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- Author or Editor: Ardeshir Ebtehaj x
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Abstract
Using satellite measurements in microwave bands to retrieve precipitation over land requires proper discrimination of the weak rainfall signals from strong and highly variable background Earth surface emissions. Traditionally, land retrieval methods rely on a weak signal of rainfall scattering on high-frequency channels and make use of empirical thresholding and regression-based techniques. Because of the increased surface signal interference, retrievals over radiometrically complex land surfaces—snow-covered lands, deserts, and coastal areas—are particularly challenging for this class of retrieval techniques. This paper evaluates the results by the recently proposed Shrunken Locally Linear Embedding Algorithm for Retrieval of Precipitation (ShARP) using data from the Tropical Rainfall Measuring Mission (TRMM) satellite. The study focuses on a radiometrically complex region, partly covering the Tibetan highlands, Himalayas, and Ganges–Brahmaputra–Meghna River basins, which is unique in terms of its diverse land surface radiation regime and precipitation type, within the TRMM domain. Promising results are presented using ShARP over snow-covered land surfaces and in the vicinity of coastlines, in comparison with the land rainfall retrievals of the standard TRMM 2A12, version 7, product. The results show that ShARP can significantly reduce the rainfall overestimation due to the background snow contamination and markedly improve detection and retrieval of rainfall in the vicinity of coastlines. During the calendar year 2013, compared to TRMM 2A25, it is demonstrated that over the study domain the root-mean-square difference can be reduced up to 38% annually, while the improvement can reach up to 70% during the cold months of the year.
Abstract
Using satellite measurements in microwave bands to retrieve precipitation over land requires proper discrimination of the weak rainfall signals from strong and highly variable background Earth surface emissions. Traditionally, land retrieval methods rely on a weak signal of rainfall scattering on high-frequency channels and make use of empirical thresholding and regression-based techniques. Because of the increased surface signal interference, retrievals over radiometrically complex land surfaces—snow-covered lands, deserts, and coastal areas—are particularly challenging for this class of retrieval techniques. This paper evaluates the results by the recently proposed Shrunken Locally Linear Embedding Algorithm for Retrieval of Precipitation (ShARP) using data from the Tropical Rainfall Measuring Mission (TRMM) satellite. The study focuses on a radiometrically complex region, partly covering the Tibetan highlands, Himalayas, and Ganges–Brahmaputra–Meghna River basins, which is unique in terms of its diverse land surface radiation regime and precipitation type, within the TRMM domain. Promising results are presented using ShARP over snow-covered land surfaces and in the vicinity of coastlines, in comparison with the land rainfall retrievals of the standard TRMM 2A12, version 7, product. The results show that ShARP can significantly reduce the rainfall overestimation due to the background snow contamination and markedly improve detection and retrieval of rainfall in the vicinity of coastlines. During the calendar year 2013, compared to TRMM 2A25, it is demonstrated that over the study domain the root-mean-square difference can be reduced up to 38% annually, while the improvement can reach up to 70% during the cold months of the year.
Abstract
Monitoring changes of precipitation phase from space is important for understanding the mass balance of Earth’s cryosphere in a changing climate. This paper examines a Bayesian nearest neighbor approach for prognostic detection of precipitation and its phase using passive microwave observations from the Global Precipitation Measurement (GPM) satellite. The method uses the weighted Euclidean distance metric to search through an a priori database populated with coincident GPM radiometer and radar observations as well as ancillary snow-cover data. The algorithm performance is evaluated using data from GPM official precipitation products, ground-based radars, and high-fidelity simulations from the Weather Research and Forecasting Model. Using the presented approach, we demonstrate that the hit probability of terrestrial precipitation detection can reach to 0.80, while the probability of false alarm remains below 0.11. The algorithm demonstrates higher skill in detecting snowfall than rainfall, on average by 10%. In particular, the probability of precipitation detection and its solid phase increases by 11% and 8%, over dry snow cover, when compared to other surface types. The main reason is found to be related to the ability of the algorithm in capturing the signal of increased liquid water content in snowy clouds over radiometrically cold snow-covered surfaces.
