at stations and numerical model outputs at nearby grid points. The PROGNOS system is a software package developed by Environment and Climate Change Canada that enables this. Relationships are built using the 'Model Output Statistics' (MOS) method and multiple linear regression (MLR). Currently, only the air temperature at 1.5 meters above the Earth's surface is statistically post-processed. The absence of statistically post-processed forecasts can be due to the unavailability of a statistical model caused by insufficient observation quality or quantity. The geographical region covered by this data includes the territory covered by Canadian meteorological stations and some American buoys. For the HRDPS, statistically post-processed forecasts are available at the same issuance frequency as the raw forecasts produced by numerical models (e.g., 00Z, 06Z, 12Z, 18Z) and at hourly forecast lead times, from 00 to 48 hours.

# paths./collections/prognos-hrdps-realtime/items.options.summary
Options for PROGNOS - High Resolution Deterministic Prediction System (HRDPS) statistically post-processed by PROGNOS-MLR items

# paths./collections/prognos-hrdps-realtime/items/{featureId}.get.description
Statistical post-processing of forecasts from numerical weather and environmental models, including the High Resolution Deterministic Prediction System (HRDPS), helps reduce systematic biases and error variance in raw forecasts. This is achieved by establishing optimized statistical relationships between observations recorded at stations and numerical model outputs at nearby grid points. The PROGNOS system is a software package developed by Environment and Climate Change Canada that enables this. Relationships are built using the 'Model Output Statistics' (MOS) method and multiple linear regression (MLR). Currently, only the air temperature at 1.5 meters above the Earth's surface is statistically post-processed. The absence of statistically post-processed forecasts can be due to the unavailability of a statistical model caused by insufficient observation quality or quantity. The geographical region covered by this data includes the territory covered by Canadian meteorological stations and some American buoys. For the HRDPS, statistically post-processed forecasts are available at the same issuance frequency as the raw forecasts produced by numerical models (e.g., 00Z, 06Z, 12Z, 18Z) and at hourly forecast lead times, from 00 to 48 hours.

# paths./collections/prognos-hrdps-realtime/items/{featureId}.get.summary
Get PROGNOS - High Resolution Deterministic Prediction System (HRDPS) statistically post-processed by PROGNOS-MLR item by id

# paths./collections/prognos-hrdps-realtime/items/{featureId}.options.description
Statistical post-processing of forecasts from numerical weather and environmental models, including the High Resolution Deterministic Prediction System (HRDPS), helps reduce systematic biases and error variance in raw forecasts. This is achieved by establishing optimized statistical relationships between observations recorded at stations and numerical model outputs at nearby grid points. The PROGNOS system is a software package developed by Environment and Climate Change Canada that enables this. Relationships are built using the 'Model Output Statistics' (MOS) method and multiple linear regression (MLR). Currently, only the air temperature at 1.5 meters above the Earth's surface is statistically post-processed. The absence of statistically post-processed forecasts can be due to the unavailability of a statistical model caused by insufficient observation quality or quantity. The geographical region covered by this data includes the territory covered by Canadian meteorological stations and some American buoys. For the HRDPS, statistically post-processed forecasts are available at the same issuance frequency as the raw forecasts produced by numerical models (e.g., 00Z, 06Z, 12Z, 18Z) and at hourly forecast lead times, from 00 to 48 hours.

# paths./collections/prognos-hrdps-realtime/items/{featureId}.options.summary
Options for PROGNOS - High Resolution Deterministic Prediction System (HRDPS) statistically post-processed by PROGNOS-MLR item by id

# paths./collections/prognos-hrdps-realtime/queryables.get.description
Statistical post-processing of forecasts from numerical weather and environmental models, including the High Resolution Deterministic Prediction System (HRDPS), helps reduce systematic biases and error variance in raw forecasts. This is achieved by establishing optimized statistical relationships between observations recorded at stations and numerical model outputs at nearby grid points. The PROGNOS system is a software package developed by Environment and Climate Change Canada that enables this. Relationships are built using the 'Model Output Statistics' (MOS) method and multiple linear regression (MLR). Currently, only the air temperature at 1.5 meters above the Earth's surface is statistically post-processed. The absence of statistically post-processed forecasts can be due to the unavailability of a statistical model caused by insufficient observation quality or quantity. The geographical region covered by this data includes the territory covered by Canadian meteorological stations and some American buoys. For the HRDPS, statistically post-processed forecasts are available at the same issuance frequency as the raw forecasts produced by numerical models (e.g., 00Z, 06Z, 12Z, 18Z) and at hourly forecast lead times, from 00 to 48 hours.

# paths./collections/prognos-hrdps-realtime/queryables.get.summary
Get PROGNOS - High Resolution Deterministic Prediction System (HRDPS) statistically post-processed by PROGNOS-MLR queryables

# paths./collections/prognos-hrdps-realtime/schema.get.description
Statistical post-processing of forecasts from numerical weather and environmental models, including the High Resolution Deterministic Prediction System (HRDPS), helps reduce systematic biases and error variance in raw forecasts. This is achieved by establishing optimized statistical relationships between observations recorded at stations and numerical model outputs at nearby grid points. The PROGNOS system is a software package developed by Environment and Climate Change Canada that enables this. Relationships are built using the 'Model Output Statistics' (MOS) method and multiple linear regression (MLR). Currently, only the air temperature at 1.5 meters above the Earth's surface is statistically post-processed. The absence of statistically post-processed forecasts can be due to the unavailability of a statistical model caused by insufficient observation quality or quantity. The geographical region covered by this data includes the territory covered by Canadian meteorological stations and some American buoys. For the HRDPS, statistically post-processed forecasts are available at the same issuance frequency as the raw forecasts produced by numerical models (e.g., 00Z, 06Z, 12Z, 18Z) and at hourly forecast lead times, from 00 to 48 hours.

# paths./collections/prognos-hrdps-realtime/schema.get.summary
Get PROGNOS - High Resolution Deterministic Prediction System (HRDPS) statistically post-processed by PROGNOS-MLR schema

# paths./collections/prognos-rdps-realtime.get.description
Statistical post-processing of forecasts from numerical weather and environmental models, including the Regional Deterministic Prediction System (RDPS), helps reduce systematic biases and error variance in raw forecasts. This is achieved by establishing optimized statistical relationships between observations recorded at stations and numerical model outputs at nearby grid points. The PROGNOS system is a software package developed by Environment and Climate Change Canada that enables this. Relationships are built using the 'Model Output Statistics' (MOS) method and multiple linear regression (MLR). The statistically post-processed weather and environmental variables include air and dew point temperatures at 1.5 meters above the Earth's surface, as well as wind speed and direction at 10 meters above the Earth's surface or at anemometer level in the case of a buoy. The absence of statistically post-processed forecasts can be due to the unavailability of a statistical model caused by insufficient observation quality or quantity. Furthermore, the absence of wind direction forecasts can also be due to predicted wind components being too weak to provide a reliable result. Wind direction and speed forecasts are derived from independent statistical post-processing models. The geographical region covered by this data includes the territory covered by Canadian meteorological stations and some American buoys. For the RDPS, statistically post-processed forecasts are available at the same issuance frequency as the raw forecasts produced by numerical models (e.g., 00Z, 06Z, 12Z, 18Z) and at hourly forecast lead times, from 00 to 84 hours.

# paths./collections/prognos-rdps-realtime.get.summary
Get PROGNOS - Regional Deterministic Prediction System (RDPS) statistically post-processed by PROGNOS-MLR metadata

# paths./collections/prognos-rdps-realtime/items.get.description
Statistical post-processing of forecasts from numerical weather and environmental models, including the Regional Deterministic Prediction System (RDPS), helps reduce systematic biases and error variance in raw forecasts. This is achieved by establishing optimized statistical relationships between observations recorded at stations and numerical model outputs at nearby grid points. The PROGNOS system is a software package developed by Environment and Climate Change Canada that enables this. Relationships are built using the 'Model Output Statistics' (MOS) method and multiple linear regression (MLR). The statistically post-processed weather and environmental variables include air and dew point temperatures at 1.5 meters above the Earth's surface, as well as wind speed and direction at 10 meters above the Earth's surface or at anemometer level in the case of a buoy. The absence of statistically post-processed forecasts can be due to the unavailability of a statistical model caused by insufficient observation quality or quantity. Furthermore, the absence of wind direction forecasts can also be due to predicted wind components being too weak to provide a reliable result. Wind direction and speed forecasts are derived from independent statistical post-processing models. The geographical region covered by this data includes the territory covered by Canadian meteorological stations and some American buoys. For the RDPS, statistically post-processed forecasts are available at the same issuance frequency as the raw forecasts produced by numerical models (e.g., 00Z, 06Z, 12Z, 18Z) and at hourly forecast lead times, from 00 to 84 hours.

# paths./collections/prognos-rdps-realtime/items.get.parameters[].description
The properties that should be included for each feature. The parameter value is a comma-separated list of property names.

# paths./collections/prognos-rdps-realtime/items.get.summary
Get PROGNOS - Regional Deterministic Prediction System (RDPS) statistically post-processed by PROGNOS-MLR items

# paths./collections/prognos-rdps-realtime/items.options.description
Statistical post-processing of forecasts from numerical weather and environmental models, including the Regional Deterministic Prediction System (RDPS), helps reduce systematic biases and error variance in raw forecasts. This is achieved by establishing optimized statistical relationships between observations recorded at stations and numerical model outputs at nearby grid points. The PROGNOS system is a software package developed by Environment and Climate Change Canada that enables this. Relationships are built using the 'Model Output Statistics' (MOS) method and multiple linear regression (MLR). The statistically post-processed weather and environmental variables include air and dew point temperatures at 1.5 meters above the Earth's surface, as well as wind speed and direction at 10 meters above the Earth's surface or at anemometer level in the case of a buoy. The absence of statistically post-processed forecasts can be due to the unavailability of a statistical model caused by insufficient observation quality or quantity. Furthermore, the absence of wind direction forecasts can also be due to predicted wind components being too weak to provide a reliable result. Wind direction and speed forecasts are derived from independent statistical post-processing models. The geographical region covered by this data includes the territory covered by Canadian meteorological stations and some American buoys. For the RDPS, statistically post-processed forecasts are available at the same issuance frequency as the raw forecasts produced by numerical models (e.g., 00Z, 06Z, 12Z, 18Z) and at hourly forecast lead times, from 00 to 84 hours.

# paths./collections/prognos-rdps-realtime/items.options.summary
Options for PROGNOS - Regional Deterministic Prediction System (RDPS) statistically post-processed by PROGNOS-MLR items

# paths./collections/prognos-rdps-realtime/items/{featureId}.get.description
Statistical post-processing of forecasts from numerical weather and environmental models, including the Regional Deterministic Prediction System (RDPS), helps reduce systematic biases and error variance in raw forecasts. This is achieved by establishing optimized statistical relationships between observations recorded at stations and numerical model outputs at nearby grid points. The PROGNOS system is a software package developed by Environment and Climate Change Canada that enables this. Relationships are built using the 'Model Output Statistics' (MOS) method and multiple linear regression (MLR). The statistically post-processed weather and environmental variables include air and dew point temperatures at 1.5 meters above the Earth's surface, as well as wind speed and direction at 10 meters above the Earth's surface or at anemometer level in the case of a buoy. The absence of statistically post-processed forecasts can be due to the unavailability of a statistical model caused by insufficient observation quality or quantity. Furthermore, the absence of wind direction forecasts can also be due to predicted wind components being too weak to provide a reliable result. Wind direction and speed forecasts are derived from independent statistical post-processing models. The geographical region covered by this data includes the territory covered by Canadian meteorological stations and some American buoys. For the RDPS, statistically post-processed forecasts are available at the same issuance frequency as the raw forecasts produced by numerical models (e.g., 00Z, 06Z, 12Z, 18Z) and at hourly forecast lead times, from 00 to 84 hours.

# paths./collections/prognos-rdps-realtime/items/{featureId}.get.summary
Get PROGNOS - Regional Deterministic Prediction System (RDPS) statistically post-processed by PROGNOS-MLR item by id

# paths./collections/prognos-rdps-realtime/items/{featureId}.options.description
Statistical post-processing of forecasts from numerical weather and environmental models, including the Regional Deterministic Prediction System (RDPS), helps reduce systematic biases and error variance in raw forecasts. This is achieved by establishing optimized statistical relationships between observations recorded at stations and numerical model outputs at nearby grid points. The PROGNOS system is a software package developed by Environment and Climate Change Canada that enables this. Relationships are built using the 'Model Output Statistics' (MOS) method and multiple linear regression (MLR). The statistically post-processed weather and environmental variables include air and dew point temperatures at 1.5 meters above the Earth's surface, as well as wind speed and direction at 10 meters above the Earth's surface or at anemometer level in the case of a buoy. The absence of statistically post-processed forecasts can be due to the unavailability of a statistical model caused by insufficient observation quality or quantity. Furthermore, the absence of wind direction forecasts can also be due to predicted wind components being too weak to provide a reliable result. Wind direction and speed forecasts are derived from independent statistical post-processing models. The geographical region covered by this data includes the territory covered by Canadian meteorological stations and some American buoys. For the RDPS, statistically post-processed forecasts are available at the same issuance frequency as the raw forecasts produced by numerical models (e.g., 00Z, 06Z, 12Z, 18Z) and at hourly forecast lead times, from 00 to 84 hours.

