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- Author or Editor: Giulia Panegrossi x
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Abstract
This paper describes a new Passive Microwave Empirical Cold Surface Classification Algorithm (PESCA) developed for snow-cover detection and characterization by using passive microwave satellite measurements. The main goal of PESCA is to support the retrieval of falling snow, since several studies have highlighted the influence of snow-cover radiative properties on the falling-snow passive microwave signature. The developed method is based on the exploitation of the lower-frequency channels (<90 GHz), common to most microwave radiometers. The method applied to the conically scanning Global Precipitation Measurement (GPM) Microwave Imager (GMI) and the cross-track-scanning Advanced Technology Microwave Sounder (ATMS) is described in this paper. PESCA is based on a decision tree developed using an empirical method and verified using the AutoSnow product built from satellite measurements. The algorithm performance appears to be robust both for sensors in dry conditions (total precipitable water < 10 mm) and for mean surface elevation < 2500 m, independent of the cloud cover. The algorithm shows very good performance for cold temperatures (2-m temperature below 270 K) with a rapid decrease of the detection capabilities between 270 and 280 K, where 280 K is assumed as the maximum temperature limit for PESCA (overall detection statistics: probability of detection is 0.98 for ATMS and 0.92 for GMI, false alarm ratio is 0.01 for ATMS and 0.08 for GMI, and Heidke skill score is 0.72 for ATMS and 0.69 for GMI). Some inconsistencies found between the snow categories identified with the two radiometers are related to their different viewing geometries, spatial resolution, and temporal sampling. The spectral signatures of the different snow classes also appear to be different at high frequency (>90 GHz), indicating potential impact for snowfall retrieval. This method can be applied to other conically scanning and cross-track-scanning radiometers, including the future operational EUMETSAT Polar System Second Generation (EPS-SG) mission microwave radiometers.
Abstract
This paper describes a new Passive Microwave Empirical Cold Surface Classification Algorithm (PESCA) developed for snow-cover detection and characterization by using passive microwave satellite measurements. The main goal of PESCA is to support the retrieval of falling snow, since several studies have highlighted the influence of snow-cover radiative properties on the falling-snow passive microwave signature. The developed method is based on the exploitation of the lower-frequency channels (<90 GHz), common to most microwave radiometers. The method applied to the conically scanning Global Precipitation Measurement (GPM) Microwave Imager (GMI) and the cross-track-scanning Advanced Technology Microwave Sounder (ATMS) is described in this paper. PESCA is based on a decision tree developed using an empirical method and verified using the AutoSnow product built from satellite measurements. The algorithm performance appears to be robust both for sensors in dry conditions (total precipitable water < 10 mm) and for mean surface elevation < 2500 m, independent of the cloud cover. The algorithm shows very good performance for cold temperatures (2-m temperature below 270 K) with a rapid decrease of the detection capabilities between 270 and 280 K, where 280 K is assumed as the maximum temperature limit for PESCA (overall detection statistics: probability of detection is 0.98 for ATMS and 0.92 for GMI, false alarm ratio is 0.01 for ATMS and 0.08 for GMI, and Heidke skill score is 0.72 for ATMS and 0.69 for GMI). Some inconsistencies found between the snow categories identified with the two radiometers are related to their different viewing geometries, spatial resolution, and temporal sampling. The spectral signatures of the different snow classes also appear to be different at high frequency (>90 GHz), indicating potential impact for snowfall retrieval. This method can be applied to other conically scanning and cross-track-scanning radiometers, including the future operational EUMETSAT Polar System Second Generation (EPS-SG) mission microwave radiometers.
Abstract
Surface snowfall rate estimates from the Global Precipitation Measurement (GPM) mission’s Core Observatory sensors and the CloudSat radar are compared to those from the Multi-Radar Multi-Sensor (MRMS) radar composite product over the continental United States during the period from November 2014 to September 2020. The analysis includes the Dual-Frequency Precipitation Radar (DPR) retrieval and its single-frequency counterparts, the GPM Combined Radar Radiometer Algorithm (CORRA), the CloudSat Snow Profile product (2C-SNOW-PROFILE), and two passive microwave retrievals, i.e., the Goddard Profiling algorithm (GPROF) and the Snow Retrieval Algorithm for GMI (SLALOM). The 2C-SNOW retrieval has the highest Heidke skill score (HSS) for detecting snowfall among the products analyzed. SLALOM ranks second; it outperforms GPROF and the other GPM algorithms, all detecting only 30% of the snow events. Since SLALOM is trained with 2C-SNOW, it suggests that the optimal use of the information content in the GMI observations critically depends on the precipitation training dataset. All the retrievals underestimate snowfall rates by a factor of 2 compared to MRMS. Large discrepancies (RMSE of 0.7–1.5 mm h−1) between spaceborne and ground-based snowfall rate estimates are attributed to the complexity of the ice scattering properties and to the limitations of the remote sensing systems: the DPR instrument has low sensitivity, while the radiometric measurements are affected by the confounding effects of the background surface emissivity and of the emission of supercooled liquid droplet layers.
