Papers by Samaneh Sabetghadam
Accurate estimation of snow water equivalent (SWE) over high mountainous regions is essential to ... more Accurate estimation of snow water equivalent (SWE) over high mountainous regions is essential to 30 support water resource management. Due to the sparse distribution of in situ observations, models have been used to estimate SWE. However, the influence of horizontal resolution on the accuracy of simulations remains poorly understood. This study evaluates the potential of the Weather Research and Forecasting (WRF) model at horizontal resolutions of 9, 3 and 1 km to estimate the daily values of SWE over the mountainous South Saskatchewan River Basin (SSRB) in Western Canada, for a representative 35 water year, 2017-18. Results show an accumulation period from October 2017 to the annual peak in April 2018, followed by a melting period to the end of water year. All WRF simulations tend to underestimate annual SWE, with largest biases (up to 58 kg/m 2 , i.e. relatively 24%) at higher elevations and coarser horizontal resolution. The two higher-resolution simulations capture the magnitude (and timing) of peak SWE very accurately, with only a 3 to 6% low bias for 1 km and 3 km simulations, respectively. This 40 demonstrates that a 3 km resolution may be appropriate for estimating SWE accumulation across the region. A relationship is identified between model elevation bias and SWE biases, suggesting that the smoothing of topographic features at lower horizontal resolution leads to lower grid cell elevations, warmer temperatures, and lower SWE. Overall, high resolution WRF simulations can provide reliable SWE values as an accurate input for hydrologic modeling over a sparsely monitored mountainous catchment.
Scientific Reports, Nov 28, 2023
Air pollution is the world's largest environmental health threat to humans and has wide-ranging a... more Air pollution is the world's largest environmental health threat to humans and has wide-ranging adverse effects on the environment. The term ventilation coefficient (VC), which is a function of the average wind speed through the planetary boundary layer (PBL) and the PBL height (PBLH), can be used to estimate air pollution potential. We analyzed PBLH, wind speed through PBL, and VC over Tehran using ERA5, and PM2.5 surface concentration using MERRA-2 during 1991-2020. Both PBLH and VC undergo substantial diurnal variations, with higher values during the day and much lower values at night. As a result, PM2.5 concentration in Tehran is the maximum in the early morning, while it is relatively lower in the afternoon. The average wind speed through PBL shows the same diurnal variation in all seasons, except in winter when winds in PBL are stronger at night than during the day. Both PBLH and VC over Tehran show substantial seasonal variations, with much higher values in summer followed in decreasing order by spring, autumn, and winter, highlighting an extremely high air pollution potential in winter. Hence, due to high pollutant emissions, the occurrence of severe air pollution is expected to be a common feature in Tehran in winter. PBLH has significantly increased over Tehran both during the day and at night for the period 1991-2020, primarily in response to the surface warming in recent decades, while wind speed through PBL has significantly declined only at night. The overall impact of such changes is an increase in VC over Tehran both during the day and at night, although the increasing trend of VC is statistically significant only at night. Our results highlight the urgent need for the implementation of effective sustainable policies to reduce air pollution and its adverse effects in winter when air pollution potential is high in Tehran.