# paths./collections/prognos-rdps-realtime/items/{featureId}.options.summary
Options for PROGNOS - Regional Deterministic Prediction System (RDPS) statistically post-processed by PROGNOS-MLR item by id

# paths./collections/prognos-rdps-realtime/queryables.get.description
Statistical post-processing of forecasts from numerical weather and environmental models, including the Regional Deterministic Prediction System (RDPS), helps reduce systematic biases and error variance in raw forecasts. This is achieved by establishing optimized statistical relationships between observations recorded at stations and numerical model outputs at nearby grid points. The PROGNOS system is a software package developed by Environment and Climate Change Canada that enables this. Relationships are built using the 'Model Output Statistics' (MOS) method and multiple linear regression (MLR). The statistically post-processed weather and environmental variables include air and dew point temperatures at 1.5 meters above the Earth's surface, as well as wind speed and direction at 10 meters above the Earth's surface or at anemometer level in the case of a buoy. The absence of statistically post-processed forecasts can be due to the unavailability of a statistical model caused by insufficient observation quality or quantity. Furthermore, the absence of wind direction forecasts can also be due to predicted wind components being too weak to provide a reliable result. Wind direction and speed forecasts are derived from independent statistical post-processing models. The geographical region covered by this data includes the territory covered by Canadian meteorological stations and some American buoys. For the RDPS, statistically post-processed forecasts are available at the same issuance frequency as the raw forecasts produced by numerical models (e.g., 00Z, 06Z, 12Z, 18Z) and at hourly forecast lead times, from 00 to 84 hours.

# paths./collections/prognos-rdps-realtime/queryables.get.summary
Get PROGNOS - Regional Deterministic Prediction System (RDPS) statistically post-processed by PROGNOS-MLR queryables

# paths./collections/prognos-rdps-realtime/schema.get.description
Statistical post-processing of forecasts from numerical weather and environmental models, including the Regional Deterministic Prediction System (RDPS), helps reduce systematic biases and error variance in raw forecasts. This is achieved by establishing optimized statistical relationships between observations recorded at stations and numerical model outputs at nearby grid points. The PROGNOS system is a software package developed by Environment and Climate Change Canada that enables this. Relationships are built using the 'Model Output Statistics' (MOS) method and multiple linear regression (MLR). The statistically post-processed weather and environmental variables include air and dew point temperatures at 1.5 meters above the Earth's surface, as well as wind speed and direction at 10 meters above the Earth's surface or at anemometer level in the case of a buoy. The absence of statistically post-processed forecasts can be due to the unavailability of a statistical model caused by insufficient observation quality or quantity. Furthermore, the absence of wind direction forecasts can also be due to predicted wind components being too weak to provide a reliable result. Wind direction and speed forecasts are derived from independent statistical post-processing models. The geographical region covered by this data includes the territory covered by Canadian meteorological stations and some American buoys. For the RDPS, statistically post-processed forecasts are available at the same issuance frequency as the raw forecasts produced by numerical models (e.g., 00Z, 06Z, 12Z, 18Z) and at hourly forecast lead times, from 00 to 84 hours.

# paths./collections/prognos-rdps-realtime/schema.get.summary
Get PROGNOS - Regional Deterministic Prediction System (RDPS) statistically post-processed by PROGNOS-MLR schema

# paths./collections/public-standard-forecast-zones.get.description
The Public Standard Forecast Zones layer is a collection of public program forecast location zone polygons that represents bounded measurable locations at the Public program Standard level. The public program standard level is used in most forecasts, warnings, watches, advisories and special weather statement. With the exception of the Manitoba Lakes, the layer is made up of mostly the land kind of polygons. The layer is made up of four depictions, cartographic detailed (with proper shorelines and high resolution polygons), cartographic coarse (with generalized shorelines and low resolution polygons), digital exaggerated (with exaggerated shorelines and low resolution polygons) and the hybrid (mix of cartographic detail and exaggerated). The digital layer (exaggerated) is a collection of polygons where shoreline boundaries are stretched offshore for the land polygons and inland for the water polygons. In this "Exaggerated" layer, the boundaries are made smooth to reduce the number of polygon vertices. Boundaries of the forecast regions of this hybrid set are derived from the polygon boundaries of both the exaggerated digital, and the cartographic detailed set. Polygon boundaries along the shoreline of this set follow the exaggerated boundaries while the inland boundaries follow the detailed lines. The Public Standard Forecast Zones layer is presented in the polygon package in two coordinate systems, "Projected" (Lambert Conformal Conic) and "Unprojected" (Geographical Coordinated System).

# paths./collections/public-standard-forecast-zones.get.summary
Get Standard Forecast Zones - Public (Hybrid) metadata

# paths./collections/public-standard-forecast-zones/items.get.description
The Public Standard Forecast Zones layer is a collection of public program forecast location zone polygons that represents bounded measurable locations at the Public program Standard level. The public program standard level is used in most forecasts, warnings, watches, advisories and special weather statement. With the exception of the Manitoba Lakes, the layer is made up of mostly the land kind of polygons. The layer is made up of four depictions, cartographic detailed (with proper shorelines and high resolution polygons), cartographic coarse (with generalized shorelines and low resolution polygons), digital exaggerated (with exaggerated shorelines and low resolution polygons) and the hybrid (mix of cartographic detail and exaggerated). The digital layer (exaggerated) is a collection of polygons where shoreline boundaries are stretched offshore for the land polygons and inland for the water polygons. In this "Exaggerated" layer, the boundaries are made smooth to reduce the number of polygon vertices. Boundaries of the forecast regions of this hybrid set are derived from the polygon boundaries of both the exaggerated digital, and the cartographic detailed set. Polygon boundaries along the shoreline of this set follow the exaggerated boundaries while the inland boundaries follow the detailed lines. The Public Standard Forecast Zones layer is presented in the polygon package in two coordinate systems, "Projected" (Lambert Conformal Conic) and "Unprojected" (Geographical Coordinated System).

# paths./collections/public-standard-forecast-zones/items.get.parameters[].description
The properties that should be included for each feature. The parameter value is a comma-separated list of property names.

# paths./collections/public-standard-forecast-zones/items.get.summary
Get Standard Forecast Zones - Public (Hybrid) items

# paths./collections/public-standard-forecast-zones/items.options.description
The Public Standard Forecast Zones layer is a collection of public program forecast location zone polygons that represents bounded measurable locations at the Public program Standard level. The public program standard level is used in most forecasts, warnings, watches, advisories and special weather statement. With the exception of the Manitoba Lakes, the layer is made up of mostly the land kind of polygons. The layer is made up of four depictions, cartographic detailed (with proper shorelines and high resolution polygons), cartographic coarse (with generalized shorelines and low resolution polygons), digital exaggerated (with exaggerated shorelines and low resolution polygons) and the hybrid (mix of cartographic detail and exaggerated). The digital layer (exaggerated) is a collection of polygons where shoreline boundaries are stretched offshore for the land polygons and inland for the water polygons. In this "Exaggerated" layer, the boundaries are made smooth to reduce the number of polygon vertices. Boundaries of the forecast regions of this hybrid set are derived from the polygon boundaries of both the exaggerated digital, and the cartographic detailed set. Polygon boundaries along the shoreline of this set follow the exaggerated boundaries while the inland boundaries follow the detailed lines. The Public Standard Forecast Zones layer is presented in the polygon package in two coordinate systems, "Projected" (Lambert Conformal Conic) and "Unprojected" (Geographical Coordinated System).

# paths./collections/public-standard-forecast-zones/items.options.summary
Options for Standard Forecast Zones - Public (Hybrid) items

# paths./collections/public-standard-forecast-zones/items/{featureId}.get.description
The Public Standard Forecast Zones layer is a collection of public program forecast location zone polygons that represents bounded measurable locations at the Public program Standard level. The public program standard level is used in most forecasts, warnings, watches, advisories and special weather statement. With the exception of the Manitoba Lakes, the layer is made up of mostly the land kind of polygons. The layer is made up of four depictions, cartographic detailed (with proper shorelines and high resolution polygons), cartographic coarse (with generalized shorelines and low resolution polygons), digital exaggerated (with exaggerated shorelines and low resolution polygons) and the hybrid (mix of cartographic detail and exaggerated). The digital layer (exaggerated) is a collection of polygons where shoreline boundaries are stretched offshore for the land polygons and inland for the water polygons. In this "Exaggerated" layer, the boundaries are made smooth to reduce the number of polygon vertices. Boundaries of the forecast regions of this hybrid set are derived from the polygon boundaries of both the exaggerated digital, and the cartographic detailed set. Polygon boundaries along the shoreline of this set follow the exaggerated boundaries while the inland boundaries follow the detailed lines. The Public Standard Forecast Zones layer is presented in the polygon package in two coordinate systems, "Projected" (Lambert Conformal Conic) and "Unprojected" (Geographical Coordinated System).

# paths./collections/public-standard-forecast-zones/items/{featureId}.get.summary
Get Standard Forecast Zones - Public (Hybrid) item by id

# paths./collections/public-standard-forecast-zones/items/{featureId}.options.description
The Public Standard Forecast Zones layer is a collection of public program forecast location zone polygons that represents bounded measurable locations at the Public program Standard level. The public program standard level is used in most forecasts, warnings, watches, advisories and special weather statement. With the exception of the Manitoba Lakes, the layer is made up of mostly the land kind of polygons. The layer is made up of four depictions, cartographic detailed (with proper shorelines and high resolution polygons), cartographic coarse (with generalized shorelines and low resolution polygons), digital exaggerated (with exaggerated shorelines and low resolution polygons) and the hybrid (mix of cartographic detail and exaggerated). The digital layer (exaggerated) is a collection of polygons where shoreline boundaries are stretched offshore for the land polygons and inland for the water polygons. In this "Exaggerated" layer, the boundaries are made smooth to reduce the number of polygon vertices. Boundaries of the forecast regions of this hybrid set are derived from the polygon boundaries of both the exaggerated digital, and the cartographic detailed set. Polygon boundaries along the shoreline of this set follow the exaggerated boundaries while the inland boundaries follow the detailed lines. The Public Standard Forecast Zones layer is presented in the polygon package in two coordinate systems, "Projected" (Lambert Conformal Conic) and "Unprojected" (Geographical Coordinated System).

# paths./collections/public-standard-forecast-zones/items/{featureId}.options.summary
Options for Standard Forecast Zones - Public (Hybrid) item by id

# paths./collections/public-standard-forecast-zones/queryables.get.description
The Public Standard Forecast Zones layer is a collection of public program forecast location zone polygons that represents bounded measurable locations at the Public program Standard level. The public program standard level is used in most forecasts, warnings, watches, advisories and special weather statement. With the exception of the Manitoba Lakes, the layer is made up of mostly the land kind of polygons. The layer is made up of four depictions, cartographic detailed (with proper shorelines and high resolution polygons), cartographic coarse (with generalized shorelines and low resolution polygons), digital exaggerated (with exaggerated shorelines and low resolution polygons) and the hybrid (mix of cartographic detail and exaggerated). The digital layer (exaggerated) is a collection of polygons where shoreline boundaries are stretched offshore for the land polygons and inland for the water polygons. In this "Exaggerated" layer, the boundaries are made smooth to reduce the number of polygon vertices. Boundaries of the forecast regions of this hybrid set are derived from the polygon boundaries of both the exaggerated digital, and the cartographic detailed set. Polygon boundaries along the shoreline of this set follow the exaggerated boundaries while the inland boundaries follow the detailed lines. The Public Standard Forecast Zones layer is presented in the polygon package in two coordinate systems, "Projected" (Lambert Conformal Conic) and "Unprojected" (Geographical Coordinated System).

# paths./collections/public-standard-forecast-zones/queryables.get.summary
Get Standard Forecast Zones - Public (Hybrid) queryables

# paths./collections/public-standard-forecast-zones/schema.get.description
The Public Standard Forecast Zones layer is a collection of public program forecast location zone polygons that represents bounded measurable locations at the Public program Standard level. The public program standard level is used in most forecasts, warnings, watches, advisories and special weather statement. With the exception of the Manitoba Lakes, the layer is made up of mostly the land kind of polygons. The layer is made up of four depictions, cartographic detailed (with proper shorelines and high resolution polygons), cartographic coarse (with generalized shorelines and low resolution polygons), digital exaggerated (with exaggerated shorelines and low resolution polygons) and the hybrid (mix of cartographic detail and exaggerated). The digital layer (exaggerated) is a collection of polygons where shoreline boundaries are stretched offshore for the land polygons and inland for the water polygons. In this "Exaggerated" layer, the boundaries are made smooth to reduce the number of polygon vertices. Boundaries of the forecast regions of this hybrid set are derived from the polygon boundaries of both the exaggerated digital, and the cartographic detailed set. Polygon boundaries along the shoreline of this set follow the exaggerated boundaries while the inland boundaries follow the detailed lines. The Public Standard Forecast Zones layer is presented in the polygon package in two coordinate systems, "Projected" (Lambert Conformal Conic) and "Unprojected" (Geographical Coordinated System).

# paths./collections/public-standard-forecast-zones/schema.get.summary
Get Standard Forecast Zones - Public (Hybrid) schema

# paths./collections/swob-marine-stations.get.description
Surface Weather Observations (SWOB) measured at the automatic and manual stations of the Environment and Climate Change Canada and partners networks, either for a single station, or for the stations of specific provinces and territories (last 30 days).

# paths./collections/swob-marine-stations/items.get.description
Surface Weather Observations (SWOB) measured at the automatic and manual stations of the Environment and Climate Change Canada and partners networks, either for a single station, or for the stations of specific provinces and territories (last 30 days).

# paths./collections/swob-marine-stations/items.get.parameters[].description
The properties that should be included for each feature. The parameter value is a comma-separated list of property names.

# paths./collections/swob-marine-stations/items.options.description
Surface Weather Observations (SWOB) measured at the automatic and manual stations of the Environment and Climate Change Canada and partners networks, either for a single station, or for the stations of specific provinces and territories (last 30 days).

# paths./collections/swob-marine-stations/items.options.summary
Options for SWOB - Stations for Marine items

# paths./collections/swob-marine-stations/items/{featureId}.get.description
Surface Weather Observations (SWOB) measured at the automatic and manual stations of the Environment and Climate Change Canada and partners networks, either for a single station, or for the stations of specific provinces and territories (last 30 days).