Abstract
Monitoring changes of precipitation phase from space is important for understanding the mass balance of Earth’s cryosphere in a changing climate. This paper examines a Bayesian nearest neighbor approach for prognostic detection of precipitation and its phase using passive microwave observations from the Global Precipitation Measurement (GPM) satellite. The method uses the weighted Euclidean distance metric to search through an a priori database populated with coincident GPM radiometer and radar observations as well as ancillary snow-cover data. The algorithm performance is evaluated using data from GPM official precipitation products, ground-based radars, and high-fidelity simulations from the Weather Research and Forecasting Model. Using the presented approach, we demonstrate that the hit probability of terrestrial precipitation detection can reach to 0.80, while the probability of false alarm remains below 0.11. The algorithm demonstrates higher skill in detecting snowfall than rainfall, on average by 10%. In particular, the probability of precipitation detection and its solid phase increases by 11% and 8%, over dry snow cover, when compared to other surface types. The main reason is found to be related to the ability of the algorithm in capturing the signal of increased liquid water content in snowy clouds over radiometrically cold snow-covered surfaces.
Abstract
The objective of this study is to develop a framework for dynamically downscaling spaceborne precipitation products using the Weather Research and Forecasting (WRF) Model with four-dimensional variational data assimilation (4D-Var). Numerical experiments have been conducted to 1) understand the sensitivity of precipitation downscaling through point-scale precipitation data assimilation and 2) investigate the impact of seasonality and associated changes in precipitation-generating mechanisms on the quality of spatiotemporal downscaling of precipitation. The point-scale experiment suggests that assimilating precipitation can significantly affect the precipitation analysis, forecast, and downscaling. Because of occasional overestimation or underestimation of small-scale summertime precipitation extremes, the numerical experiments presented here demonstrate that the wintertime assimilation produces downscaled precipitation estimates that are in closer agreement with the reference National Centers for Environmental Prediction stage IV dataset than similar summertime experiments. This study concludes that the WRF 4D-Var system is able to effectively downscale a 6-h precipitation product with a spatial resolution of 20 km to hourly precipitation with a spatial resolution of less than 10 km in grid spacing—relevant to finescale hydrologic applications for the era of the Global Precipitation Measurement mission.
Abstract
The objective of this study is to develop a framework for dynamically downscaling spaceborne precipitation products using the Weather Research and Forecasting (WRF) Model with four-dimensional variational data assimilation (4D-Var). Numerical experiments have been conducted to 1) understand the sensitivity of precipitation downscaling through point-scale precipitation data assimilation and 2) investigate the impact of seasonality and associated changes in precipitation-generating mechanisms on the quality of spatiotemporal downscaling of precipitation. The point-scale experiment suggests that assimilating precipitation can significantly affect the precipitation analysis, forecast, and downscaling. Because of occasional overestimation or underestimation of small-scale summertime precipitation extremes, the numerical experiments presented here demonstrate that the wintertime assimilation produces downscaled precipitation estimates that are in closer agreement with the reference National Centers for Environmental Prediction stage IV dataset than similar summertime experiments. This study concludes that the WRF 4D-Var system is able to effectively downscale a 6-h precipitation product with a spatial resolution of 20 km to hourly precipitation with a spatial resolution of less than 10 km in grid spacing—relevant to finescale hydrologic applications for the era of the Global Precipitation Measurement mission.
Abstract
This paper presents a framework that enables simultaneous assimilation of satellite precipitation and soil moisture observations into the coupled Weather Research and Forecasting (WRF) and Noah land surface model through variational approaches. The authors tested the framework by assimilating precipitation data from the Tropical Rainfall Measuring Mission (TRMM) and soil moisture data from the Soil Moisture Ocean Salinity (SMOS) satellite. The results show that assimilation of both TRMM and SMOS data can effectively improve the forecast skills of precipitation, top 10-cm soil moisture, and 2-m temperature and specific humidity. Within a 2-day time window, impacts of precipitation data assimilation on the forecasts remain relatively constant for forecast lead times greater than 6 h, while the influence of soil moisture data assimilation increases with lead time. The study also demonstrates that the forecast skill of precipitation, soil moisture, and near-surface temperature and humidity are further improved when both the TRMM and SMOS data are assimilated. In particular, the combined data assimilation reduces the prediction biases and root-mean-square errors, respectively, by 57% and 6% (for precipitation); 73% and 27% (for soil moisture); 17% and 9% (for 2-m temperature); and 33% and 11% (for 2-m specific humidity).