International Journal of Climatology, Jun 8, 2016
Visibility impairment, one of the restricting phenomena for aviation, results from light scatteri... more Visibility impairment, one of the restricting phenomena for aviation, results from light scattering and absorption. In this study, the historical visibility database for the period of 1981–2010 is used to obtain extinction coefficient according to the Koschmieder theory in the four busiest airports in Iran, including Tehran‐Mehrabad, Mashhad, Shiraz and Isfahan. The long‐term trend of extinction coefficient and its seasonal variations are investigated using 10th and 90th percentiles of extinction data to show the visual range in each airport. Correlation of the long‐term visibility trend with relative humidity (RH) and cloudiness is also examined. The comparison of seasonal mean extinctions shows the noticeable effect of local climate. The highest extinction coefficient value in each airport is seen during winter, while the lowest value occurs in summer. The seasonal mean extinction coefficient level in winter is about 0.1 km−1 higher than those of the other seasons. This is likely due to pollutants trapped by the stagnant cold air that partly obstruct visibility and increase the extinction in wintertime. There is not an outstanding variation of the tenth percentile of the extinction coefficient in the four airports. The low variation may relate to the definition of the upper threshold in visibility. Analysis shows that there is a positive correlation between extinction and humidity in all the airports that indicates the increase of scattering by hygroscopic particles with increasing humidity, such effect would be accentuated in high RH. The same result is seen for different percentages of cloudiness. To minimize the effects of humidity and cloudiness on the long‐term visibility trend, the days with the relative humidity values more than 70% and cloudiness of more than 5/8 of the sky are removed from the visibility trend analysis. The trends of the screened days are nearly parallel to the trends of raw data, but with a slight difference in each airport. The overall filtered long‐term trends show the increase of extinction coefficient at all the airports that emphasize the effect of pollution on the trend of light extinction within the whole period of this study. Eliminating the meteorological factors from the raw data does not change the overall increasing trend of extinction at Tehran and Isfahan airports. It suggests that the changes in air quality are responsible for the long‐term visibility degradation at these two stations that are located in the two most highly industrialized and polluted cities in Iran.
Atmospheric Environment, Feb 1, 2021
Variability of aerosol properties was investigated over the Middle East. • A good agreement betwe... more Variability of aerosol properties was investigated over the Middle East. • A good agreement between the AOD values from MODIS and AERONET were found. • The highest AODs were found over the Arabian Peninsula especially during warm seasons. • Regions with the highest aerosol loads are accompanied with the lowest AE values. • Desert dust is the second-highest contributor to aerosol composition in warm season.
Quarterly Journal of the Royal Meteorological Society, Sep 19, 2018
The Climate Hazards Group Infrared Precipitation with Station data (CHIRPS) dataset, first releas... more The Climate Hazards Group Infrared Precipitation with Station data (CHIRPS) dataset, first released in 2014, is a high-resolution blended rainfall product with quasi-global coverage that has not been previously evaluated over Iran. Here, we assess the performance of the CHIRPS rainfall estimates against ground-based rainfall observations across Iran over the time period from 2005 to 2014 inclusive. Results show that CHIRPS' performance is better over areas and during the months of predominantly convective precipitation with highest correlations in the southern coastal lowlands characterized by heavy rains from convective origin. Correlations are stronger with variables such as altitude, particularly alongside coastal regions in the north and south, where surface water produces more moisture in the atmosphere. Results of pairwise comparison statistics and categorical skill scores reveal the influence of altitude and precipitation amount, while categorical skill metrics vary more with changes in precipitation amount than with latitudinal or longitudinal changes.
تحلیل فضایی مخاطرات محیطی, Nov 1, 2020
Journal of Aerosol Science, Feb 1, 2014
Aerosol optical depth (AOD) provides a useful characterization of the total absorption and scatte... more Aerosol optical depth (AOD) provides a useful characterization of the total absorption and scattering effect of particles in direct or scattered sunlight, and can be derived from sun spectra measured directly by sun photometers. In this paper, atmospheric optical properties (e.g. AOD 440-1020 nm , α and β, the coefficients in Angstrom formula) and meteorological conditions are presented for: summer (July-August-September) and winter (December-January-February-March) of 2009 -2010 over Zanjan (36.411 N, 48 over Zanjan (36.411 N, 48.291 E) in northwestern Iran. The diurnal variation of AOD in Zanjan is approximately 15%. An exponential dependence of α on AOD in winter indicates that dust aerosols are major contributions of atmospheric turbidity in this region. AOD regressed against PM 10 to establish prediction models. The role of three meteorological parameters on the correlation of AOD and PM 10 are analyzed. Results show that there is a high correlation between AOD 440 and PM 10 in wintertime, and β is a better indicator of air quality in winter than in summer for the study region considered here. Hourly analysis shows that this correlation is highest in the afternoon when the atmospheric mixed layer is at its highest thickness. A similar behavior for AOD-PM 10 and a correlation between optical properties with NO 2 and PM 10 are detected. A sensitivity study was designed to quantify the role of meteorological properties, such as relative humidity, wind speed, and temperature, on the correlation between AOD and PM 10 concentration.