# paths./collections/swob-marine-stations/items/{featureId}.get.summary
Get SWOB - Stations for Marine item by id

# paths./collections/swob-marine-stations/items/{featureId}.options.description
Surface Weather Observations (SWOB) measured at the automatic and manual stations of the Environment and Climate Change Canada and partners networks, either for a single station, or for the stations of specific provinces and territories (last 30 days).

# paths./collections/swob-marine-stations/items/{featureId}.options.summary
Options for SWOB - Stations for Marine item by id

# paths./collections/swob-marine-stations/queryables.get.description
Surface Weather Observations (SWOB) measured at the automatic and manual stations of the Environment and Climate Change Canada and partners networks, either for a single station, or for the stations of specific provinces and territories (last 30 days).

# paths./collections/swob-marine-stations/queryables.get.summary
Get SWOB - Stations for Marine queryables

# paths./collections/swob-marine-stations/schema.get.description
Surface Weather Observations (SWOB) measured at the automatic and manual stations of the Environment and Climate Change Canada and partners networks, either for a single station, or for the stations of specific provinces and territories (last 30 days).

# paths./collections/swob-partner-stations.get.description
Surface Weather Observations (SWOB) measured at the automatic and manual stations of the Environment and Climate Change Canada and partners networks, either for a single station, or for the stations of specific provinces and territories (last 30 days).

# paths./collections/swob-partner-stations.get.summary
Get SWOB - Stations from Partners metadata

# paths./collections/swob-partner-stations/items.get.description
Surface Weather Observations (SWOB) measured at the automatic and manual stations of the Environment and Climate Change Canada and partners networks, either for a single station, or for the stations of specific provinces and territories (last 30 days).

# paths./collections/swob-partner-stations/items.get.parameters[].description
The properties that should be included for each feature. The parameter value is a comma-separated list of property names.

# paths./collections/swob-partner-stations/items.options.description
Surface Weather Observations (SWOB) measured at the automatic and manual stations of the Environment and Climate Change Canada and partners networks, either for a single station, or for the stations of specific provinces and territories (last 30 days).

# paths./collections/swob-partner-stations/items.options.summary
Options for SWOB - Stations from Partners items

# paths./collections/swob-partner-stations/items/{featureId}.get.description
Surface Weather Observations (SWOB) measured at the automatic and manual stations of the Environment and Climate Change Canada and partners networks, either for a single station, or for the stations of specific provinces and territories (last 30 days).

# paths./collections/swob-partner-stations/items/{featureId}.get.summary
Get SWOB - Stations from Partners item by id

# paths./collections/swob-partner-stations/items/{featureId}.options.description
Surface Weather Observations (SWOB) measured at the automatic and manual stations of the Environment and Climate Change Canada and partners networks, either for a single station, or for the stations of specific provinces and territories (last 30 days).

# paths./collections/swob-partner-stations/items/{featureId}.options.summary
Options for SWOB - Stations from Partners item by id

# paths./collections/swob-partner-stations/queryables.get.description
Surface Weather Observations (SWOB) measured at the automatic and manual stations of the Environment and Climate Change Canada and partners networks, either for a single station, or for the stations of specific provinces and territories (last 30 days).

# paths./collections/swob-partner-stations/queryables.get.summary
Get SWOB - Stations from Partners queryables

# paths./collections/swob-partner-stations/schema.get.description
Surface Weather Observations (SWOB) measured at the automatic and manual stations of the Environment and Climate Change Canada and partners networks, either for a single station, or for the stations of specific provinces and territories (last 30 days).

# paths./collections/swob-partner-stations/schema.get.summary
Get SWOB - Stations from Partners schema

# paths./collections/swob-realtime.get.description
Surface Weather Observations (SWOB) measured at the automatic and manual stations of the Environment and Climate Change Canada and partners networks, either for a single station, or for the stations of specific provinces and territories (last 30 days).

# paths./collections/swob-realtime/items.get.description
Surface Weather Observations (SWOB) measured at the automatic and manual stations of the Environment and Climate Change Canada and partners networks, either for a single station, or for the stations of specific provinces and territories (last 30 days).

# paths./collections/swob-realtime/items.get.parameters[].description
The properties that should be included for each feature. The parameter value is a comma-separated list of property names.

# paths./collections/swob-realtime/items.options.description
Surface Weather Observations (SWOB) measured at the automatic and manual stations of the Environment and Climate Change Canada and partners networks, either for a single station, or for the stations of specific provinces and territories (last 30 days).

# paths./collections/swob-realtime/items/{featureId}.get.description
Surface Weather Observations (SWOB) measured at the automatic and manual stations of the Environment and Climate Change Canada and partners networks, either for a single station, or for the stations of specific provinces and territories (last 30 days).

# paths./collections/swob-realtime/items/{featureId}.options.description
Surface Weather Observations (SWOB) measured at the automatic and manual stations of the Environment and Climate Change Canada and partners networks, either for a single station, or for the stations of specific provinces and territories (last 30 days).

# paths./collections/swob-realtime/items/{featureId}.options.summary
Options for SWOB - Real-Time Data item by id

# paths./collections/swob-realtime/queryables.get.description
Surface Weather Observations (SWOB) measured at the automatic and manual stations of the Environment and Climate Change Canada and partners networks, either for a single station, or for the stations of specific provinces and territories (last 30 days).

# paths./collections/swob-realtime/schema.get.description
Surface Weather Observations (SWOB) measured at the automatic and manual stations of the Environment and Climate Change Canada and partners networks, either for a single station, or for the stations of specific provinces and territories (last 30 days).

# paths./collections/swob-stations.get.description
Surface Weather Observations (SWOB) measured at the automatic and manual stations of the Environment and Climate Change Canada and partners networks, either for a single station, or for the stations of specific provinces and territories (last 30 days).

# paths./collections/swob-stations/items.get.description
Surface Weather Observations (SWOB) measured at the automatic and manual stations of the Environment and Climate Change Canada and partners networks, either for a single station, or for the stations of specific provinces and territories (last 30 days).

# paths./collections/swob-stations/items.get.parameters[].description
The properties that should be included for each feature. The parameter value is a comma-separated list of property names.

# paths./collections/swob-stations/items.options.description
Surface Weather Observations (SWOB) measured at the automatic and manual stations of the Environment and Climate Change Canada and partners networks, either for a single station, or for the stations of specific provinces and territories (last 30 days).

# paths./collections/swob-stations/items/{featureId}.get.description
Surface Weather Observations (SWOB) measured at the automatic and manual stations of the Environment and Climate Change Canada and partners networks, either for a single station, or for the stations of specific provinces and territories (last 30 days).

# paths./collections/swob-stations/items/{featureId}.options.description
Surface Weather Observations (SWOB) measured at the automatic and manual stations of the Environment and Climate Change Canada and partners networks, either for a single station, or for the stations of specific provinces and territories (last 30 days).

# paths./collections/swob-stations/queryables.get.description
Surface Weather Observations (SWOB) measured at the automatic and manual stations of the Environment and Climate Change Canada and partners networks, either for a single station, or for the stations of specific provinces and territories (last 30 days).

# paths./collections/swob-stations/schema.get.description
Surface Weather Observations (SWOB) measured at the automatic and manual stations of the Environment and Climate Change Canada and partners networks, either for a single station, or for the stations of specific provinces and territories (last 30 days).

# paths./collections/thunderstorm_outlook.get.description
The Thunderstorm Outlook is a graphical or GeoJSON forecast product, which depicts the expected geographic areas of thunderstorms in Canada. Thunderstorm Outlooks are issued by storm prediction centres across Canada during the convective season (May to October) each year. Both the graphical and GeoJSON products use the same source data that forecasters enter through the Ninjo software. Thunderstorm Outlooks are issued once per day at around 12:00 pm regional local time. Since each office has their own standards of procedure, the forecasters are not required to send it at 12:00 pm. Nonetheless, the timestamp in the data is preconfigured to say it is issued at 12:00 pm in the GeoJSON product. Subsequent amendments to existing Thunderstorm Outlooks are issued on an as-needed basis during the day. Each amendment generates a new GeoJSON file and graphical product. The Thunderstorm Outlook may not be updated with active severe weather alerts. Users are asked to check with the latest watches and warnings for the most current conditions.

# paths./collections/thunderstorm_outlook.get.summary
Get Thunderstorm Outlook [experimental] metadata

# paths./collections/thunderstorm_outlook/items.get.description
The Thunderstorm Outlook is a graphical or GeoJSON forecast product, which depicts the expected geographic areas of thunderstorms in Canada. Thunderstorm Outlooks are issued by storm prediction centres across Canada during the convective season (May to October) each year. Both the graphical and GeoJSON products use the same source data that forecasters enter through the Ninjo software. Thunderstorm Outlooks are issued once per day at around 12:00 pm regional local time. Since each office has their own standards of procedure, the forecasters are not required to send it at 12:00 pm. Nonetheless, the timestamp in the data is preconfigured to say it is issued at 12:00 pm in the GeoJSON product. Subsequent amendments to existing Thunderstorm Outlooks are issued on an as-needed basis during the day. Each amendment generates a new GeoJSON file and graphical product. The Thunderstorm Outlook may not be updated with active severe weather alerts. Users are asked to check with the latest watches and warnings for the most current conditions.

# paths./collections/thunderstorm_outlook/items.get.parameters[].description
The properties that should be included for each feature. The parameter value is a comma-separated list of property names.

# paths./collections/thunderstorm_outlook/items.get.summary
Get Thunderstorm Outlook [experimental] items

# paths./collections/thunderstorm_outlook/items.options.description
The Thunderstorm Outlook is a graphical or GeoJSON forecast product, which depicts the expected geographic areas of thunderstorms in Canada. Thunderstorm Outlooks are issued by storm prediction centres across Canada during the convective season (May to October) each year. Both the graphical and GeoJSON products use the same source data that forecasters enter through the Ninjo software. Thunderstorm Outlooks are issued once per day at around 12:00 pm regional local time. Since each office has their own standards of procedure, the forecasters are not required to send it at 12:00 pm. Nonetheless, the timestamp in the data is preconfigured to say it is issued at 12:00 pm in the GeoJSON product. Subsequent amendments to existing Thunderstorm Outlooks are issued on an as-needed basis during the day. Each amendment generates a new GeoJSON file and graphical product. The Thunderstorm Outlook may not be updated with active severe weather alerts. Users are asked to check with the latest watches and warnings for the most current conditions.

# paths./collections/thunderstorm_outlook/items.options.summary
Options for Thunderstorm Outlook [experimental] items

# paths./collections/thunderstorm_outlook/items/{featureId}.get.description
The Thunderstorm Outlook is a graphical or GeoJSON forecast product, which depicts the expected geographic areas of thunderstorms in Canada. Thunderstorm Outlooks are issued by storm prediction centres across Canada during the convective season (May to October) each year. Both the graphical and GeoJSON products use the same source data that forecasters enter through the Ninjo software. Thunderstorm Outlooks are issued once per day at around 12:00 pm regional local time. Since each office has their own standards of procedure, the forecasters are not required to send it at 12:00 pm. Nonetheless, the timestamp in the data is preconfigured to say it is issued at 12:00 pm in the GeoJSON product. Subsequent amendments to existing Thunderstorm Outlooks are issued on an as-needed basis during the day. Each amendment generates a new GeoJSON file and graphical product. The Thunderstorm Outlook may not be updated with active severe weather alerts. Users are asked to check with the latest watches and warnings for the most current conditions.

# paths./collections/thunderstorm_outlook/items/{featureId}.get.summary
Get Thunderstorm Outlook [experimental] item by id

# paths./collections/thunderstorm_outlook/items/{featureId}.options.description
The Thunderstorm Outlook is a graphical or GeoJSON forecast product, which depicts the expected geographic areas of thunderstorms in Canada. Thunderstorm Outlooks are issued by storm prediction centres across Canada during the convective season (May to October) each year. Both the graphical and GeoJSON products use the same source data that forecasters enter through the Ninjo software. Thunderstorm Outlooks are issued once per day at around 12:00 pm regional local time. Since each office has their own standards of procedure, the forecasters are not required to send it at 12:00 pm. Nonetheless, the timestamp in the data is preconfigured to say it is issued at 12:00 pm in the GeoJSON product. Subsequent amendments to existing Thunderstorm Outlooks are issued on an as-needed basis during the day. Each amendment generates a new GeoJSON file and graphical product. The Thunderstorm Outlook may not be updated with active severe weather alerts. Users are asked to check with the latest watches and warnings for the most current conditions.

# paths./collections/thunderstorm_outlook/items/{featureId}.options.summary
Options for Thunderstorm Outlook [experimental] item by id

# paths./collections/thunderstorm_outlook/queryables.get.description
The Thunderstorm Outlook is a graphical or GeoJSON forecast product, which depicts the expected geographic areas of thunderstorms in Canada. Thunderstorm Outlooks are issued by storm prediction centres across Canada during the convective season (May to October) each year. Both the graphical and GeoJSON products use the same source data that forecasters enter through the Ninjo software. Thunderstorm Outlooks are issued once per day at around 12:00 pm regional local time. Since each office has their own standards of procedure, the forecasters are not required to send it at 12:00 pm. Nonetheless, the timestamp in the data is preconfigured to say it is issued at 12:00 pm in the GeoJSON product. Subsequent amendments to existing Thunderstorm Outlooks are issued on an as-needed basis during the day. Each amendment generates a new GeoJSON file and graphical product. The Thunderstorm Outlook may not be updated with active severe weather alerts. Users are asked to check with the latest watches and warnings for the most current conditions.

# paths./collections/thunderstorm_outlook/schema.get.description
The Thunderstorm Outlook is a graphical or GeoJSON forecast product, which depicts the expected geographic areas of thunderstorms in Canada. Thunderstorm Outlooks are issued by storm prediction centres across Canada during the convective season (May to October) each year. Both the graphical and GeoJSON products use the same source data that forecasters enter through the Ninjo software. Thunderstorm Outlooks are issued once per day at around 12:00 pm regional local time. Since each office has their own standards of procedure, the forecasters are not required to send it at 12:00 pm. Nonetheless, the timestamp in the data is preconfigured to say it is issued at 12:00 pm in the GeoJSON product. Subsequent amendments to existing Thunderstorm Outlooks are issued on an as-needed basis during the day. Each amendment generates a new GeoJSON file and graphical product. The Thunderstorm Outlook may not be updated with active severe weather alerts. Users are asked to check with the latest watches and warnings for the most current conditions.