Abstract
This paper presents a framework that enables simultaneous assimilation of satellite precipitation and soil moisture observations into the coupled Weather Research and Forecasting (WRF) and Noah land surface model through variational approaches. The authors tested the framework by assimilating precipitation data from the Tropical Rainfall Measuring Mission (TRMM) and soil moisture data from the Soil Moisture Ocean Salinity (SMOS) satellite. The results show that assimilation of both TRMM and SMOS data can effectively improve the forecast skills of precipitation, top 10-cm soil moisture, and 2-m temperature and specific humidity. Within a 2-day time window, impacts of precipitation data assimilation on the forecasts remain relatively constant for forecast lead times greater than 6 h, while the influence of soil moisture data assimilation increases with lead time. The study also demonstrates that the forecast skill of precipitation, soil moisture, and near-surface temperature and humidity are further improved when both the TRMM and SMOS data are assimilated. In particular, the combined data assimilation reduces the prediction biases and root-mean-square errors, respectively, by 57% and 6% (for precipitation); 73% and 27% (for soil moisture); 17% and 9% (for 2-m temperature); and 33% and 11% (for 2-m specific humidity).
Abstract
Snowfall is one of the primary drivers of the global cryosphere and is declining in many regions of the world with widespread hydrological and ecological consequences. Previous studies have shown that the probability of snowfall occurrence is well described by wet-bulb temperatures below 1°C (1.1°C) over land (ocean). Using this relationship, wet-bulb temperatures from three reanalysis products as well as multisatellite and reanalysis precipitation data are analyzed from 1979 to 2017 to study changes in potential snowfall areas, snowfall-to-rainfall transition latitude, snowfall amount, and snowfall-to-precipitation ratio (SPR). Results are presented at hemispheric scales, as well as for three Köppen–Geiger climate classes and four major mountainous regions including the Alps, the western United States, High Mountain Asia (HMA), and the Andes. In all reanalysis products, while changes in the wet-bulb temperature over the Southern Hemisphere are mostly insignificant, significant positive trends are observed over the Northern Hemisphere (NH). Significant reductions are observed in annual-mean potential snowfall areas over NH land (ocean) by 0.52 (0.34) million km2 decade−1 due to an increase of 0.34°C (0.35°C) decade−1 in wet-bulb temperature. The fastest retreat in NH transition latitudes is observed over Europe and central Asia at 0.7° and 0.45° decade−1. Among mountainous regions, the largest decline in potential snowfall areas is observed over the Alps at 3.64% decade−1 followed by the western United States at 2.81% and HMA at 1.85% decade−1. This maximum decrease over the Alps is associated with significant reductions in annual snowfall of 20 mm decade−1 and SPR of 2% decade−1.
Abstract
Snowfall is one of the primary drivers of the global cryosphere and is declining in many regions of the world with widespread hydrological and ecological consequences. Previous studies have shown that the probability of snowfall occurrence is well described by wet-bulb temperatures below 1°C (1.1°C) over land (ocean). Using this relationship, wet-bulb temperatures from three reanalysis products as well as multisatellite and reanalysis precipitation data are analyzed from 1979 to 2017 to study changes in potential snowfall areas, snowfall-to-rainfall transition latitude, snowfall amount, and snowfall-to-precipitation ratio (SPR). Results are presented at hemispheric scales, as well as for three Köppen–Geiger climate classes and four major mountainous regions including the Alps, the western United States, High Mountain Asia (HMA), and the Andes. In all reanalysis products, while changes in the wet-bulb temperature over the Southern Hemisphere are mostly insignificant, significant positive trends are observed over the Northern Hemisphere (NH). Significant reductions are observed in annual-mean potential snowfall areas over NH land (ocean) by 0.52 (0.34) million km2 decade−1 due to an increase of 0.34°C (0.35°C) decade−1 in wet-bulb temperature. The fastest retreat in NH transition latitudes is observed over Europe and central Asia at 0.7° and 0.45° decade−1. Among mountainous regions, the largest decline in potential snowfall areas is observed over the Alps at 3.64% decade−1 followed by the western United States at 2.81% and HMA at 1.85% decade−1. This maximum decrease over the Alps is associated with significant reductions in annual snowfall of 20 mm decade−1 and SPR of 2% decade−1.