Atmospheric Pollution Research, Sep 1, 2018
Knowledge of spatial and temporal variations of aerosols is essential for understanding the impac... more Knowledge of spatial and temporal variations of aerosols is essential for understanding the impacts of aerosols on air quality. Using aerosol products of the Collection 6 Terra MODIS Deep Blue, regional and temporal variations of aerosol optical depth (AOD) at 0.55 μm in sixteen locations spread over nine different regions of Iran are studied for the period 2001-2015. Monthly means of dust column mass density in three locations of Iran are also obtained from the MERRA-2 dataset. It is found that southwestern Iran experiences the highest annual mean AOD, while other regions experience significantly lower values. Indeed, southwestern Iran is identified as a regional hot spot of aerosols in Southwest Asia, significantly contributing to degrading the air quality in the nearby regions. Annual mean AOD values in most of the other studied locations are between 0.08 and 0.12. High AOD over southwestern Iran is strongly related to frequent dust outbreaks over the region all year long, although AOD values are higher from April to August, during which dust events are more frequent over Southwest Asia. In other, mostly urban populated areas, maximum AOD values occur from mid-winter to mid-spring due to significant aerosol emissions from combustion of fossil fuels, combined with shallow atmospheric boundary-layer depths, which lead to the development of a concentrated mass of aerosols near the surface. On the other hand, minimum values of AOD occur from August to November. Trend analysis indicated that none of the regions of Iran has experienced a noticeable increase or decrease in AOD during 2001-2015.
تحلیل فضایی مخاطرات محیطی, Jun 1, 2019
Environmental Science and Pollution Research, Jun 29, 2013
Light extinction, which is the extent of attenuation of light signal for every distance traveled ... more Light extinction, which is the extent of attenuation of light signal for every distance traveled by light in the absence of special weather conditions (e.g., fog and rain), can be expressed as the sum of scattering and absorption effects of aerosols. In this paper, diurnal and seasonal variations of the extinction coefficient are investigated for the urban areas of Tehran from 2007 to 2009. Cases of visibility impairment that were concurrent with reports of fog, mist, precipitation, or relative humidity above 90 % are filtered. The mean value and standard deviation of daily extinction are 0.49 and 0.39 km -1 , respectively. The average is much higher than that in many other large cities in the world, indicating the rather poor air quality over Tehran. The extinction coefficient shows obvious diurnal variations in each season, with a peak in the morning that is more pronounced in the wintertime. Also, there is a very slight increasing trend in the annual variations of atmospheric extinction coefficient, which suggests that air quality has regressed since 2007. The horizontal extinction coefficient decreased from January to July in each year and then increased between July and December, with the maximum value in the winter. Diurnal variation of extinction is often associated with small values for low relative humidity (RH), but increases significantly at higher RH. Annual correlation analysis shows that there is a positive correlation between the extinction coefficient and RH, CO, PM 10 , SO 2 , and NO 2 concentration, while negative correlation exists between the extinction and T, WS, and O 3 , implying their unfavorable impact on extinction variation. The extinction budget was derived from multiple regression equations using the regression coefficients. On average, 44 % of the extinction is from suspended particles, 3 % is from air molecules, about 5 % is from NO 2 absorption, 0.35 % is from RH, and approximately 48 % is unaccounted for, which may represent errors in the data as well as contribution of other atmospheric constituents omitted from the analysis. Stronger regression equation is achieved in the summer, meaning that the extinction is more predictable in this season using pollutant concentrations.