# paths./collections/thunderstorm_outlook/schema.get.summary
Get Thunderstorm Outlook [experimental] schema

# paths./collections/weather-alerts.get.description
Environment Canada issues weather alerts about weather related hazards in order to notify those in affected areas so that they can take steps to protect themselves and their property from harm. Alerts are classified depending on the severity and timing of the subject event and include: warnings, watches, advisories and statements. Warnings are usually issued six to 24 hours in advance, although some severe weather (such as thunderstorms and tornadoes) can occur rapidly, with less than a half hours' notice.

# paths./collections/weather-alerts/items.get.description
Environment Canada issues weather alerts about weather related hazards in order to notify those in affected areas so that they can take steps to protect themselves and their property from harm. Alerts are classified depending on the severity and timing of the subject event and include: warnings, watches, advisories and statements. Warnings are usually issued six to 24 hours in advance, although some severe weather (such as thunderstorms and tornadoes) can occur rapidly, with less than a half hours' notice.

# paths./collections/weather-alerts/items.get.parameters[].description
The properties that should be included for each feature. The parameter value is a comma-separated list of property names.

# paths./collections/weather-alerts/items.options.description
Environment Canada issues weather alerts about weather related hazards in order to notify those in affected areas so that they can take steps to protect themselves and their property from harm. Alerts are classified depending on the severity and timing of the subject event and include: warnings, watches, advisories and statements. Warnings are usually issued six to 24 hours in advance, although some severe weather (such as thunderstorms and tornadoes) can occur rapidly, with less than a half hours' notice.

# paths./collections/weather-alerts/items/{featureId}.get.description
Environment Canada issues weather alerts about weather related hazards in order to notify those in affected areas so that they can take steps to protect themselves and their property from harm. Alerts are classified depending on the severity and timing of the subject event and include: warnings, watches, advisories and statements. Warnings are usually issued six to 24 hours in advance, although some severe weather (such as thunderstorms and tornadoes) can occur rapidly, with less than a half hours' notice.

# paths./collections/weather-alerts/items/{featureId}.options.description
Environment Canada issues weather alerts about weather related hazards in order to notify those in affected areas so that they can take steps to protect themselves and their property from harm. Alerts are classified depending on the severity and timing of the subject event and include: warnings, watches, advisories and statements. Warnings are usually issued six to 24 hours in advance, although some severe weather (such as thunderstorms and tornadoes) can occur rapidly, with less than a half hours' notice.

# paths./collections/weather-alerts/queryables.get.description
Environment Canada issues weather alerts about weather related hazards in order to notify those in affected areas so that they can take steps to protect themselves and their property from harm. Alerts are classified depending on the severity and timing of the subject event and include: warnings, watches, advisories and statements. Warnings are usually issued six to 24 hours in advance, although some severe weather (such as thunderstorms and tornadoes) can occur rapidly, with less than a half hours' notice.

# paths./collections/weather-alerts/schema.get.description
Environment Canada issues weather alerts about weather related hazards in order to notify those in affected areas so that they can take steps to protect themselves and their property from harm. Alerts are classified depending on the severity and timing of the subject event and include: warnings, watches, advisories and statements. Warnings are usually issued six to 24 hours in advance, although some severe weather (such as thunderstorms and tornadoes) can occur rapidly, with less than a half hours' notice.

# paths./collections/weather:cansips:100km:forecast:members.get.description
The Canadian Seasonal to Inter-annual Prediction System (CanSIPS) carries out physics calculations to arrive at probabilistic predictions of atmospheric elements from the beginning of a month out to up to 12 months into the future, resulting in seasonal forecasts. Atmospheric elements include temperature, precipitation, wind speed and direction and others. This product contains raw numerical results of these calculations. Geographical coverage is global. Data is available on a grid at a horizontal resolution of 1 degree and for a few selected vertical levels.

# paths./collections/weather:cansips:100km:forecast:members.get.summary
Get CanSIPS - Members at 100 km metadata

# paths./collections/weather:cansips:100km:forecast:members/coverage.get.description
The Canadian Seasonal to Inter-annual Prediction System (CanSIPS) carries out physics calculations to arrive at probabilistic predictions of atmospheric elements from the beginning of a month out to up to 12 months into the future, resulting in seasonal forecasts. Atmospheric elements include temperature, precipitation, wind speed and direction and others. This product contains raw numerical results of these calculations. Geographical coverage is global. Data is available on a grid at a horizontal resolution of 1 degree and for a few selected vertical levels.

# paths./collections/weather:cansips:100km:forecast:members/coverage.get.summary
Get CanSIPS - Members at 100 km coverage

# paths./collections/weather:cansips:100km:forecast:monthly-products.get.description
The Canadian Seasonal to Inter-annual Prediction System (CanSIPS) carries out physics calculations to arrive at probabilistic predictions of atmospheric elements from the beginning of a month out to up to 12 months into the future, resulting in seasonal forecasts. Atmospheric elements include temperature, precipitation, wind speed and direction and others. This product contains raw numerical results of these calculations. Geographical coverage is global. Data is available on a grid at a horizontal resolution of 2.5 degrees and 1 degree and for a few selected vertical levels. In addition, forecast probabilities for below, near, and above normal temperature and precipitation are available at both resolutions. Predictions and corresponding hindcast are made available monthly.

# paths./collections/weather:cansips:100km:forecast:monthly-products.get.summary
Get CanSIPS - Monthly forecast probabilities for temperatures and precipitations at 100km resolution metadata

# paths./collections/weather:cansips:100km:forecast:monthly-products/cov.get.description
The Canadian Seasonal to Inter-annual Prediction System (CanSIPS) carries out physics calculations to arrive at probabilistic predictions of atmospheric elements from the beginning of a month out to up to 12 months into the future, resulting in seasonal forecasts. Atmospheric elements include temperature, precipitation, wind speed and direction and others. This product contains raw numerical results of these calculations. Geographical coverage is global. Data is available on a grid at a horizontal resolution of 2.5 degrees and 1 degree and for a few selected vertical levels. In addition, forecast probabilities for below, near, and above normal temperature and precipitation are available at both resolutions. Predictions and corresponding hindcast are made available monthly.

# paths./collections/weather:cansips:100km:forecast:monthly-products/cov.get.summary
Get CanSIPS - Monthly forecast probabilities for temperatures and precipitations at 100km resolution coverage

# paths./collections/weather:cansips:100km:forecast:seasonal-products.get.description
The Canadian Seasonal to Inter-annual Prediction System (CanSIPS) carries out physics calculations to arrive at probabilistic predictions of atmospheric elements from the beginning of a month out to up to 12 months into the future, resulting in seasonal forecasts. Atmospheric elements include temperature, precipitation, wind speed and direction and others. This product contains raw numerical results of these calculations. Geographical coverage is global. Data is available on a grid at a horizontal resolution of 2.5 degrees and 1 degree and for a few selected vertical levels. In addition, forecast probabilities for below, near, and above normal temperature and precipitation are available at both resolutions. Predictions and corresponding hindcast are made available monthly.

# paths./collections/weather:cansips:100km:forecast:seasonal-products.get.summary
Get CanSIPS - Seasonal forecast probabilities for temperatures and precipitations at 100km resolution metadata

# paths./collections/weather:cansips:100km:forecast:seasonal-products/co.get.description
The Canadian Seasonal to Inter-annual Prediction System (CanSIPS) carries out physics calculations to arrive at probabilistic predictions of atmospheric elements from the beginning of a month out to up to 12 months into the future, resulting in seasonal forecasts. Atmospheric elements include temperature, precipitation, wind speed and direction and others. This product contains raw numerical results of these calculations. Geographical coverage is global. Data is available on a grid at a horizontal resolution of 2.5 degrees and 1 degree and for a few selected vertical levels. In addition, forecast probabilities for below, near, and above normal temperature and precipitation are available at both resolutions. Predictions and corresponding hindcast are made available monthly.

# paths./collections/weather:cansips:100km:forecast:seasonal-products/co.get.summary
Get CanSIPS - Seasonal forecast probabilities for temperatures and precipitations at 100km resolution coverage

# paths./collections/weather:cansips:250km:forecast:members.get.description
The Canadian Seasonal to Inter-annual Prediction System (CanSIPS) carries out physics calculations to arrive at probabilistic predictions of atmospheric elements from the beginning of a month out to up to 12 months into the future, resulting in seasonal forecasts. Atmospheric elements include temperature, precipitation, wind speed and direction and others. This product contains raw numerical results of these calculations. Geographical coverage is global. Data is available on a grid at a horizontal resolution of 2.5 degrees and for a few selected vertical levels.

# paths./collections/weather:cansips:250km:forecast:members.get.summary
Get CanSIPS - Members at 250 km metadata

# paths./collections/weather:cansips:250km:forecast:members/coverage.get.description
The Canadian Seasonal to Inter-annual Prediction System (CanSIPS) carries out physics calculations to arrive at probabilistic predictions of atmospheric elements from the beginning of a month out to up to 12 months into the future, resulting in seasonal forecasts. Atmospheric elements include temperature, precipitation, wind speed and direction and others. This product contains raw numerical results of these calculations. Geographical coverage is global. Data is available on a grid at a horizontal resolution of 2.5 degrees and for a few selected vertical levels.

# paths./collections/weather:cansips:250km:forecast:members/coverage.get.summary
Get CanSIPS - Members at 250 km coverage

# paths./collections/weather:rdpa:10km:24f.get.description
The Regional Deterministic Precipitation Analysis (RDPA) produces a best estimate of the amount of precipitation that occurred over recent past periods of 24 hours. The estimate integrates data from in situ precipitation gauge measurements, weather radar and numerical weather prediction models. Geographic coverage is North America (Canada, United States and Mexico). Data is available at horizontal resolution of 10 km. Data is only available for the surface level. Analysis data is made available once a day for the 24h intervals. A preliminary estimate is available approximately 1h after the end of the accumulation period, and revised 6h after in order to assimilate gauge data arriving later.

# paths./collections/weather:rdpa:10km:24f.get.summary
Get RDPA - 24 Hours Accumulation at 10 km Resolution metadata

# paths./collections/weather:rdpa:10km:24f/coverage.get.description
The Regional Deterministic Precipitation Analysis (RDPA) produces a best estimate of the amount of precipitation that occurred over recent past periods of 24 hours. The estimate integrates data from in situ precipitation gauge measurements, weather radar and numerical weather prediction models. Geographic coverage is North America (Canada, United States and Mexico). Data is available at horizontal resolution of 10 km. Data is only available for the surface level. Analysis data is made available once a day for the 24h intervals. A preliminary estimate is available approximately 1h after the end of the accumulation period, and revised 6h after in order to assimilate gauge data arriving later.

# paths./collections/weather:rdpa:10km:24f/coverage.get.summary
Get RDPA - 24 Hours Accumulation at 10 km Resolution coverage

# paths./collections/weather:rdpa:10km:24p.get.description
The Regional Deterministic Precipitation Analysis (RDPA) produces a best estimate of the amount of precipitation that occurred over recent past periods of 24 hours. The estimate integrates data from in situ precipitation gauge measurements, weather radar and numerical weather prediction models. Geographic coverage is North America (Canada, United States and Mexico). Data is available at horizontal resolution of 10 km. Data is only available for the surface level. Analysis data is made available once a day for the 24h intervals. The preliminary estimate is available approximately 1h after the end of the accumulation period.

# paths./collections/weather:rdpa:10km:24p.get.summary
Get RDPA - 24 Hours Accumulation at 10 km Resolution (Preliminary) metadata

# paths./collections/weather:rdpa:10km:24p/coverage.get.description
The Regional Deterministic Precipitation Analysis (RDPA) produces a best estimate of the amount of precipitation that occurred over recent past periods of 24 hours. The estimate integrates data from in situ precipitation gauge measurements, weather radar and numerical weather prediction models. Geographic coverage is North America (Canada, United States and Mexico). Data is available at horizontal resolution of 10 km. Data is only available for the surface level. Analysis data is made available once a day for the 24h intervals. The preliminary estimate is available approximately 1h after the end of the accumulation period.

# paths./collections/weather:rdpa:10km:24p/coverage.get.summary
Get RDPA - 24 Hours Accumulation at 10 km Resolution (Preliminary) coverage

# paths./collections/weather:rdpa:10km:6f.get.description
The Regional Deterministic Precipitation Analysis (RDPA) produces a best estimate of the amount of precipitation that occurred over recent past periods of 6 hours. The estimate integrates data from in situ precipitation gauge measurements, weather radar and numerical weather prediction models. Geographic coverage is North America (Canada, United States and Mexico). Data is available at horizontal resolution of 10 km. Data is only available for the surface level. Analysis data is made available four times a day for 6h intervals. A preliminary estimate is available approximately 1h after the end of the accumulation period, and revised 6h after in order to assimilate gauge data arriving later.

# paths./collections/weather:rdpa:10km:6f.get.summary
Get RDPA - 6 Hours Accumulation at 10 km Resolution metadata

# paths./collections/weather:rdpa:10km:6f/coverage.get.description
The Regional Deterministic Precipitation Analysis (RDPA) produces a best estimate of the amount of precipitation that occurred over recent past periods of 6 hours. The estimate integrates data from in situ precipitation gauge measurements, weather radar and numerical weather prediction models. Geographic coverage is North America (Canada, United States and Mexico). Data is available at horizontal resolution of 10 km. Data is only available for the surface level. Analysis data is made available four times a day for 6h intervals. A preliminary estimate is available approximately 1h after the end of the accumulation period, and revised 6h after in order to assimilate gauge data arriving later.