Abstract
Surface snowfall rate estimates from the Global Precipitation Measurement (GPM) mission’s Core Observatory sensors and the CloudSat radar are compared to those from the Multi-Radar Multi-Sensor (MRMS) radar composite product over the continental United States during the period from November 2014 to September 2020. The analysis includes the Dual-Frequency Precipitation Radar (DPR) retrieval and its single-frequency counterparts, the GPM Combined Radar Radiometer Algorithm (CORRA), the CloudSat Snow Profile product (2C-SNOW-PROFILE), and two passive microwave retrievals, i.e., the Goddard Profiling algorithm (GPROF) and the Snow Retrieval Algorithm for GMI (SLALOM). The 2C-SNOW retrieval has the highest Heidke skill score (HSS) for detecting snowfall among the products analyzed. SLALOM ranks second; it outperforms GPROF and the other GPM algorithms, all detecting only 30% of the snow events. Since SLALOM is trained with 2C-SNOW, it suggests that the optimal use of the information content in the GMI observations critically depends on the precipitation training dataset. All the retrievals underestimate snowfall rates by a factor of 2 compared to MRMS. Large discrepancies (RMSE of 0.7–1.5 mm h−1) between spaceborne and ground-based snowfall rate estimates are attributed to the complexity of the ice scattering properties and to the limitations of the remote sensing systems: the DPR instrument has low sensitivity, while the radiometric measurements are affected by the confounding effects of the background surface emissivity and of the emission of supercooled liquid droplet layers.
Abstract
Precipitation estimation from passive microwave radiometry based on physically based profile retrieval algorithms must be aided by a microphysical generator providing structure information on the lower portions of the cloud, consistent with the upper-cloud structures that are sensed. One of the sources for this information is mesoscale model simulations involving explicit or parameterized microphysics. Such microphysical information can be then associated to brightness temperature signatures by using radiative transfer models, forming what are referred to as cloud–radiation databases. In this study cloud–radiation databases from three different storm simulations involving two different mesoscale models run at cloud scales are developed and analyzed. Each database relates a set of microphysical profile realizations describing the space–time properties of a given precipitating storm to multifrequency brightness temperatures associated to a measuring radiometer. In calculating the multifrequency signatures associated with the individual microphysical profiles over model space–time, the authors form what are called brightness temperature model manifolds. Their dimensionality is determined by the number of frequencies carried by the measuring radiometer. By then forming an analogous measurement manifold based on the actual radiometer observations, the radiative consistency between the model representation of a rain cloud and the measured representation are compared. In the analysis, the authors explore how various microphysical, macrophysical, and environmental factors affect the nature of the model manifolds, and how these factors produce or mitigate mismatch between the measurement and model manifolds. Various methods are examined that can be used to eliminate such mismatch. The various cloud–radiation databases are also used with a simplified profile retrieval algorithm to examine the sensitivity of the retrieved hydrometeor profiles and surface rainrates to the different microphysical, macrophysical, and environmental factors of the simulated storms. The results emphasize the need for physical retrieval algorithms to account for a number of these factors, thus preventing biased interpretation of the rain properties of precipitating storms, and minimizing rms uncertainties in the retrieved quantities.