Environmental Science and Pollution Research, Oct 7, 2020
Global solar radiation is the total amount of solar energy received on a horizontal surface and d... more Global solar radiation is the total amount of solar energy received on a horizontal surface and defined as the sum of direct, diffused, and reflected solar radiation. Global solar radiation is an important variable in agricultural, meteorological, hydrological, and climatological studies. The purpose of this paper is to develop an effective method to estimate the daily global solar radiation using different atmospheric properties detected from satellite data, including cloud fraction, cloud optical depth, aerosol optical depth, aerosol exponent, aerosol index, and precipitable water vapor from Moderate Resolution Imaging Spectroradiometer (MODIS) and ozone monitoring instrument (OMI) daytime data in the urban area of Mashhad, Iran, during the years from 2000 to 2018. Based on seven combinations of the atmospheric properties, models were developed using a standard statistical method, namely, multiple linear regression method and a specific class of artificial neural networks, namely, feedforward multilayer perceptron. The efficiency of the models was compared for the assessment of the daily global solar radiation based on the combinations of the input data. For both methods, 80% percent of the data are used for model development and the remaining data for validation. Results of pairwise statistics indicate that, on average, the estimates were more accurate using the artificial neural networks than the regression method. Results show that in both methods, the accuracy of estimation improves when cloud fraction is used as a predictor. This implies the significant effect of cloud cover on solar radiation. However, using the cloud optical depth decreases the accuracy of the estimation of global solar radiation, i.e., the least accurate model is the one with cloud fraction and cloud optical depth for the neural network method and the model with CF and AE for the regression method. The estimation error comes from the inaccuracy in measuring cloud optical depth that depends on satellite sensor resolution and the inhomogeneity of types and microphysical properties of clouds over the study area. Due to the arid climate of the study area, the precipitable water vapor content does not considerably affect radiation attenuation. The best estimate is earned by cloud fraction and aerosol index as inputs indicating the simultaneous role of aerosol and cloud in global solar radiation. Aerosol index considers the effect of absorbing aerosols such as black carbon and dust and is a complementary information to the cloud cover. The results imply that both methods have the potential to achieve an operational stage, taking advantage of the better availability of satellite data. Even though the artificial neural network is found to be more accurate than multiple linear regression, using the regression method is recommended because it is more easy to use. Results show that the effective variables vary in different seasons. In both methods, estimation error is highest in the spring and lowest in the fall and winter. The high inaccuracy may be due to the high sensitivity of radiative transfer to atmospheric condition in spring. On the other hand, the high accuracy may be caused by the less solar radiation fluctuations during fall and winter because of the lower solar radiation flux.
Research Square (Research Square), Aug 2, 2021
The rst cases of Covid-19 in Iran were reported shortly after the disease outbreak in Wuhan, Chin... more The rst cases of Covid-19 in Iran were reported shortly after the disease outbreak in Wuhan, China. The end of the Persian year and the beginning of the Nowruz holidays in the following year (March 2020) coincided with its global pandemic, which led to quarantine and lockdown in the country. Many studies have shown that with the spread of this disease and the decline of industrial activities, environmental pollutants were drastically reduced. Among these pollutants, Nitrogen Dioxide (NO 2 ) and Carbon Monoxide (CO) are widely caused by anthropogenic and industrial activities. In this study, the changes of these pollutants in Iran and its four metropolises (i.e., Tehran, Mashhad, Isfahan, and Tabriz) in three time periods from March 11 to April 8 of 2019, 2020, and 2021 were investigated. To this end, time-series of the Sentinel-5P TROPOMI and in-situ data within the Google Earth Engine (GEE) cloud-based platform were employed. It was observed that the results obtained from the satellite data were in agreement with the in-situ data (average correlation coe cient = 0.7). Moreover, the results showed that the concentration of NO 2 and CO pollutants in 2020 (the rst year of the Covid-19 pandemic) was 5% lower than in 2019, indicating the observance of quarantine rules as well as people's initial fear of the Coronavirus. Contrarily, these pollutants in 2021 (the second year of the Covid-19 pandemic) were higher than those in 2020 by 5%, which could be due to high vehicle tra c and the lack of serious policy and law-making by the government to ban urban and interurban tra c. Furthermore, the increase of the NO 2 and CO in 2021 was followed by an increase in the deaths caused by Covid-19 and triggering the fourth peak in the Covid-19 cases, signifying a link between exposure to air pollution and Covid-19 mortality in Iran.