# paths./collections/weather:rdpa:10km:6f/coverage.get.summary
Get RDPA - 6 Hours Accumulation at 10 km Resolution coverage

# paths./collections/weather:rdpa:10km:6p.get.description
The Regional Deterministic Precipitation Analysis (RDPA) produces a best estimate of the amount of precipitation that occurred over recent past periods of 6 hours. The estimate integrates data from in situ precipitation gauge measurements, weather radar and numerical weather prediction models. Geographic coverage is North America (Canada, United States and Mexico). Data is available at horizontal resolution of 10 km. Data is only available for the surface level. Analysis data is made available four times a day for 6h intervals. The preliminary estimate is available approximately 1h after the end of the accumulation period.

# paths./collections/weather:rdpa:10km:6p.get.summary
Get RDPA - 6 Hours Accumulation at 10 km Resolution (Preliminary) metadata

# paths./collections/weather:rdpa:10km:6p/coverage.get.description
The Regional Deterministic Precipitation Analysis (RDPA) produces a best estimate of the amount of precipitation that occurred over recent past periods of 6 hours. The estimate integrates data from in situ precipitation gauge measurements, weather radar and numerical weather prediction models. Geographic coverage is North America (Canada, United States and Mexico). Data is available at horizontal resolution of 10 km. Data is only available for the surface level. Analysis data is made available four times a day for 6h intervals. The preliminary estimate is available approximately 1h after the end of the accumulation period.

# paths./collections/weather:rdpa:10km:6p/coverage.get.summary
Get RDPA - 6 Hours Accumulation at 10 km Resolution (Preliminary) coverage

# paths./collections/weather:rdpa:15km:24f.get.description
The Regional Deterministic Precipitation Analysis (RDPA) produces a best estimate of the amount of precipitation that occurred over recent past periods of 24 hours. The estimate integrates data from in situ precipitation gauge measurements, weather radar and numerical weather prediction models. Geographic coverage is North America (Canada, United States and Mexico). Data is available at horizontal resolution of 15 km. Data is only available for the surface level. Analysis data is made available once a day for the 24h intervals. A preliminary estimate is available approximately 1h after the end of the accumulation period, and revised 6h after in order to assimilate gauge data arriving later.

# paths./collections/weather:rdpa:15km:24f.get.summary
Get RDPA - 24 Hours Accumulation at 15 km Resolution metadata

# paths./collections/weather:rdpa:15km:24f/coverage.get.description
The Regional Deterministic Precipitation Analysis (RDPA) produces a best estimate of the amount of precipitation that occurred over recent past periods of 24 hours. The estimate integrates data from in situ precipitation gauge measurements, weather radar and numerical weather prediction models. Geographic coverage is North America (Canada, United States and Mexico). Data is available at horizontal resolution of 15 km. Data is only available for the surface level. Analysis data is made available once a day for the 24h intervals. A preliminary estimate is available approximately 1h after the end of the accumulation period, and revised 6h after in order to assimilate gauge data arriving later.

# paths./collections/weather:rdpa:15km:24f/coverage.get.summary
Get RDPA - 24 Hours Accumulation at 15 km Resolution coverage

# paths./collections/weather:rdpa:15km:6f.get.description
The Regional Deterministic Precipitation Analysis (RDPA) produces a best estimate of the amount of precipitation that occurred over recent past periods of 24 hours. The estimate integrates data from in situ precipitation gauge measurements, weather radar and numerical weather prediction models. Geographic coverage is North America (Canada, United States and Mexico). Data is available at horizontal resolution of 15 km. Data is only available for the surface level. Analysis data is made available four times a day for 6h intervals. A preliminary estimate is available approximately 1h after the end of the accumulation period, and revised 6h after in order to assimilate gauge data arriving later.

# paths./collections/weather:rdpa:15km:6f.get.summary
Get RDPA - 6 Hours Accumulation at 15 km Resolution metadata

# paths./collections/weather:rdpa:15km:6f/coverage.get.description
The Regional Deterministic Precipitation Analysis (RDPA) produces a best estimate of the amount of precipitation that occurred over recent past periods of 24 hours. The estimate integrates data from in situ precipitation gauge measurements, weather radar and numerical weather prediction models. Geographic coverage is North America (Canada, United States and Mexico). Data is available at horizontal resolution of 15 km. Data is only available for the surface level. Analysis data is made available four times a day for 6h intervals. A preliminary estimate is available approximately 1h after the end of the accumulation period, and revised 6h after in order to assimilate gauge data arriving later.

# paths./collections/weather:rdpa:15km:6f/coverage.get.summary
Get RDPA - 6 Hours Accumulation at 15 km Resolution coverage

# paths./processes/raster-drill/execution.post.summary
Process GeoMet-Climate Raster Drill process execution

# servers[].description
GeoMet-OGC-API provides public access to the Meteorological Service of Canada (MSC) and Environment and Climate Change Canada (ECCC) data via interoperable web services and application programming interfaces (API). Through open standards, users can freely and quickly access thousands of real-time and archived weather, climate and water datasets and products and integrate them in their domain-specific applications and decision support systems. Users can build mobile apps, create interactive web maps, and display and animate MSC data in desktop software. MSC GeoMet also enables on demand raw data clipping and reprojection, on demand format conversion and custom visualization.

# tags[].description
GeoMet-OGC-API provides public access to the Meteorological Service of Canada (MSC) and Environment and Climate Change Canada (ECCC) data via interoperable web services and application programming interfaces (API). Through open standards, users can freely and quickly access thousands of real-time and archived weather, climate and water datasets and products and integrate them in their domain-specific applications and decision support systems. Users can build mobile apps, create interactive web maps, and display and animate MSC data in desktop software. MSC GeoMet also enables on demand raw data clipping and reprojection, on demand format conversion and custom visualization.

# tags[].description
A station is a site on a river or lake where water quantity (water level and flow) are collected and recorded.

# tags[].description
The daily mean is the average of all unit values for a given day.

# tags[].description
The monthly mean is the average of daily mean values for a given month.

# tags[].description
The annual maximum and minimum daily data are the maximum and minimum daily mean values for a given year.

# tags[].description
The annual maximum and minimum instantaneous data are the maximum and minimum instantaneous values for a given year.

# tags[].description
Real-time water level and flow (discharge) data collected at over 2100 hydrometric stations across Canada (last 30 days).

# tags[].description
Climate Normals are used to summarize or describe the average climatic conditions of a particular location. At the completion of each decade, Environment and Climate Change Canada updates its climate normals for as many locations and as many climatic characteristics as possible. The climate normals offered here are based on Canadian climate stations with at least 15 years of data between 1981 to 2010.

# tags[].description
Climate observations are derived from two sources of data. The first are Daily Climate Stations producing one or two observations per day of temperature, precipitation. The second are hourly stations that typically produce more weather elements e.g. wind or snow on ground.

# tags[].description
A cross-country summary of the averages and extremes for the month, including precipitation totals, max-min temperatures, and degree days. This data is available from stations that produce daily data.

# tags[].description
Provides current conditions, forecasts, warnings and regional normals for a selection of Canadian cities.

# tags[].description
Environment Canada (EC) provides a marine forecasting service to mariners and marine interests. This enables them to make informed decisions about marine weather conditions that may impact their safety, security or operations. EC also provides marine forecasts in order to meet Canada’s treaty obligations under the International Maritime Organization’s Safety Of Life At Sea (SOLAS) Convention.

# tags[].description
Daily climate observations are derived from two sources of data. The first are Daily Climate Stations producing one or two observations per day of temperature, precipitation. The second are hourly stations that typically produce more weather elements e.g. wind or snow on ground. Only a subset of the total stations is shown due to size limitations. The criteria for station selection are listed as below. The priorities for inclusion are as follows: (1) Station is currently operational, (2) Stations with long periods of record, (3) Stations that are co-located with the categories above and supplement the period of record

# tags[].description
Canadian hourly climate data are available for public access from the ECCC/MSC's National Climate Archive. These are surface weather stations that produce hourly meteorological observations, taken each hour of the day. Only a subset of the total stations found on Environment and Climate Change Canada’s Historical Climate Data Page is shown due to size limitations.The priorities for inclusion are as follows: stations in cities with populations of 10000+, stations that are Regional Basic Climatological Network status and stations with 30+ years of data.

# tags[].description
Climate station datasets that incorporate adjustments (derived from statistical procedures) to the original historical station data to account for discontinuities from non-climatic factors, such as instrument changes or station relocation.

# tags[].description
Adjusted and Homogenized Canadian Climate Data (AHCCD) are climate station datasets that incorporate adjustments (derived from statistical procedures) to the original historical station data to account for discontinuities from non-climatic factors, such as instrument changes or station relocation. Data are provided for temperature, precipitation, pressure and wind speed. Station trend data are provided when available. Trends are calculated using the Theil-Sen method using the station's full period of available data. The availability of trends will vary by station; if more than 5 consecutive years are missing data or more than 10% of the data within the time series is missing, a trend was not calculated.

# tags[].description
Adjusted and Homogenized Canadian Climate Data (AHCCD) are climate station datasets that incorporate adjustments (derived from statistical procedures) to the original historical station data to account for discontinuities from non-climatic factors, such as instrument changes or station relocation. Data are provided for temperature, precipitation, pressure and wind speed. Station trend data are provided when available. Trends are calculated using the Theil-Sen method using the station's full period of available data. The availability of trends will vary by station; if more than 5 consecutive years are missing data or more than 10% of the data within the time series is missing, a trend was not calculated.

# tags[].description
Adjusted and Homogenized Canadian Climate Data (AHCCD) are climate station datasets that incorporate adjustments (derived from statistical procedures) to the original historical station data to account for discontinuities from non-climatic factors, such as instrument changes or station relocation. Data are provided for temperature, precipitation, pressure and wind speed. Station trend data are provided when available. Trends are calculated using the Theil-Sen method using the station's full period of available data. The availability of trends will vary by station; if more than 5 consecutive years are missing data or more than 10% of the data within the time series is missing, a trend was not calculated.

# tags[].description
Adjusted and Homogenized Canadian Climate Data (AHCCD) are climate station datasets that incorporate adjustments (derived from statistical procedures) to the original historical station data to account for discontinuities from non-climatic factors, such as instrument changes or station relocation. Data are provided for temperature, precipitation, pressure and wind speed. Station trend data are provided when available. Trends are calculated using the Theil-Sen method using the station's full period of available data. The availability of trends will vary by station; if more than 5 consecutive years are missing data or more than 10% of the data within the time series is missing, a trend was not calculated.

# tags[].description
Surface Weather Observations (SWOB) measured at the automatic and manual stations of the Environment and Climate Change Canada and partners networks, either for a single station, or for the stations of specific provinces and territories (last 30 days).

# tags[].description
Surface Weather Observations (SWOB) measured at the automatic and manual stations of the Environment and Climate Change Canada and partners networks, either for a single station, or for the stations of specific provinces and territories (last 30 days).

# tags[].description
Surface Weather Observations (SWOB) measured at the automatic and manual stations of the Environment and Climate Change Canada and partners networks, either for a single station, or for the stations of specific provinces and territories (last 30 days).

# tags[].description
Surface Weather Observations (SWOB) measured at the automatic and manual stations of the Environment and Climate Change Canada and partners networks, either for a single station, or for the stations of specific provinces and territories (last 30 days).

# tags[].description
A Virtual Climate station is the result of threading together climate data from proximate current and historical stations to construct a long term threaded data set. For the purpose of identifying and tabulating daily extremes of record for temperature, precipitation and snowfall, the Meteorological Service of Canada has threaded or put together data from closely related stations to compile a long time series of data for about 750 locations in Canada to monitor for record-breaking weather. The length of the time series of virtual stations is often greater than 100 years. A Virtual Climate station is always named for an “Area” rather than a point, e.g. Winnipeg Area, to indicate that the data are drawn from that area(within a 20km radius from the urban center) rather than a single precise location.

# tags[].description
The daily climate records database, also known as Long Term Climate Extremes (LTCE), was developed to address the fragmentation of climate information due to station changes (opening, closing, relocation, etc.) over time. For approximately 750 locations in Canada, "virtual" climate stations have been developed by joining (threading) climate data for an urban location, from nearby stations to make long-term records. Each long-term record consists of the extremes (record values) of daily maximum/minimum temperatures, total precipitation and snowfall for each day of the year. Many of the longest data sets of extremes date as far back as the 1800s. This data provides the daily extremes of record for Temperature for each day of the year. Daily elements include: High Maximum, Low Maximum, High Minimum, Low Minimum.

# tags[].description
The daily climate records database, also known as Long Term Climate Extremes (LTCE), was developed to address the fragmentation of climate information due to station changes (opening, closing, relocation, etc.) over time. For approximately 750 locations in Canada, "virtual" climate stations have been developed by joining (threading) climate data for an urban location, from nearby stations to make long-term records. Each long-term record consists of the extremes (record values) of daily maximum/minimum temperatures, total precipitation and snowfall for each day of the year. Many of the longest data sets of extremes date as far back as the 1800s. This data provides the daily extremes of record for Precipitation for each day of the year. Daily elements include: Greatest Precipitation.

# tags[].description
The daily climate records database, also known as Long Term Climate Extremes (LTCE), was developed to address the fragmentation of climate information due to station changes (opening, closing, relocation, etc.) over time. For approximately 750 locations in Canada, "virtual" climate stations have been developed by joining (threading) climate data for an urban location, from nearby stations to make long-term records. Each long-term record consists of the extremes (record values) of daily maximum/minimum temperatures, total precipitation and snowfall for each day of the year. Many of the longest data sets of extremes date as far back as the 1800s. This data provides the daily extremes of record for Snowfall for each day of the year. Daily elements include: Greatest Snowfall.

