Most of the annual rainfall in the Southeastern Mediterranean falls in the wet season from Novemb... more Most of the annual rainfall in the Southeastern Mediterranean falls in the wet season from November to March. It is associated with Mediterranean cyclones, and is sensitive to climate variability. Predicting the wet season precipitation with a few months advance is highly valuable for water resource planning and climate-associated risk management in this semi-arid region. The regional water resource managements and climate-sensitive economic activities have relied on seasonal forecasts from global climate prediction centers. However due to their coarse resolutions, global seasonal forecasts lack regional and local scale information required by regional and local water resource managements. In this study, an analog statistical-downscaling algorithm, k-nearest neighbors (KNN), was introduced to bridge the gap between the coarse forecasts from global models and the needed fine-scale information for the Southeastern Mediterranean. The algorithm, driven by the NCEP Climate Forecast System (CFS) operational forecast and the NCEP/DOE reanalysis, provides monthly precipitations at 2-4 months of lead-time at 18 stations within the major regional hydrological basins. Large-scale predictors for KNN were objectively determined by the correlations between the station historic daily precipitation and variables in reanalysis and CFS reforecast. Besides a single deterministic forecast, this study constructed sixty ensemble members for probabilistic estimates. The KNN algorithm demonstrated its robustness when validated with NCEP/DOE reanalysis from 1981 to 2009 as hindcasts before applied to downscale CFS forecasts. The downscaled predictions show fine-scale information, such as station-to-station variability. The verification against observations shows improved skills of this downscaling utility relative to the CFS model. The KNN-based downscaling system has been in operation for the Israel Water Authority predicting precipitation and driving hydrologic models estimating river flow and aquifer charge for water supply.
This paper investigates the sensitivities of the Weather Research and Forecasting (WRF) model sim... more This paper investigates the sensitivities of the Weather Research and Forecasting (WRF) model simulations to different parameterization schemes (atmospheric boundary layer, microphysics, cumulus, longwave and shortwave radiations and other model configuration parameters) on a domain centered over the inter-mountain western United States (U.S.). Sensitivities are evaluated through a multi-model, multi-physics and multi-perturbation operational ensemble system based on the real-time four-dimensional data assimilation (RTFDDA) forecasting scheme, which was developed at the National Center for Atmospheric Research (NCAR) in the United States. The modeling system has three nested domains with horizontal grid intervals of 30 km, 10 km and 3.3 km. Each member of the ensemble system is treated as one of 48 sensitivity experiments. Validation with station observations is done with simulations on a 3.3-km domain from a cold period (January) and a warm period (July). Analyses and forecasts were run every 6 h during one week in each period. Performance metrics, calculated station-by-station and as a grid-wide average, are the bias, root mean square error (RMSE), mean absolute error (MAE), normalized standard deviation and the correlation between the observation and model. Across all members, the 2-m temperature has domain-average biases of −1.5-0.8 K; the 2-m specific humidity has biases from −0.5-−0.05 g/kg; and the 10-m wind speed and wind direction have biases from 0.2-1.18 m/s and −0.5-4 degrees, respectively. Surface temperature is most sensitive to the microphysics and atmospheric boundary layer schemes, which can also produce significant differences in surface wind speed and direction. All examined variables are sensitive to data assimilation.
CRTC Measurements are relatively sparse and irregular in space and time Data itself are not suffi... more CRTC Measurements are relatively sparse and irregular in space and time Data itself are not sufficient to describe the structures of local-scale circulations Mesoscale Weather Analysis and Forecast Mesoscale Weather Analysis and Forecast
Mesoscale orography and land-surface heterogeneities, including land-water contrasts, vegetation ... more Mesoscale orography and land-surface heterogeneities, including land-water contrasts, vegetation variations and soil-property differences can greatly affect precipitation development and produce very rich temporal-spatial structures, as observed in individual precipitation events as well as in observed precipitation climatographies. The granularities of precipitation structures are very important for hydrological applications. Unfortunately, precipitation observations and available coarse-resolution global models that produce precipitation analyses and forecasts are incapable of simulating these scales and thus can not provide the valuable mesoscale and smaller precipitation distributions.