Abstract
Precipitation estimation from passive microwave radiometry based on physically based profile retrieval algorithms must be aided by a microphysical generator providing structure information on the lower portions of the cloud, consistent with the upper-cloud structures that are sensed. One of the sources for this information is mesoscale model simulations involving explicit or parameterized microphysics. Such microphysical information can be then associated to brightness temperature signatures by using radiative transfer models, forming what are referred to as cloud–radiation databases. In this study cloud–radiation databases from three different storm simulations involving two different mesoscale models run at cloud scales are developed and analyzed. Each database relates a set of microphysical profile realizations describing the space–time properties of a given precipitating storm to multifrequency brightness temperatures associated to a measuring radiometer. In calculating the multifrequency signatures associated with the individual microphysical profiles over model space–time, the authors form what are called brightness temperature model manifolds. Their dimensionality is determined by the number of frequencies carried by the measuring radiometer. By then forming an analogous measurement manifold based on the actual radiometer observations, the radiative consistency between the model representation of a rain cloud and the measured representation are compared. In the analysis, the authors explore how various microphysical, macrophysical, and environmental factors affect the nature of the model manifolds, and how these factors produce or mitigate mismatch between the measurement and model manifolds. Various methods are examined that can be used to eliminate such mismatch. The various cloud–radiation databases are also used with a simplified profile retrieval algorithm to examine the sensitivity of the retrieved hydrometeor profiles and surface rainrates to the different microphysical, macrophysical, and environmental factors of the simulated storms. The results emphasize the need for physical retrieval algorithms to account for a number of these factors, thus preventing biased interpretation of the rain properties of precipitating storms, and minimizing rms uncertainties in the retrieved quantities.
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.
Extreme Lake-Effect Snow from a GPM Microwave Imager Perspective: Observational Analysis and Precipitation Retrieval EvaluationAbstract
This study focuses on the ability of the Global Precipitation Measurement (GPM) passive microwave sensors to detect and provide quantitative precipitation estimates (QPE) for extreme lake-effect snowfall events over the U.S. lower Great Lakes region. GPM Microwave Imager (GMI) high-frequency channels can clearly detect intense shallow convective snowfall events. However, GMI Goddard Profiling (GPROF) QPE retrievals produce inconsistent results when compared with the Multi-Radar Multi-Sensor (MRMS) ground-based radar reference dataset. While GPROF retrievals adequately capture intense snowfall rates and spatial patterns of one event, GPROF systematically underestimates intense snowfall rates in another event. Furthermore, GPROF produces abundant light snowfall rates that do not accord with MRMS observations. Ad hoc precipitation-rate thresholds are suggested to partially mitigate GPROF’s overproduction of light snowfall rates. The sensitivity and retrieval efficiency of GPROF to key parameters (2-m temperature, total precipitable water, and background surface type) used to constrain the GPROF a priori retrieval database are investigated. Results demonstrate that typical lake-effect snow environmental and surface conditions, especially coastal surfaces, are underpopulated in the database and adversely affect GPROF retrievals. For the two presented case studies, using a snow-cover a priori database in the locations originally deemed as coastline improves retrieval. This study suggests that it is particularly important to have more accurate GPROF surface classifications and better representativeness of the a priori databases to improve intense lake-effect snow detection and retrieval performance.
Abstract
This study focuses on the ability of the Global Precipitation Measurement (GPM) passive microwave sensors to detect and provide quantitative precipitation estimates (QPE) for extreme lake-effect snowfall events over the U.S. lower Great Lakes region. GPM Microwave Imager (GMI) high-frequency channels can clearly detect intense shallow convective snowfall events. However, GMI Goddard Profiling (GPROF) QPE retrievals produce inconsistent results when compared with the Multi-Radar Multi-Sensor (MRMS) ground-based radar reference dataset. While GPROF retrievals adequately capture intense snowfall rates and spatial patterns of one event, GPROF systematically underestimates intense snowfall rates in another event. Furthermore, GPROF produces abundant light snowfall rates that do not accord with MRMS observations. Ad hoc precipitation-rate thresholds are suggested to partially mitigate GPROF’s overproduction of light snowfall rates. The sensitivity and retrieval efficiency of GPROF to key parameters (2-m temperature, total precipitable water, and background surface type) used to constrain the GPROF a priori retrieval database are investigated. Results demonstrate that typical lake-effect snow environmental and surface conditions, especially coastal surfaces, are underpopulated in the database and adversely affect GPROF retrievals. For the two presented case studies, using a snow-cover a priori database in the locations originally deemed as coastline improves retrieval. This study suggests that it is particularly important to have more accurate GPROF surface classifications and better representativeness of the a priori databases to improve intense lake-effect snow detection and retrieval performance.