# tags[].description
The Air Quality Health Index (AQHI) is a scale designed to help quantify the quality of the air in a certain region on a scale from 1 to 10. When the amount of air pollution is very high, the number is reported as 10+. It also includes a category that describes the health risk associated with the index reading (e.g. Low, Moderate, High, or Very High Health Risk). The AQHI is calculated based on the relative risks of a combination of common air pollutants that are known to harm human health, including ground-level ozone, particulate matter, and nitrogen dioxide. The AQHI formulation captures only the short term or acute health risk (exposure of hour or days at a maximum). The formulation of the AQHI may change over time to reflect new understanding associated with air pollution health effects. The AQHI is calculated from data observed in real time, without being verified (quality control).

# tags[].description
The Air Quality Health Index (AQHI) is a scale designed to help quantify the quality of the air in a certain region on a scale from 1 to 10. When the amount of air pollution is very high, the number is reported as 10+. It also includes a category that describes the health risk associated with the index reading (e.g. Low, Moderate, High, or Very High Health Risk). The AQHI is calculated based on the relative risks of a combination of common air pollutants that are known to harm human health, including ground-level ozone, particulate matter, and nitrogen dioxide. The AQHI formulation captures only the short term or acute health risk (exposure of hour or days at a maximum). The formulation of the AQHI may change over time to reflect new understanding associated with air pollution health effects. The AQHI is calculated from data observed in real time, without being verified (quality control).

# tags[].description
The Air Quality Health Index (AQHI) is a scale designed to help quantify the quality of the air in a certain region on a scale from 1 to 10. When the amount of air pollution is very high, the number is reported as 10+. It also includes a category that describes the health risk associated with the index reading (e.g. Low, Moderate, High, or Very High Health Risk). The AQHI is calculated based on the relative risks of a combination of common air pollutants that are known to harm human health, including ground-level ozone, particulate matter, and nitrogen dioxide. The AQHI formulation captures only the short term or acute health risk (exposure of hour or days at a maximum). The formulation of the AQHI may change over time to reflect new understanding associated with air pollution health effects. The AQHI is calculated from data observed in real time, without being verified (quality control).

# tags[].description
Hurricane tracks and response zone are issued by the Canadian Hurricane Centre as part of the regular hurricane season forecast.

# tags[].description
Hurricane tracks and response zone are issued by the Canadian Hurricane Centre as part of the regular hurricane season forecast.

# tags[].description
Hurricane tracks and response zone are issued by the Canadian Hurricane Centre as part of the regular hurricane season forecast.

# tags[].description
Hurricane tracks and response zone are issued by the Canadian Hurricane Centre as part of the regular hurricane season forecast.

# tags[].description
Statistical post-processing of forecasts from numerical weather and environmental models, including the Global Deterministic Prediction System (GDPS), helps reduce systematic biases and error variance in raw forecasts. This is achieved by establishing optimized statistical relationships between observations recorded at stations and numerical model outputs at nearby grid points. The PROGNOS system is a software package developed by Environment and Climate Change Canada that enables this. Relationships are built using the 'Model Output Statistics' (MOS) method and multiple linear regression (MLR). Currently, only the air temperature at 1.5 meters above the Earth's surface is statistically post-processed. The absence of statistically post-processed forecasts can be due to the unavailability of a statistical model caused by insufficient observation quality or quantity. The geographical region covered by this data includes the territory covered by Canadian meteorological stations and some American buoys. For the GDPS, statistically post-processed forecasts are available at the same issuance frequency as the raw forecasts produced by numerical models (e.g., 00Z, 12Z) and at hourly forecast lead times, from 00 to 84 hours, and then every 3 hours, from 87 to 168 hours.

# tags[].description
Statistical post-processing of forecasts from numerical weather and environmental models, including the Regional Deterministic Prediction System (RDPS), helps reduce systematic biases and error variance in raw forecasts. This is achieved by establishing optimized statistical relationships between observations recorded at stations and numerical model outputs at nearby grid points. The PROGNOS system is a software package developed by Environment and Climate Change Canada that enables this. Relationships are built using the 'Model Output Statistics' (MOS) method and multiple linear regression (MLR). The statistically post-processed weather and environmental variables include air and dew point temperatures at 1.5 meters above the Earth's surface, as well as wind speed and direction at 10 meters above the Earth's surface or at anemometer level in the case of a buoy. The absence of statistically post-processed forecasts can be due to the unavailability of a statistical model caused by insufficient observation quality or quantity. Furthermore, the absence of wind direction forecasts can also be due to predicted wind components being too weak to provide a reliable result. Wind direction and speed forecasts are derived from independent statistical post-processing models. The geographical region covered by this data includes the territory covered by Canadian meteorological stations and some American buoys. For the RDPS, statistically post-processed forecasts are available at the same issuance frequency as the raw forecasts produced by numerical models (e.g., 00Z, 06Z, 12Z, 18Z) and at hourly forecast lead times, from 00 to 84 hours.

# tags[].description
Statistical post-processing of forecasts from numerical weather and environmental models, including the High Resolution Deterministic Prediction System (HRDPS), helps reduce systematic biases and error variance in raw forecasts. This is achieved by establishing optimized statistical relationships between observations recorded at stations and numerical model outputs at nearby grid points. The PROGNOS system is a software package developed by Environment and Climate Change Canada that enables this. Relationships are built using the 'Model Output Statistics' (MOS) method and multiple linear regression (MLR). Currently, only the air temperature at 1.5 meters above the Earth's surface is statistically post-processed. The absence of statistically post-processed forecasts can be due to the unavailability of a statistical model caused by insufficient observation quality or quantity. The geographical region covered by this data includes the territory covered by Canadian meteorological stations and some American buoys. For the HRDPS, statistically post-processed forecasts are available at the same issuance frequency as the raw forecasts produced by numerical models (e.g., 00Z, 06Z, 12Z, 18Z) and at hourly forecast lead times, from 00 to 48 hours.

# tags[].description
Real-time meteorological bulletins (last 140 days)

# tags[].description
MetNotes are a geo- and time-referenced, free form polygon product issued by MSC that complement today's location-based dissemination systems. The concise text of a MetNote (similar to a Tweet) is consistent with communication today where people are seeking information at a glance. Meteorologists will issue a MetNote to add contextual and/or impact information to complement the public forecast that is valid over a specific area, for a specific time range.

# tags[].description
The Global climate model scenarios dataset is based on an ensemble of global climate model projections from the Coupled Model Intercomparison Project Phase 5 (CMIP5). Multi-model ensembles of modelled output (actual value) and projected change (anomaly) are available for historical simulations and three emission scenarios at a 1x1 degree grid resolution. Projected changes are expressed as anomalies with respect to the reference period of 1986-2005.

# tags[].description
The Global climate model scenarios dataset is based on an ensemble of global climate model projections from the Coupled Model Intercomparison Project Phase 5 (CMIP5). Multi-model ensembles of modelled output (actual value) and projected change (anomaly) are available for historical simulations and three emission scenarios at a 1x1 degree grid resolution. Projected changes are expressed as anomalies with respect to the reference period of 1986-2005. A range of percentiles across the multi-model ensembles are available for download.

# tags[].description
The Global climate model scenarios dataset is based on an ensemble of global climate model projections from the Coupled Model Intercomparison Project Phase 5 (CMIP5). Multi-model ensembles of modelled output (actual value) and projected change (anomaly) are available for historical simulations and three emission scenarios at a 1x1 degree grid resolution. Projected changes are expressed as anomalies with respect to the reference period of 1986-2005. A range of percentiles across the multi-model ensembles are available for download.

# tags[].description
The Global climate model scenarios dataset is based on an ensemble of global climate model projections from the Coupled Model Intercomparison Project Phase 5 (CMIP5). Multi-model ensembles of modelled output (actual value) and projected change (anomaly) are available for historical simulations and three emission scenarios at a 1x1 degree grid resolution. Projected changes are expressed as anomalies with respect to the reference period of 1986-2005. A range of percentiles across the multi-model ensembles are available for download.

# tags[].description
The Global climate model scenarios dataset is based on an ensemble of global climate model projections from the Coupled Model Intercomparison Project Phase 5 (CMIP5). Multi-model ensembles of modelled output (actual value) and projected change (anomaly) are available for historical simulations and three emission scenarios at a 1x1 degree grid resolution. Projected changes are expressed as anomalies with respect to the reference period of 1986-2005. A range of percentiles across the multi-model ensembles are available for download.

# tags[].description
The Global climate model scenarios dataset is based on an ensemble of global climate model projections from the Coupled Model Intercomparison Project Phase 5 (CMIP5). Multi-model ensembles of modelled output (actual value) and projected change (anomaly) are available for historical simulations and three emission scenarios at a 1x1 degree grid resolution. Projected changes are expressed as anomalies with respect to the reference period of 1986-2005. A range of percentiles across the multi-model ensembles are available for download.

# tags[].description
The Global climate model scenarios dataset is based on an ensemble of global climate model projections from the Coupled Model Intercomparison Project Phase 5 (CMIP5). Multi-model ensembles of modelled output (actual value) and projected change (anomaly) are available for historical simulations and three emission scenarios at a 1x1 degree grid resolution. Projected changes are expressed as anomalies with respect to the reference period of 1986-2005. A range of percentiles across the multi-model ensembles are available for download.

# tags[].description
The Global climate model scenarios dataset is based on an ensemble of global climate model projections from the Coupled Model Intercomparison Project Phase 5 (CMIP5). Multi-model ensembles of modelled output (actual value) and projected change (anomaly) are available for historical simulations and three emission scenarios at a 1x1 degree grid resolution. Projected changes are expressed as anomalies with respect to the reference period of 1986-2005. A range of percentiles across the multi-model ensembles are available for download.

# tags[].description
The Global climate model scenarios dataset is based on an ensemble of global climate model projections from the Coupled Model Intercomparison Project Phase 5 (CMIP5). Multi-model ensembles of modelled output (actual value) and projected change (anomaly) are available for historical simulations and three emission scenarios at a 1x1 degree grid resolution. Projected changes are expressed as anomalies with respect to the reference period of 1986-2005. A range of percentiles across the multi-model ensembles are available for download.

# tags[].description
The Global climate model scenarios dataset is based on an ensemble of global climate model projections from the Coupled Model Intercomparison Project Phase 5 (CMIP5). Multi-model ensembles of modelled output (actual value) and projected change (anomaly) are available for historical simulations and three emission scenarios at a 1x1 degree grid resolution. Projected changes are expressed as anomalies with respect to the reference period of 1986-2005. A range of percentiles across the multi-model ensembles are available for download.

# tags[].description
The Global climate model scenarios dataset is based on an ensemble of global climate model projections from the Coupled Model Intercomparison Project Phase 5 (CMIP5). Multi-model ensembles of modelled output (actual value) and projected change (anomaly) are available for historical simulations and three emission scenarios at a 1x1 degree grid resolution. Projected changes are expressed as anomalies with respect to the reference period of 1986-2005. A range of percentiles across the multi-model ensembles are available for download.

# tags[].description
The Global climate model scenarios dataset is based on an ensemble of global climate model projections from the Coupled Model Intercomparison Project Phase 5 (CMIP5). Multi-model ensembles of modelled output (actual value) and projected change (anomaly) are available for historical simulations and three emission scenarios at a 1x1 degree grid resolution. Projected changes are expressed as anomalies with respect to the reference period of 1986-2005. A range of percentiles across the multi-model ensembles are available for download.

# tags[].description
The Global climate model scenarios dataset is based on an ensemble of global climate model projections from the Coupled Model Intercomparison Project Phase 5 (CMIP5). Multi-model ensembles of modelled output (actual value) and projected change (anomaly) are available for historical simulations and three emission scenarios at a 1x1 degree grid resolution. Projected changes are expressed as anomalies with respect to the reference period of 1986-2005. A range of percentiles across the multi-model ensembles are available for download.

# tags[].description
The Global climate model scenarios dataset is based on an ensemble of global climate model projections from the Coupled Model Intercomparison Project Phase 5 (CMIP5). Multi-model ensembles of modelled output (actual value) and projected change (anomaly) are available for historical simulations and three emission scenarios at a 1x1 degree grid resolution. Projected changes are expressed as anomalies with respect to the reference period of 1986-2005. A range of percentiles across the multi-model ensembles are available for download.

# tags[].description
CMIP6 climate projections are based on both updated global climate models and new emissions scenarios called “Shared Socioeconomic Pathways” (SSPs). Statistically downscaled datasets have been produced from 26 CMIP6 Global Climate Models (GCMs) under three different emission scenarios (i.e., SSP1-2.6, SSP2-4.5, and SSP5-8.5) using the same downscaling method (Bias Correction/Constructed Analogues with Quantile mapping (BCCAQv2)) and downscaling target data (NRCANmet) as the CMIP5-based downscaled scenarios. Downscaled daily maximum and minimum temperatures (°C) and daily precipitation (mm) are available across Canada at 6x10km grid spatial resolution for the 1950-2014 historical period and for the 2015-2100 period following each of the three emission scenarios.

# tags[].description
CMIP6 climate projections are based on both updated global climate models and new emissions scenarios called “Shared Socioeconomic Pathways” (SSPs). Statistically downscaled datasets have been produced from 26 CMIP6 Global Climate Models (GCMs) under three different emission scenarios (i.e., SSP1-2.6, SSP2-4.5, and SSP5-8.5) using the same downscaling method (Bias Correction/Constructed Analogues with Quantile mapping (BCCAQv2)) and downscaling target data (NRCANmet) as the CMIP5-based downscaled scenarios. Downscaled daily maximum and minimum temperatures (°C) and daily precipitation (mm) are available across Canada at 6x10km grid spatial resolution for the 1950-2014 historical period and for the 2015-2100 period following each of the three emission scenarios.

# tags[].description
CMIP6 climate projections are based on both updated global climate models and new emissions scenarios called “Shared Socioeconomic Pathways” (SSPs). Statistically downscaled datasets have been produced from 26 CMIP6 Global Climate Models (GCMs) under three different emission scenarios (i.e., SSP1-2.6, SSP2-4.5, and SSP5-8.5) using the same downscaling method (Bias Correction/Constructed Analogues with Quantile mapping (BCCAQv2)) and downscaling target data (NRCANmet) as the CMIP5-based downscaled scenarios. Downscaled daily maximum and minimum temperatures (°C) and daily precipitation (mm) are available across Canada at 6x10km grid spatial resolution for the 1950-2014 historical period and for the 2015-2100 period following each of the three emission scenarios.