Many applications rely on accurate weather information in an (range) area of several to few 10s k... more Many applications rely on accurate weather information in an (range) area of several to few 10s kilometers. For example, wind farm power production, airport aerospace management, wildfire containment, military tests such as those at the seven US Army test ranges, and many others, highly desire accurate 4D microscale weather analysis and forecasts in such small spatial scales. Regional and local scale terrain and underlying land surface properties often impose large impact on the dynamical and thermal forcing and generate fast evolving and highly inhomogeneous microscale flows and weather (such as fog patches). It is observed that winds frequently vary significantly (up to 15 m/s) across a wind farm of a few kilometers. Our knowledge and modeling capabilities about such microscale wind characteristics are very limited. In fact, mesoscale numerical weather prediction (NWP) models, running at > l km grid sizes, simulate the Boundary Layer processes using column PBL parameterizations...
Mesoscale (10-2000 km) meteorological data assimilation and prediction are challenging due to spa... more Mesoscale (10-2000 km) meteorological data assimilation and prediction are challenging due to sparse observations, especially in the upper atmosphere. A new source of sensor data called Tropospheric Airborne Meteorological Data Reporting (TAMDAR) has been introduced, and it can potentially fill these data-void regions. The TAMDAR sensors, developed by AirDat, LLC, in collaboration with NASA, FAA and NOAA, are specially designed for smaller commercial aircrafts that fly in the lower troposphere over the CONUS and other parts of the world. These sensors provide a full suite of meteorological measurements with very high space-time density, which include temperature, winds, humidity, icing, turbulence, and pressure. By 15 January 2005, AirDat had completed sensor installations on 63 Saab 340 aircraft operated by Mesaba Airlines, which executes ~400 flights a day, providing ~800 soundings. At present, AirDat is working with other airlines to field more TAMDAR sensors, and aims to complet...
Aiming at regional weather-critical applications, a mesoscale ensemble analysis and prediction sy... more Aiming at regional weather-critical applications, a mesoscale ensemble analysis and prediction system is developed at NCAR/RAL. This system is built upon the NCAR RTFDDA (real-time four-dimensional data assimilation and forecasting), which is an “observation-nudging” based, multi-scale rapid cycling regional and local scale weather analysis and forecasting system (Liu et al., 2006). RTFDDA is an enhanced MM5 and WRF and it has been operated at 20+ regions across US and other global regions, providing real-time multi-scale current weather analyses and 0 – 48 h forecasts. The mesoscale ensemble system described in this paper is an extension of the RTFDDA system enhanced for probabilistic forecast using ensemble modeling approach. Although there are numerous additions to the RTFDDA, the core data analysis and forecasting engine of the ensemble members are essentially similar to RTFDDA, and thus we referred this ensemble system as to ensemble RTFDDA (ERTFDDA).
Ensemble-based mesoscale data assimilation and probabilistic forecasting are traditionally separa... more Ensemble-based mesoscale data assimilation and probabilistic forecasting are traditionally separated in their developments. However, an accurate forecast of probabilistic distribution functions of state variables is in fact equally important for both ensemble-based data assimilation and probabilistic prediction. Poor sampling and forward propagation of initial states and model uncertainties lead to inaccurate probabilistic forecasts and deficient estimate of background error covariance required for Ensemble Kalman Filter data assimilation (EnKF). Thus a well-formulated ensemble prediction system should provide more accurate estimate of the forecast error covariance for EnKF. On the other hand, EnKF is an effective tool for sampling the model initial condition uncertainties that are highly desirable for mesoscale ensemble prediction. It should be noted that mesoscale processes are more complicated than global models and may be dominated by physical processes at times. Thus mesoscale ...
In the last few years, NCAR and the Army Test and Evaluation Command (ATEC) have jointly develope... more In the last few years, NCAR and the Army Test and Evaluation Command (ATEC) have jointly developed a real-time rapid-cycling FDDA and forecast (RTFDDA) system. This MM5-based system has been deployed and is running operationally at five Army testing ranges. The ATEC RTFDDA data assimilation procedure was developed based on the observation-nudging scheme in the standard MM5 (Stauffer and Seaman 1994). It was imported into the WRF model as a part of the ATEC modeling transition from the MM5 to WRF framework. The basic code porting was completed in April 2005. Since then, the WRF-FDDA system has been tested with real-time cycling for the Dugway Provide Ground (DPG), in parallel with the MM5-based RTFDDA system running at the range. To achieve a fair comparison between the WRFand MM5based systems, the WRF model is configured to have the same domain configurations and similar physics to those of the operational MM5.