Environmental Science and Pollution Research, 2020
Global solar radiation is the total amount of solar energy received on a horizontal surface and d... more Global solar radiation is the total amount of solar energy received on a horizontal surface and defined as the sum of direct, diffused, and reflected solar radiation. Global solar radiation is an important variable in agricultural, meteorological, hydrological, and climatological studies. The purpose of this paper is to develop an effective method to estimate the daily global solar radiation using different atmospheric properties detected from satellite data, including cloud fraction, cloud optical depth, aerosol optical depth, aerosol exponent, aerosol index, and precipitable water vapor from Moderate Resolution Imaging Spectroradiometer (MODIS) and ozone monitoring instrument (OMI) daytime data in the urban area of Mashhad, Iran, during the years from 2000 to 2018. Based on seven combinations of the atmospheric properties, models were developed using a standard statistical method, namely, multiple linear regression method and a specific class of artificial neural networks, namely, feedforward multilayer perceptron. The efficiency of the models was compared for the assessment of the daily global solar radiation based on the combinations of the input data. For both methods, 80% percent of the data are used for model development and the remaining data for validation. Results of pairwise statistics indicate that, on average, the estimates were more accurate using the artificial neural networks than the regression method. Results show that in both methods, the accuracy of estimation improves when cloud fraction is used as a predictor. This implies the significant effect of cloud cover on solar radiation. However, using the cloud optical depth decreases the accuracy of the estimation of global solar radiation, i.e., the least accurate model is the one with cloud fraction and cloud optical depth for the neural network method and the model with CF and AE for the regression method. The estimation error comes from the inaccuracy in measuring cloud optical depth that depends on satellite sensor resolution and the inhomogeneity of types and microphysical properties of clouds over the study area. Due to the arid climate of the study area, the precipitable water vapor content does not considerably affect radiation attenuation. The best estimate is earned by cloud fraction and aerosol index as inputs indicating the simultaneous role of aerosol and cloud in global solar radiation. Aerosol index considers the effect of absorbing aerosols such as black carbon and dust and is a complementary information to the cloud cover. The results imply that both methods have the potential to achieve an operational stage, taking advantage of the better availability of satellite data. Even though the artificial neural network is found to be more accurate than multiple linear regression, using the regression method is recommended because it is more easy to use. Results show that the effective variables vary in different seasons. In both methods, estimation error is highest in the spring and lowest in the fall and winter. The high inaccuracy may be due to the high sensitivity of radiative transfer to atmospheric condition in spring. On the other hand, the high accuracy may be caused by the less solar radiation fluctuations during fall and winter because of the lower solar radiation flux.
Atmospheric Environment, 2021
Variability of aerosol properties was investigated over the Middle East. • A good agreement betwe... more Variability of aerosol properties was investigated over the Middle East. • A good agreement between the AOD values from MODIS and AERONET were found. • The highest AODs were found over the Arabian Peninsula especially during warm seasons. • Regions with the highest aerosol loads are accompanied with the lowest AE values. • Desert dust is the second-highest contributor to aerosol composition in warm season.