# tags[].description
CMIP6 climate projections are based on both updated global climate models and new emissions scenarios called “Shared Socioeconomic Pathways” (SSPs). Statistically downscaled datasets have been produced from 26 CMIP6 Global Climate Models (GCMs) under three different emission scenarios (i.e., SSP1-2.6, SSP2-4.5, and SSP5-8.5) using the same downscaling method (Bias Correction/Constructed Analogues with Quantile mapping (BCCAQv2)) and downscaling target data (NRCANmet) as the CMIP5-based downscaled scenarios. Downscaled daily maximum and minimum temperatures (°C) and daily precipitation (mm) are available across Canada at 6x10km grid spatial resolution for the 1950-2014 historical period and for the 2015-2100 period following each of the three emission scenarios.

# tags[].description
CMIP6 climate projections are based on both updated global climate models and new emissions scenarios called “Shared Socioeconomic Pathways” (SSPs). Statistically downscaled datasets have been produced from 26 CMIP6 Global Climate Models (GCMs) under three different emission scenarios (i.e., SSP1-2.6, SSP2-4.5, and SSP5-8.5) using the same downscaling method (Bias Correction/Constructed Analogues with Quantile mapping (BCCAQv2)) and downscaling target data (NRCANmet) as the CMIP5-based downscaled scenarios. Downscaled daily maximum and minimum temperatures (°C) and daily precipitation (mm) are available across Canada at 6x10km grid spatial resolution for the 1950-2014 historical period and for the 2015-2100 period following each of the three emission scenarios.

# tags[].description
CMIP6 climate projections are based on both updated global climate models and new emissions scenarios called “Shared Socioeconomic Pathways” (SSPs). Statistically downscaled datasets have been produced from 26 CMIP6 Global Climate Models (GCMs) under three different emission scenarios (i.e., SSP1-2.6, SSP2-4.5, and SSP5-8.5) using the same downscaling method (Bias Correction/Constructed Analogues with Quantile mapping (BCCAQv2)) and downscaling target data (NRCANmet) as the CMIP5-based downscaled scenarios. Downscaled daily maximum and minimum temperatures (°C) and daily precipitation (mm) are available across Canada at 6x10km grid spatial resolution for the 1950-2014 historical period and for the 2015-2100 period following each of the three emission scenarios.

# tags[].description
CMIP6 climate projections are based on both updated global climate models and new emissions scenarios called “Shared Socioeconomic Pathways” (SSPs). Statistically downscaled datasets have been produced from 26 CMIP6 Global Climate Models (GCMs) under three different emission scenarios (i.e., SSP1-2.6, SSP2-4.5, and SSP5-8.5) using the same downscaling method (Bias Correction/Constructed Analogues with Quantile mapping (BCCAQv2)) and downscaling target data (NRCANmet) as the CMIP5-based downscaled scenarios. Downscaled daily maximum and minimum temperatures (°C) and daily precipitation (mm) are available across Canada at 6x10km grid spatial resolution for the 1950-2014 historical period and for the 2015-2100 period following each of the three emission scenarios.

# tags[].description
CMIP6 climate projections are based on both updated global climate models and new emissions scenarios called “Shared Socioeconomic Pathways” (SSPs). Statistically downscaled datasets have been produced from 26 CMIP6 Global Climate Models (GCMs) under three different emission scenarios (i.e., SSP1-2.6, SSP2-4.5, and SSP5-8.5) using the same downscaling method (Bias Correction/Constructed Analogues with Quantile mapping (BCCAQv2)) and downscaling target data (NRCANmet) as the CMIP5-based downscaled scenarios. Downscaled daily maximum and minimum temperatures (°C) and daily precipitation (mm) are available across Canada at 6x10km grid spatial resolution for the 1950-2014 historical period and for the 2015-2100 period following each of the three emission scenarios.

# tags[].description
CMIP6 climate projections are based on both updated global climate models and new emissions scenarios called “Shared Socioeconomic Pathways” (SSPs). Statistically downscaled datasets have been produced from 26 CMIP6 Global Climate Models (GCMs) under three different emission scenarios (i.e., SSP1-2.6, SSP2-4.5, and SSP5-8.5) using the same downscaling method (Bias Correction/Constructed Analogues with Quantile mapping (BCCAQv2)) and downscaling target data (NRCANmet) as the CMIP5-based downscaled scenarios. Downscaled daily maximum and minimum temperatures (°C) and daily precipitation (mm) are available across Canada at 6x10km grid spatial resolution for the 1950-2014 historical period and for the 2015-2100 period following each of the three emission scenarios.

# tags[].description
CMIP6 climate projections are based on both updated global climate models and new emissions scenarios called “Shared Socioeconomic Pathways” (SSPs). Statistically downscaled datasets have been produced from 26 CMIP6 Global Climate Models (GCMs) under three different emission scenarios (i.e., SSP1-2.6, SSP2-4.5, and SSP5-8.5) using the same downscaling method (Bias Correction/Constructed Analogues with Quantile mapping (BCCAQv2)) and downscaling target data (NRCANmet) as the CMIP5-based downscaled scenarios. Downscaled daily maximum and minimum temperatures (°C) and daily precipitation (mm) are available across Canada at 6x10km grid spatial resolution for the 1950-2014 historical period and for the 2015-2100 period following each of the three emission scenarios.

# tags[].description
The statistically downscaled climate scenarios dataset provides multi-model ensembles of modelled output (actual value) and projected change (anomaly) are available for historical simulations and three emission scenarios, RCP2.6, RCP4.5, RCP8.5, at a 10km resolution. Projected changes are expressed as anomalies with respect to the reference period of 1986-2005. Downscaled data are based on global climate model projections from the Coupled Model Intercomparison Project Phase 5 (CMIP5). A range of percentiles across the multi-model ensemble are available for download.

# tags[].description
The statistically downscaled climate scenarios dataset provides multi-model ensembles of modelled output (actual value) and projected change (anomaly) are available for historical simulations and three emission scenarios, RCP2.6, RCP4.5, RCP8.5, at a 10km resolution. Projected changes are expressed as anomalies with respect to the reference period of 1986-2005. Downscaled data are based on global climate model projections from the Coupled Model Intercomparison Project Phase 5 (CMIP5). A range of percentiles across the multi-model ensemble are available for download.

# tags[].description
The statistically downscaled climate scenarios dataset provides multi-model ensembles of modelled output (actual value) and projected change (anomaly) are available for historical simulations and three emission scenarios, RCP2.6, RCP4.5, RCP8.5, at a 10km resolution. Projected changes are expressed as anomalies with respect to the reference period of 1986-2005. Downscaled data are based on global climate model projections from the Coupled Model Intercomparison Project Phase 5 (CMIP5). A range of percentiles across the multi-model ensemble are available for download.

# tags[].description
The statistically downscaled climate scenarios dataset provides multi-model ensembles of modelled output (actual value) and projected change (anomaly) are available for historical simulations and three emission scenarios, RCP2.6, RCP4.5, RCP8.5, at a 10km resolution. Projected changes are expressed as anomalies with respect to the reference period of 1986-2005. Downscaled data are based on global climate model projections from the Coupled Model Intercomparison Project Phase 5 (CMIP5). A range of percentiles across the multi-model ensemble are available for download.

# tags[].description
The statistically downscaled climate scenarios dataset provides multi-model ensembles of modelled output (actual value) and projected change (anomaly) are available for historical simulations and three emission scenarios, RCP2.6, RCP4.5, RCP8.5, at a 10km resolution. Projected changes are expressed as anomalies with respect to the reference period of 1986-2005. Downscaled data are based on global climate model projections from the Coupled Model Intercomparison Project Phase 5 (CMIP5). A range of percentiles across the multi-model ensemble are available for download.

# tags[].description
The statistically downscaled climate scenarios dataset provides multi-model ensembles of modelled output (actual value) and projected change (anomaly) are available for historical simulations and three emission scenarios, RCP2.6, RCP4.5, RCP8.5, at a 10km resolution. Projected changes are expressed as anomalies with respect to the reference period of 1986-2005. Downscaled data are based on global climate model projections from the Coupled Model Intercomparison Project Phase 5 (CMIP5). A range of percentiles across the multi-model ensemble are available for download.

# tags[].description
The statistically downscaled climate scenarios dataset provides multi-model ensembles of modelled output (actual value) and projected change (anomaly) are available for historical simulations and three emission scenarios, RCP2.6, RCP4.5, RCP8.5, at a 10km resolution. Projected changes are expressed as anomalies with respect to the reference period of 1986-2005. Downscaled data are based on global climate model projections from the Coupled Model Intercomparison Project Phase 5 (CMIP5). A range of percentiles across the multi-model ensemble are available for download.

# tags[].description
The statistically downscaled climate scenarios dataset provides multi-model ensembles of modelled output (actual value) and projected change (anomaly) are available for historical simulations and three emission scenarios, RCP2.6, RCP4.5, RCP8.5, at a 10km resolution. Projected changes are expressed as anomalies with respect to the reference period of 1986-2005. Downscaled data are based on global climate model projections from the Coupled Model Intercomparison Project Phase 5 (CMIP5). A range of percentiles across the multi-model ensemble are available for download.

# tags[].description
The statistically downscaled climate scenarios dataset provides multi-model ensembles of modelled output (actual value) and projected change (anomaly) are available for historical simulations and three emission scenarios, RCP2.6, RCP4.5, RCP8.5, at a 10km resolution. Projected changes are expressed as anomalies with respect to the reference period of 1986-2005. Downscaled data are based on global climate model projections from the Coupled Model Intercomparison Project Phase 5 (CMIP5). A range of percentiles across the multi-model ensemble are available for download.

# tags[].description
The statistically downscaled climate scenarios dataset provides multi-model ensembles of modelled output (actual value) and projected change (anomaly) are available for historical simulations and three emission scenarios, RCP2.6, RCP4.5, RCP8.5, at a 10km resolution. Projected changes are expressed as anomalies with respect to the reference period of 1986-2005. Downscaled data are based on global climate model projections from the Coupled Model Intercomparison Project Phase 5 (CMIP5). A range of percentiles across the multi-model ensemble are available for download.

# tags[].description
The statistically downscaled climate scenarios dataset provides multi-model ensembles of modelled output (actual value) and projected change (anomaly) are available for historical simulations and three emission scenarios, RCP2.6, RCP4.5, RCP8.5, at a 10km resolution. Projected changes are expressed as anomalies with respect to the reference period of 1986-2005. Downscaled data are based on global climate model projections from the Coupled Model Intercomparison Project Phase 5 (CMIP5). A range of percentiles across the multi-model ensemble are available for download.

# tags[].description
The statistically downscaled climate scenarios dataset provides multi-model ensembles of modelled output (actual value) and projected change (anomaly) are available for historical simulations and three emission scenarios, RCP2.6, RCP4.5, RCP8.5, at a 10km resolution. Projected changes are expressed as anomalies with respect to the reference period of 1986-2005. Downscaled data are based on global climate model projections from the Coupled Model Intercomparison Project Phase 5 (CMIP5). A range of percentiles across the multi-model ensemble are available for download.

# tags[].description
High-resolution statistically downscaled climate indices based on model projections from 24 global climate models (GCMs) that participated in the Coupled Model Intercomparison Project Phase 5 (CMIP5) are available. The climate indices are grouped into temperature-related, precipitation-related and agroclimatic categories.

# tags[].description
High-resolution statistically downscaled climate indices based on model projections from 24 global climate models (GCMs) that participated in the Coupled Model Intercomparison Project Phase 5 (CMIP5) are available. The climate indices are grouped into temperature-related, precipitation-related and agroclimatic categories.

# tags[].description
Standardized Precipitation Evapotranspiration Index (SPEI) calculated using global climate model projections from the Coupled Model Intercomparison Project Phase 5 (CMIP5) are provided. SPEI is a multiscalar index frequently used to quantify drought and is based on a climate water balance.

# tags[].description
Standardized Precipitation Evapotranspiration Index (SPEI) calculated using global climate model projections from the Coupled Model Intercomparison Project Phase 5 (CMIP5) are provided. SPEI is a multiscalar index frequently used to quantify drought and is based on a climate water balance.

# tags[].description
Standardized Precipitation Evapotranspiration Index (SPEI) calculated using global climate model projections from the Coupled Model Intercomparison Project Phase 5 (CMIP5) are provided. SPEI is a multiscalar index frequently used to quantify drought and is based on a climate water balance.

# tags[].description
Standardized Precipitation Evapotranspiration Index (SPEI) calculated using global climate model projections from the Coupled Model Intercomparison Project Phase 5 (CMIP5) are provided. SPEI is a multiscalar index frequently used to quantify drought and is based on a climate water balance.

# tags[].description
Standardized Precipitation Evapotranspiration Index (SPEI) calculated using global climate model projections from the Coupled Model Intercomparison Project Phase 5 (CMIP5) are provided. SPEI is a multiscalar index frequently used to quantify drought and is based on a climate water balance.

# tags[].description
Standardized Precipitation Evapotranspiration Index (SPEI) calculated using global climate model projections from the Coupled Model Intercomparison Project Phase 5 (CMIP5) are provided. SPEI is a multiscalar index frequently used to quantify drought and is based on a climate water balance.

# tags[].description
CANGRD data are interpolated from adjusted and homogenized climate station data (i.e., AHCCD datasets). Homogenized climate data incorporate adjustments to the original station data to account for discontinuities from non-climatic factors, such as instrument changes or station relocation. Annual trends of relative total precipitation change (%) for 1948-2012 based on Canadian gridded data (CANGRD) are available, at a 50km resolution across Canada. The relative trends reflect the percent change in total precipitation over a period from the baseline value (defined as the average over 1961-1990 as the reference period). Annual trends of mean surface air temperature change (degrees Celsius) for 1948-2016 based on Canadian gridded data (CANGRD) are available at a 50km resolution across Canada. Temperature trends represent the departure from a mean reference period (1961-1990).