Accurate wind and severe-weather forecasts are crucial for wind-energy production and grid-load m... more Accurate wind and severe-weather forecasts are crucial for wind-energy production and grid-load management. Most of the prevailing wind power forecast methods rely heavily on statistical approaches that typically do not deal directly with weather processes. Currently employed numerical weather prediction (NWP) models are deemed insufficiently accurate by many industry stakeholders for wind power prediction, even though they have been used for such applications. The reason is partly because the NWP products used for power forecasting are typically produced by the coarse-resolution models at the major operational weather centers. Although a few of high-resolution models are run by some wind energy industries, most of these model do not contain advanced data assimilation capabilities that are required to initialize the model prediction with the important high-resolution weather information.
Mesoscale (10 2000 km) meteorological processes differ from synoptic circulations in that mesosca... more Mesoscale (10 2000 km) meteorological processes differ from synoptic circulations in that mesoscale weather changes rapidly in space and time, which renders it less predictable. Mesoscale processes are influenced by synoptic circulations, and can be caused by local topography and underlying surface physical properties. Physical processes such as radiative transfer, cloud and precipitation, boundary layer mixing, etc., sometimes play dominant roles in shaping the regional weather and climate. Thus, unlike the global ensemble systems in which attention is mostly focused on initial conditions, where perturbations associated with the fast growing modes are added, mesoscale ensemble prediction systems need to address the uncertainties associated with other aspects of modeling systems. It is known that relatively large errors in mesoscale models often lead to an unrealistically small spread and large systematic errors in ensemble forecasts.
Upper-air observations are disproportionately sparse, both temporally and geographically, when co... more Upper-air observations are disproportionately sparse, both temporally and geographically, when compared to surface observations. The lack of data is likely one of the largest limiting factors in numerical weather prediction. Atmospheric measurements performed by the Tropospheric Airborne Meteorological Data Reporting (TAMDAR) sensors of humidity, pressure, temperature, winds, icing, and turbulence provide significant additional information in the lower troposphere. The meteorological data, along with the corresponding location, time, and altitude from built-in GPS, are relayed to a ground-based network operations center via satellite in real-time. The TAMDAR sensors were deployed on a fleet of 63 Saab 340s operated by Mesaba Airlines in the Great Lakes region as a part of the NASA-sponsored Great Lakes Fleet Experiment (GLFE) by 15 Jan. 2005. More than 800 soundings are generated from 400 flights to 75 regional airports daily. A study of the impact of the TAMDAR data on mesoscale NW...
Most of the annual rainfall in the Southeastern Mediterranean falls in the wet season from Novemb... more Most of the annual rainfall in the Southeastern Mediterranean falls in the wet season from November to March. It is associated with Mediterranean cyclones, and is sensitive to climate variability. Predicting the wet season precipitation with a few months advance is highly valuable for water resource planning and climate-associated risk management in this semi-arid region. The regional water resource managements and climate-sensitive economic activities have relied on seasonal forecasts from global climate prediction centers. However due to their coarse resolutions, global seasonal forecasts lack regional and local scale information required by regional and local water resource managements. In this study, an analog statistical-downscaling algorithm, k-nearest neighbors (KNN), was introduced to bridge the gap between the coarse forecasts from global models and the needed fine-scale information for the Southeastern Mediterranean. The algorithm, driven by the NCEP Climate Forecast System (CFS) operational forecast and the NCEP/DOE reanalysis, provides monthly precipitations at 2-4 months of lead-time at 18 stations within the major regional hydrological basins. Large-scale predictors for KNN were objectively determined by the correlations between the station historic daily precipitation and variables in reanalysis and CFS reforecast. Besides a single deterministic forecast, this study constructed sixty ensemble members for probabilistic estimates. The KNN algorithm demonstrated its robustness when validated with NCEP/DOE reanalysis from 1981 to 2009 as hindcasts before applied to downscale CFS forecasts. The downscaled predictions show fine-scale information, such as station-to-station variability. The verification against observations shows improved skills of this downscaling utility relative to the CFS model. The KNN-based downscaling system has been in operation for the Israel Water Authority predicting precipitation and driving hydrologic models estimating river flow and aquifer charge for water supply.