Abstract
This study assesses the level-2 snowfall retrieval results from 11 passive microwave radiometers generated by the version 5 Goddard profiling algorithm (GPROF) relative to two spaceborne radars: CloudSat Cloud Profiling Radar (CPR) and Global Precipitation Measurement (GPM) Ku-band Precipitation Radar (KuPR). These 11 radiometers include six conical scanning radiometers [Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E), its successor sensor AMSR2, GPM Microwave Imager (GMI), and three Special Sensor Microwave Imager/Sounders (SSMIS)] and five cross-track scanning radiometers [Advanced Technology Microwave Sounder (ATMS) and four Microwave Humidity Sounders (MHS)]. Results show that over ocean conical scanning radiometers have better detection and intensity estimation skills than cross-track sensors, likely due to the availability and usage of the low-frequency channels (e.g., 19 and 37 GHz). Over land, AMSR-E and AMSR2 have noticeably worse performance than other sensors, primarily due to the lack of higher than 89-GHz channels (e.g., 150, 166, and 183 GHz). Over both land and ocean, all 11 sensors severely underestimate the snowfall intensity, which propagates to the widely used level 3 precipitation product [i.e., Integrated Multi-satelliteE Retrievals for GPM (IMERG)]. These conclusions hold regardless of using either KuPR or CPR as the reference, though the statistical metrics vary quantitatively. The conclusions drawn from these comparisons apply solely to the GPROF version 5 algorithm.
Abstract
This study assesses the level-2 snowfall retrieval results from 11 passive microwave radiometers generated by the version 5 Goddard profiling algorithm (GPROF) relative to two spaceborne radars: CloudSat Cloud Profiling Radar (CPR) and Global Precipitation Measurement (GPM) Ku-band Precipitation Radar (KuPR). These 11 radiometers include six conical scanning radiometers [Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E), its successor sensor AMSR2, GPM Microwave Imager (GMI), and three Special Sensor Microwave Imager/Sounders (SSMIS)] and five cross-track scanning radiometers [Advanced Technology Microwave Sounder (ATMS) and four Microwave Humidity Sounders (MHS)]. Results show that over ocean conical scanning radiometers have better detection and intensity estimation skills than cross-track sensors, likely due to the availability and usage of the low-frequency channels (e.g., 19 and 37 GHz). Over land, AMSR-E and AMSR2 have noticeably worse performance than other sensors, primarily due to the lack of higher than 89-GHz channels (e.g., 150, 166, and 183 GHz). Over both land and ocean, all 11 sensors severely underestimate the snowfall intensity, which propagates to the widely used level 3 precipitation product [i.e., Integrated Multi-satelliteE Retrievals for GPM (IMERG)]. These conclusions hold regardless of using either KuPR or CPR as the reference, though the statistical metrics vary quantitatively. The conclusions drawn from these comparisons apply solely to the GPROF version 5 algorithm.
Abstract
A fully global satellite-based precipitation estimate that can transition across the changing Earth surface and complex land/water conditions is an important capability for many hydrological applications, and for independent evaluation of the precipitation derived from weather and climate models. This capability is inherently challenging owing to the complexity of the surface geophysical properties upon which the satellite-based instruments view. To date, these satellite observations originate primarily from a variety of wide-swath passive microwave (MW) imagers and sounders. In contrast to open ocean and large water bodies, the surface emissivity contribution to passive MW measurements is much more variable for land surfaces, with varying sensitivities to near-surface precipitation. The NASA–JAXA Global Precipitation Measurement (GPM) spacecraft (2014–present) is equipped with a dual-frequency precipitation radar and a multichannel passive MW imaging radiometer specifically designed for precipitation measurement, covering substantially more land area than its predecessor Tropical Rainfall Measuring Mission (TRMM). The synergy between GPM’s instruments has guided a number of new frameworks for passive MW precipitation retrieval algorithms, whereby the information carried by the single narrow-swath precipitation radar is exploited to recover precipitation from a disparate constellation of passive MW imagers and sounders. With over 6 years of increased land surface coverage provided by GPM, new insight has been gained into the nature of the microwave surface emissivity over land and ice/snow-covered surfaces, leading to improvements in a number of physically and semiphysically based precipitation retrieval techniques that adapt to variable Earth surface conditions. In this manuscript, the workings and capabilities of several of these approaches are highlighted.