Journal of Spatial Analysis Environmental Hazarts, 2019
Atmospheric Pollution Research, 2018
Knowledge of spatial and temporal variations of aerosols is essential for understanding the impac... more Knowledge of spatial and temporal variations of aerosols is essential for understanding the impacts of aerosols on air quality. Using aerosol products of the Collection 6 Terra MODIS Deep Blue, regional and temporal variations of aerosol optical depth (AOD) at 0.55 μm in sixteen locations spread over nine different regions of Iran are studied for the period 2001-2015. Monthly means of dust column mass density in three locations of Iran are also obtained from the MERRA-2 dataset. It is found that southwestern Iran experiences the highest annual mean AOD, while other regions experience significantly lower values. Indeed, southwestern Iran is identified as a regional hot spot of aerosols in Southwest Asia, significantly contributing to degrading the air quality in the nearby regions. Annual mean AOD values in most of the other studied locations are between 0.08 and 0.12. High AOD over southwestern Iran is strongly related to frequent dust outbreaks over the region all year long, although AOD values are higher from April to August, during which dust events are more frequent over Southwest Asia. In other, mostly urban populated areas, maximum AOD values occur from mid-winter to mid-spring due to significant aerosol emissions from combustion of fossil fuels, combined with shallow atmospheric boundary-layer depths, which lead to the development of a concentrated mass of aerosols near the surface. On the other hand, minimum values of AOD occur from August to November. Trend analysis indicated that none of the regions of Iran has experienced a noticeable increase or decrease in AOD during 2001-2015.
Quarterly Journal of the Royal Meteorological Society, 2018
The Climate Hazards Group Infrared Precipitation with Station data (CHIRPS) dataset, first releas... more The Climate Hazards Group Infrared Precipitation with Station data (CHIRPS) dataset, first released in 2014, is a high‐resolution blended rainfall product with quasi‐global coverage that has not previously been evaluated over Iran. Here, we assess the performance of the CHIRPS rainfall estimates against ground‐based rainfall observations across Iran over the time period 2005–2014 inclusive. Results show that performance of CHIRPS is best over areas and during months of predominantly convective precipitation, with the highest correlations in the southern coastal lowlands, which are characterized by heavy rains of convective origin. Correlations are stronger with variables such as altitude, particularly alongside coastal regions in the north and south, where surface water produces more moisture in the atmosphere. Results of pairwise comparison statistics and categorical skill scores reveal the influence of altitude and precipitation amount, while categorical skill metrics vary more wi...
International Journal of Climatology, 2016
Visibility impairment, one of the restricting phenomena for aviation, results from light scatteri... more Visibility impairment, one of the restricting phenomena for aviation, results from light scattering and absorption. In this study, the historical visibility database for the period of 1981–2010 is used to obtain extinction coefficient according to the Koschmieder theory in the four busiest airports in Iran, including Tehran‐Mehrabad, Mashhad, Shiraz and Isfahan. The long‐term trend of extinction coefficient and its seasonal variations are investigated using 10th and 90th percentiles of extinction data to show the visual range in each airport. Correlation of the long‐term visibility trend with relative humidity (RH) and cloudiness is also examined. The comparison of seasonal mean extinctions shows the noticeable effect of local climate. The highest extinction coefficient value in each airport is seen during winter, while the lowest value occurs in summer. The seasonal mean extinction coefficient level in winter is about 0.1 km−1 higher than those of the other seasons. This is likely due to pollutants trapped by the stagnant cold air that partly obstruct visibility and increase the extinction in wintertime. There is not an outstanding variation of the tenth percentile of the extinction coefficient in the four airports. The low variation may relate to the definition of the upper threshold in visibility. Analysis shows that there is a positive correlation between extinction and humidity in all the airports that indicates the increase of scattering by hygroscopic particles with increasing humidity, such effect would be accentuated in high RH. The same result is seen for different percentages of cloudiness. To minimize the effects of humidity and cloudiness on the long‐term visibility trend, the days with the relative humidity values more than 70% and cloudiness of more than 5/8 of the sky are removed from the visibility trend analysis. The trends of the screened days are nearly parallel to the trends of raw data, but with a slight difference in each airport. The overall filtered long‐term trends show the increase of extinction coefficient at all the airports that emphasize the effect of pollution on the trend of light extinction within the whole period of this study. Eliminating the meteorological factors from the raw data does not change the overall increasing trend of extinction at Tehran and Isfahan airports. It suggests that the changes in air quality are responsible for the long‐term visibility degradation at these two stations that are located in the two most highly industrialized and polluted cities in Iran.
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Papers by Samaneh Sabetghadam