This paper investigates the sensitivities of the Weather Research and Forecasting (WRF) model sim... more This paper investigates the sensitivities of the Weather Research and Forecasting (WRF) model simulations to different parameterization schemes (atmospheric boundary layer, microphysics, cumulus, longwave and shortwave radiations and other model configuration parameters) on a domain centered over the inter-mountain western United States (U.S.). Sensitivities are evaluated through a multi-model, multi-physics and multi-perturbation operational ensemble system based on the real-time four-dimensional data assimilation (RTFDDA) forecasting scheme, which was developed at the National Center for Atmospheric Research (NCAR) in the United States. The modeling system has three nested domains with horizontal grid intervals of 30 km, 10 km and 3.3 km. Each member of the ensemble system is treated as one of 48 sensitivity experiments. Validation with station observations is done with simulations on a 3.3-km domain from a cold period (January) and a warm period (July). Analyses and forecasts were run every 6 h during one week in each period. Performance metrics, calculated station-by-station and as a grid-wide average, are the bias, root mean square error (RMSE), mean absolute error (MAE), normalized standard deviation and the correlation between the observation and model. Across all members, the 2-m temperature has domain-average biases of −1.5-0.8 K; the 2-m specific humidity has biases from −0.5-−0.05 g/kg; and the 10-m wind speed and wind direction have biases from 0.2-1.18 m/s and −0.5-4 degrees, respectively. Surface temperature is most sensitive to the microphysics and atmospheric boundary layer schemes, which can also produce significant differences in surface wind speed and direction. All examined variables are sensitive to data assimilation.
CRTC Measurements are relatively sparse and irregular in space and time Data itself are not suffi... more CRTC Measurements are relatively sparse and irregular in space and time Data itself are not sufficient to describe the structures of local-scale circulations Mesoscale Weather Analysis and Forecast Mesoscale Weather Analysis and Forecast
Mesoscale orography and land-surface heterogeneities, including land-water contrasts, vegetation ... more Mesoscale orography and land-surface heterogeneities, including land-water contrasts, vegetation variations and soil-property differences can greatly affect precipitation development and produce very rich temporal-spatial structures, as observed in individual precipitation events as well as in observed precipitation climatographies. The granularities of precipitation structures are very important for hydrological applications. Unfortunately, precipitation observations and available coarse-resolution global models that produce precipitation analyses and forecasts are incapable of simulating these scales and thus can not provide the valuable mesoscale and smaller precipitation distributions.
Many applications rely on accurate weather information in an (range) area of several to few 10s k... more Many applications rely on accurate weather information in an (range) area of several to few 10s kilometers. For example, wind farm power production, airport aerospace management, wildfire containment, military tests such as those at the seven US Army test ranges, and many others, highly desire accurate 4D microscale weather analysis and forecasts in such small spatial scales. Regional and local scale terrain and underlying land surface properties often impose large impact on the dynamical and thermal forcing and generate fast evolving and highly inhomogeneous microscale flows and weather (such as fog patches). It is observed that winds frequently vary significantly (up to 15 m/s) across a wind farm of a few kilometers. Our knowledge and modeling capabilities about such microscale wind characteristics are very limited. In fact, mesoscale numerical weather prediction (NWP) models, running at > l km grid sizes, simulate the Boundary Layer processes using column PBL parameterizations...
Mesoscale (10-2000 km) meteorological data assimilation and prediction are challenging due to spa... more Mesoscale (10-2000 km) meteorological data assimilation and prediction are challenging due to sparse observations, especially in the upper atmosphere. A new source of sensor data called Tropospheric Airborne Meteorological Data Reporting (TAMDAR) has been introduced, and it can potentially fill these data-void regions. The TAMDAR sensors, developed by AirDat, LLC, in collaboration with NASA, FAA and NOAA, are specially designed for smaller commercial aircrafts that fly in the lower troposphere over the CONUS and other parts of the world. These sensors provide a full suite of meteorological measurements with very high space-time density, which include temperature, winds, humidity, icing, turbulence, and pressure. By 15 January 2005, AirDat had completed sensor installations on 63 Saab 340 aircraft operated by Mesaba Airlines, which executes ~400 flights a day, providing ~800 soundings. At present, AirDat is working with other airlines to field more TAMDAR sensors, and aims to complet...
Aiming at regional weather-critical applications, a mesoscale ensemble analysis and prediction sy... more Aiming at regional weather-critical applications, a mesoscale ensemble analysis and prediction system is developed at NCAR/RAL. This system is built upon the NCAR RTFDDA (real-time four-dimensional data assimilation and forecasting), which is an “observation-nudging” based, multi-scale rapid cycling regional and local scale weather analysis and forecasting system (Liu et al., 2006). RTFDDA is an enhanced MM5 and WRF and it has been operated at 20+ regions across US and other global regions, providing real-time multi-scale current weather analyses and 0 – 48 h forecasts. The mesoscale ensemble system described in this paper is an extension of the RTFDDA system enhanced for probabilistic forecast using ensemble modeling approach. Although there are numerous additions to the RTFDDA, the core data analysis and forecasting engine of the ensemble members are essentially similar to RTFDDA, and thus we referred this ensemble system as to ensemble RTFDDA (ERTFDDA).