Abstract
A fully global satellite-based precipitation estimate that can transition across the changing Earth surface and complex land/water conditions is an important capability for many hydrological applications, and for independent evaluation of the precipitation derived from weather and climate models. This capability is inherently challenging owing to the complexity of the surface geophysical properties upon which the satellite-based instruments view. To date, these satellite observations originate primarily from a variety of wide-swath passive microwave (MW) imagers and sounders. In contrast to open ocean and large water bodies, the surface emissivity contribution to passive MW measurements is much more variable for land surfaces, with varying sensitivities to near-surface precipitation. The NASA–JAXA Global Precipitation Measurement (GPM) spacecraft (2014–present) is equipped with a dual-frequency precipitation radar and a multichannel passive MW imaging radiometer specifically designed for precipitation measurement, covering substantially more land area than its predecessor Tropical Rainfall Measuring Mission (TRMM). The synergy between GPM’s instruments has guided a number of new frameworks for passive MW precipitation retrieval algorithms, whereby the information carried by the single narrow-swath precipitation radar is exploited to recover precipitation from a disparate constellation of passive MW imagers and sounders. With over 6 years of increased land surface coverage provided by GPM, new insight has been gained into the nature of the microwave surface emissivity over land and ice/snow-covered surfaces, leading to improvements in a number of physically and semiphysically based precipitation retrieval techniques that adapt to variable Earth surface conditions. In this manuscript, the workings and capabilities of several of these approaches are highlighted.
Abstract
In-depth knowledge about the global patterns and dynamics of land surface net water flux (NWF) is essential for quantification of depletion and recharge of groundwater resources. Net water flux cannot be directly measured, and its estimates as a residual of individual surface flux components often suffer from mass conservation errors due to accumulated systematic biases of individual fluxes. Here, for the first time, we provide direct estimates of global NWF based on near-surface satellite soil moisture retrievals from the Soil Moisture Ocean Salinity (SMOS) and Soil Moisture Active Passive (SMAP) satellites. We apply a recently developed analytical model derived via inversion of the linearized Richards’ equation. The model is parsimonious, yet yields unbiased estimates of long-term cumulative NWF that is generally well correlated with the terrestrial water storage anomaly from the Gravity Recovery and Climate Experiment (GRACE) satellite. In addition, in conjunction with precipitation and evapotranspiration retrievals, the resultant NWF estimates provide a new means for retrieving global infiltration and runoff from satellite observations. However, the efficacy of the proposed approach over densely vegetated regions is questionable, due to the uncertainty of the satellite soil moisture retrievals and the lack of explicit parameterization of transpiration by deeply rooted plants in the proposed model. Future research is needed to advance this modeling paradigm to explicitly account for plant transpiration.
Abstract
In-depth knowledge about the global patterns and dynamics of land surface net water flux (NWF) is essential for quantification of depletion and recharge of groundwater resources. Net water flux cannot be directly measured, and its estimates as a residual of individual surface flux components often suffer from mass conservation errors due to accumulated systematic biases of individual fluxes. Here, for the first time, we provide direct estimates of global NWF based on near-surface satellite soil moisture retrievals from the Soil Moisture Ocean Salinity (SMOS) and Soil Moisture Active Passive (SMAP) satellites. We apply a recently developed analytical model derived via inversion of the linearized Richards’ equation. The model is parsimonious, yet yields unbiased estimates of long-term cumulative NWF that is generally well correlated with the terrestrial water storage anomaly from the Gravity Recovery and Climate Experiment (GRACE) satellite. In addition, in conjunction with precipitation and evapotranspiration retrievals, the resultant NWF estimates provide a new means for retrieving global infiltration and runoff from satellite observations. However, the efficacy of the proposed approach over densely vegetated regions is questionable, due to the uncertainty of the satellite soil moisture retrievals and the lack of explicit parameterization of transpiration by deeply rooted plants in the proposed model. Future research is needed to advance this modeling paradigm to explicitly account for plant transpiration.