# tags[].description
Gridded annual mean temperature anomalies derived from daily minimum, maximum and mean surface air temperatures (degrees Celsius) and anomalies derived from daily total precipitation is available at a 50km resolution across Canada. The Canadian gridded data (CANGRD) are interpolated from homogenized temperature (i.e., AHCCD datasets). Homogenized temperatures incorporate adjustments to the original station data to account for discontinuities from non-climatic factors, such as instrument changes or station relocation. The anomalies are the difference between the temperature for a given year or season and a baseline value (defined as the average over 1961-1990 as the reference period). The yearly and seasonal temperature anomalies were computed for the years 1948 to 2017. The data will continue to be updated every year. For precipitation, the Canadian gridded data (CANGRD) are interpolated from adjusted precipitation (i.e., AHCCD datasets). Adjusted precipitation data incorporate adjustments to the original station data to account for discontinuities from non-climatic factors, such as instrument changes or station relocation. The anomalies are the percentage difference between the value for a given year or season and a baseline value (defined as the average over 1961-1990 as the reference period). The yearly and seasonal relative precipitation anomalies were computed for the years 1948 to 2014. The data will be updated as time permits.

# tags[].description
Gridded monthly mean temperature anomalies derived from daily minimum, maximum and mean surface air temperatures (degrees Celsius) and anomalies derived from daily total precipitation is available at a 50km resolution across Canada. The Canadian gridded data (CANGRD) are interpolated from homogenized temperature (i.e., AHCCD datasets). Homogenized temperatures incorporate adjustments to the original station data to account for discontinuities from non-climatic factors, such as instrument changes or station relocation. The anomalies are the difference between the temperature for a given year or season and a baseline value (defined as the average over 1961-1990 as the reference period). The yearly and seasonal temperature anomalies were computed for the years 1948 to 2017. The data will continue to be updated every year. For precipitation, the Canadian gridded data (CANGRD) are interpolated from adjusted precipitation (i.e., AHCCD datasets). Adjusted precipitation data incorporate adjustments to the original station data to account for discontinuities from non-climatic factors, such as instrument changes or station relocation. The anomalies are the percentage difference between the value for a given year or season and a baseline value (defined as the average over 1961-1990 as the reference period). The yearly and seasonal relative precipitation anomalies were computed for the years 1948 to 2014. The data will be updated as time permits.

# tags[].description
CANGRD data are interpolated from adjusted and homogenized climate station data (i.e., AHCCD datasets). Homogenized climate data incorporate adjustments to the original station data to account for discontinuities from non-climatic factors, such as instrument changes or station relocation. Seasonal trends of relative total precipitation change (%) for 1948-2012 based on Canadian gridded data (CANGRD) are available, at a 50km resolution across Canada. The relative trends reflect the percent change in total precipitation over a period from the baseline value (defined as the average over 1961-1990 as the reference period). Seasonal trends of mean surface air temperature change (degrees Celsius) for 1948-2016 based on Canadian gridded data (CANGRD) are available at a 50km resolution across Canada. Temperature trends represent the departure from a mean reference period (1961-1990).

# tags[].description
Gridded seasonal mean temperature anomalies derived from daily minimum, maximum and mean surface air temperatures (degrees Celsius) and anomalies derived from daily total precipitation is available at a 50km resolution across Canada. The Canadian gridded data (CANGRD) are interpolated from homogenized temperature (i.e., AHCCD datasets). Homogenized temperatures incorporate adjustments to the original station data to account for discontinuities from non-climatic factors, such as instrument changes or station relocation. The anomalies are the difference between the temperature for a given year or season and a baseline value (defined as the average over 1961-1990 as the reference period). The yearly and seasonal temperature anomalies were computed for the years 1948 to 2017. The data will continue to be updated every year. For precipitation, the Canadian gridded data (CANGRD) are interpolated from adjusted precipitation (i.e., AHCCD datasets). Adjusted precipitation data incorporate adjustments to the original station data to account for discontinuities from non-climatic factors, such as instrument changes or station relocation. The anomalies are the percentage difference between the value for a given year or season and a baseline value (defined as the average over 1961-1990 as the reference period). The yearly and seasonal relative precipitation anomalies were computed for the years 1948 to 2014. The data will be updated as time permits.

# tags[].description
The Regional Deterministic Precipitation Analysis (RDPA) produces a best estimate of the amount of precipitation that occurred over recent past periods of 24 hours. The estimate integrates data from in situ precipitation gauge measurements, weather radar and numerical weather prediction models. Geographic coverage is North America (Canada, United States and Mexico). Data is available at horizontal resolution of 15 km. Data is only available for the surface level. Analysis data is made available once a day for the 24h intervals. A preliminary estimate is available approximately 1h after the end of the accumulation period, and revised 6h after in order to assimilate gauge data arriving later.

# tags[].description
The Regional Deterministic Precipitation Analysis (RDPA) produces a best estimate of the amount of precipitation that occurred over recent past periods of 24 hours. The estimate integrates data from in situ precipitation gauge measurements, weather radar and numerical weather prediction models. Geographic coverage is North America (Canada, United States and Mexico). Data is available at horizontal resolution of 15 km. Data is only available for the surface level. Analysis data is made available four times a day for 6h intervals. A preliminary estimate is available approximately 1h after the end of the accumulation period, and revised 6h after in order to assimilate gauge data arriving later.

# tags[].description
The Regional Deterministic Precipitation Analysis (RDPA) produces a best estimate of the amount of precipitation that occurred over recent past periods of 24 hours. The estimate integrates data from in situ precipitation gauge measurements, weather radar and numerical weather prediction models. Geographic coverage is North America (Canada, United States and Mexico). Data is available at horizontal resolution of 10 km. Data is only available for the surface level. Analysis data is made available once a day for the 24h intervals. A preliminary estimate is available approximately 1h after the end of the accumulation period, and revised 6h after in order to assimilate gauge data arriving later.

# tags[].description
The Regional Deterministic Precipitation Analysis (RDPA) produces a best estimate of the amount of precipitation that occurred over recent past periods of 6 hours. The estimate integrates data from in situ precipitation gauge measurements, weather radar and numerical weather prediction models. Geographic coverage is North America (Canada, United States and Mexico). Data is available at horizontal resolution of 10 km. Data is only available for the surface level. Analysis data is made available four times a day for 6h intervals. A preliminary estimate is available approximately 1h after the end of the accumulation period, and revised 6h after in order to assimilate gauge data arriving later.

# tags[].description
The Regional Deterministic Precipitation Analysis (RDPA) produces a best estimate of the amount of precipitation that occurred over recent past periods of 24 hours. The estimate integrates data from in situ precipitation gauge measurements, weather radar and numerical weather prediction models. Geographic coverage is North America (Canada, United States and Mexico). Data is available at horizontal resolution of 10 km. Data is only available for the surface level. Analysis data is made available once a day for the 24h intervals. The preliminary estimate is available approximately 1h after the end of the accumulation period.

# tags[].description
The Regional Deterministic Precipitation Analysis (RDPA) produces a best estimate of the amount of precipitation that occurred over recent past periods of 6 hours. The estimate integrates data from in situ precipitation gauge measurements, weather radar and numerical weather prediction models. Geographic coverage is North America (Canada, United States and Mexico). Data is available at horizontal resolution of 10 km. Data is only available for the surface level. Analysis data is made available four times a day for 6h intervals. The preliminary estimate is available approximately 1h after the end of the accumulation period.

# tags[].description
The Canadian Seasonal to Inter-annual Prediction System (CanSIPS) carries out physics calculations to arrive at probabilistic predictions of atmospheric elements from the beginning of a month out to up to 12 months into the future, resulting in seasonal forecasts. Atmospheric elements include temperature, precipitation, wind speed and direction and others. This product contains raw numerical results of these calculations. Geographical coverage is global. Data is available on a grid at a horizontal resolution of 1 degree and for a few selected vertical levels.

# tags[].description
The Canadian Seasonal to Inter-annual Prediction System (CanSIPS) carries out physics calculations to arrive at probabilistic predictions of atmospheric elements from the beginning of a month out to up to 12 months into the future, resulting in seasonal forecasts. Atmospheric elements include temperature, precipitation, wind speed and direction and others. This product contains raw numerical results of these calculations. Geographical coverage is global. Data is available on a grid at a horizontal resolution of 2.5 degrees and for a few selected vertical levels.

# tags[].description
The Canadian Seasonal to Inter-annual Prediction System (CanSIPS) carries out physics calculations to arrive at probabilistic predictions of atmospheric elements from the beginning of a month out to up to 12 months into the future, resulting in seasonal forecasts. Atmospheric elements include temperature, precipitation, wind speed and direction and others. This product contains raw numerical results of these calculations. Geographical coverage is global. Data is available on a grid at a horizontal resolution of 2.5 degrees and 1 degree and for a few selected vertical levels. In addition, forecast probabilities for below, near, and above normal temperature and precipitation are available at both resolutions. Predictions and corresponding hindcast are made available monthly.

# tags[].description
The Canadian Seasonal to Inter-annual Prediction System (CanSIPS) carries out physics calculations to arrive at probabilistic predictions of atmospheric elements from the beginning of a month out to up to 12 months into the future, resulting in seasonal forecasts. Atmospheric elements include temperature, precipitation, wind speed and direction and others. This product contains raw numerical results of these calculations. Geographical coverage is global. Data is available on a grid at a horizontal resolution of 2.5 degrees and 1 degree and for a few selected vertical levels. In addition, forecast probabilities for below, near, and above normal temperature and precipitation are available at both resolutions. Predictions and corresponding hindcast are made available monthly.

# tags[].description
The Public Standard Forecast Zones layer is a collection of public program forecast location zone polygons that represents bounded measurable locations at the Public program Standard level. The public program standard level is used in most forecasts, warnings, watches, advisories and special weather statement. With the exception of the Manitoba Lakes, the layer is made up of mostly the land kind of polygons. The layer is made up of four depictions, cartographic detailed (with proper shorelines and high resolution polygons), cartographic coarse (with generalized shorelines and low resolution polygons), digital exaggerated (with exaggerated shorelines and low resolution polygons) and the hybrid (mix of cartographic detail and exaggerated). The digital layer (exaggerated) is a collection of polygons where shoreline boundaries are stretched offshore for the land polygons and inland for the water polygons. In this "Exaggerated" layer, the boundaries are made smooth to reduce the number of polygon vertices. Boundaries of the forecast regions of this hybrid set are derived from the polygon boundaries of both the exaggerated digital, and the cartographic detailed set. Polygon boundaries along the shoreline of this set follow the exaggerated boundaries while the inland boundaries follow the detailed lines. The Public Standard Forecast Zones layer is presented in the polygon package in two coordinate systems, "Projected" (Lambert Conformal Conic) and "Unprojected" (Geographical Coordinated System).

# tags[].description
The Marine Standard Forecast Zones layer is a collection of forecast location polygons that represents bounded (zone) locations at the Marine Program Standard level. The Marine Program standard level is used in most forecasts, warnings, watches, advisories and special marine weather statements. This layer is comprised mainly of water based Marine regions, Met Areas, Lakes, Great Lakes and many of the larger Inland water bodies. The layer is further comprised of four depictions, cartographic detailed (with proper shorelines and high resolution polygons), cartographic coarse (with generalized shorelines and low resolution polygons), and digital exaggerated (with exaggerated shorelines and low resolution polygons) and the hybrid (mix of cartographic detail and exaggerated). The digital layer (exaggerated) is a collection of polygons where shoreline boundaries are stretched inland. In this "Exaggerated" part of the layer, the boundaries are made smooth to reduce the number of polygon vertices. Boundaries of the forecast regions of this hybrid set are derived from the polygon boundaries of both the exaggerated digital, and the cartographic detailed set. Polygon boundaries along the shoreline of this set follow the exaggerated boundaries while the inland boundaries follow the detailed lines. The Marine Standard Forecast Zones layer is also presented in the polygon package in two coordinate systems, "Projected" (Lambert Conic Conformal) and "Unprojected" (Geographical Coordinated System).

# tags[].description
The Thunderstorm Outlook is a graphical or GeoJSON forecast product, which depicts the expected geographic areas of thunderstorms in Canada. Thunderstorm Outlooks are issued by storm prediction centres across Canada during the convective season (May to October) each year. Both the graphical and GeoJSON products use the same source data that forecasters enter through the Ninjo software. Thunderstorm Outlooks are issued once per day at around 12:00 pm regional local time. Since each office has their own standards of procedure, the forecasters are not required to send it at 12:00 pm. Nonetheless, the timestamp in the data is preconfigured to say it is issued at 12:00 pm in the GeoJSON product. Subsequent amendments to existing Thunderstorm Outlooks are issued on an as-needed basis during the day. Each amendment generates a new GeoJSON file and graphical product. The Thunderstorm Outlook may not be updated with active severe weather alerts. Users are asked to check with the latest watches and warnings for the most current conditions.

# tags[].description
The Coastal Flooding Risk Index is a geo- and time-referenced polygon product issued by the Meteorological Service of Canada (MSC) to articulate the coastal flooding risk, impact and probability. Products are issued daily by Storm Prediction Centres and are intended to provide early notification, out to 5 days, of coastal flooding due to astronomical tide, storm surge and wave impacts.

# tags[].description
Environment Canada issues weather alerts about weather related hazards in order to notify those in affected areas so that they can take steps to protect themselves and their property from harm. Alerts are classified depending on the severity and timing of the subject event and include: warnings, watches, advisories and statements. Warnings are usually issued six to 24 hours in advance, although some severe weather (such as thunderstorms and tornadoes) can occur rapidly, with less than a half hours' notice.