Ensemble-based mesoscale data assimilation and probabilistic forecasting are traditionally separa... more Ensemble-based mesoscale data assimilation and probabilistic forecasting are traditionally separated in their developments. However, an accurate forecast of probabilistic distribution functions of state variables is in fact equally important for both ensemble-based data assimilation and probabilistic prediction. Poor sampling and forward propagation of initial states and model uncertainties lead to inaccurate probabilistic forecasts and deficient estimate of background error covariance required for Ensemble Kalman Filter data assimilation (EnKF). Thus a well-formulated ensemble prediction system should provide more accurate estimate of the forecast error covariance for EnKF. On the other hand, EnKF is an effective tool for sampling the model initial condition uncertainties that are highly desirable for mesoscale ensemble prediction. It should be noted that mesoscale processes are more complicated than global models and may be dominated by physical processes at times. Thus mesoscale ...
In the last few years, NCAR and the Army Test and Evaluation Command (ATEC) have jointly develope... more In the last few years, NCAR and the Army Test and Evaluation Command (ATEC) have jointly developed a real-time rapid-cycling FDDA and forecast (RTFDDA) system. This MM5-based system has been deployed and is running operationally at five Army testing ranges. The ATEC RTFDDA data assimilation procedure was developed based on the observation-nudging scheme in the standard MM5 (Stauffer and Seaman 1994). It was imported into the WRF model as a part of the ATEC modeling transition from the MM5 to WRF framework. The basic code porting was completed in April 2005. Since then, the WRF-FDDA system has been tested with real-time cycling for the Dugway Provide Ground (DPG), in parallel with the MM5-based RTFDDA system running at the range. To achieve a fair comparison between the WRFand MM5based systems, the WRF model is configured to have the same domain configurations and similar physics to those of the operational MM5.
Accurate wind and severe-weather forecasts are crucial for wind-energy production and grid-load m... more Accurate wind and severe-weather forecasts are crucial for wind-energy production and grid-load management. Most of the prevailing wind power forecast methods rely heavily on statistical approaches that typically do not deal directly with weather processes. Currently employed numerical weather prediction (NWP) models are deemed insufficiently accurate by many industry stakeholders for wind power prediction, even though they have been used for such applications. The reason is partly because the NWP products used for power forecasting are typically produced by the coarse-resolution models at the major operational weather centers. Although a few of high-resolution models are run by some wind energy industries, most of these model do not contain advanced data assimilation capabilities that are required to initialize the model prediction with the important high-resolution weather information.
Mesoscale (10 2000 km) meteorological processes differ from synoptic circulations in that mesosca... more Mesoscale (10 2000 km) meteorological processes differ from synoptic circulations in that mesoscale weather changes rapidly in space and time, which renders it less predictable. Mesoscale processes are influenced by synoptic circulations, and can be caused by local topography and underlying surface physical properties. Physical processes such as radiative transfer, cloud and precipitation, boundary layer mixing, etc., sometimes play dominant roles in shaping the regional weather and climate. Thus, unlike the global ensemble systems in which attention is mostly focused on initial conditions, where perturbations associated with the fast growing modes are added, mesoscale ensemble prediction systems need to address the uncertainties associated with other aspects of modeling systems. It is known that relatively large errors in mesoscale models often lead to an unrealistically small spread and large systematic errors in ensemble forecasts.
Upper-air observations are disproportionately sparse, both temporally and geographically, when co... more Upper-air observations are disproportionately sparse, both temporally and geographically, when compared to surface observations. The lack of data is likely one of the largest limiting factors in numerical weather prediction. Atmospheric measurements performed by the Tropospheric Airborne Meteorological Data Reporting (TAMDAR) sensors of humidity, pressure, temperature, winds, icing, and turbulence provide significant additional information in the lower troposphere. The meteorological data, along with the corresponding location, time, and altitude from built-in GPS, are relayed to a ground-based network operations center via satellite in real-time. The TAMDAR sensors were deployed on a fleet of 63 Saab 340s operated by Mesaba Airlines in the Great Lakes region as a part of the NASA-sponsored Great Lakes Fleet Experiment (GLFE) by 15 Jan. 2005. More than 800 soundings are generated from 400 flights to 75 regional airports daily. A study of the impact of the TAMDAR data on mesoscale NW...
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