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Extreme Lake-Effect Snow from a GPM Microwave Imager Perspective: Observational Analysis and Precipitation Retrieval Evaluationdetection statistics ( Skofronick-Jackson et al. 2019 ). In particular, the Goddard Profiling Algorithm (GPROF; Kummerow et al. 1996 ; Kummerow et al. 2015 ), which retrieves precipitation rates using passive microwave (PMW) observations, generally underestimates both snowfall detection and quantification when compared to active remote sensing sensor snowfall products. Previous studies based on theoretical analyses ( Skofronick-Jackson and Johnson 2011 ) and radiometer observations ( Panegrossi et al
detection statistics ( Skofronick-Jackson et al. 2019 ). In particular, the Goddard Profiling Algorithm (GPROF; Kummerow et al. 1996 ; Kummerow et al. 2015 ), which retrieves precipitation rates using passive microwave (PMW) observations, generally underestimates both snowfall detection and quantification when compared to active remote sensing sensor snowfall products. Previous studies based on theoretical analyses ( Skofronick-Jackson and Johnson 2011 ) and radiometer observations ( Panegrossi et al
1. Introduction Global precipitation products capitalize upon the long period of record of satellite-based passive microwave (MW) radiometer observations ( Aonashi and Ferraro 2020 ). The passive MW brightness temperature (TB) represents the net top-of-atmosphere upwelling radiation, after taking into consideration the emission and scattering properties of hydrometeors within the top-to-bottom profile, including the contribution from the surface emissivity. The surface precipitation represents
1. Introduction Global precipitation products capitalize upon the long period of record of satellite-based passive microwave (MW) radiometer observations ( Aonashi and Ferraro 2020 ). The passive MW brightness temperature (TB) represents the net top-of-atmosphere upwelling radiation, after taking into consideration the emission and scattering properties of hydrometeors within the top-to-bottom profile, including the contribution from the surface emissivity. The surface precipitation represents
of global precipitation products ( Skofronick-Jackson et al. 2018 ). The GPM Microwave Imager (GMI) observations are taken near coincidentally with the DPR on the core satellite, but the other constellation members have passive MW-only capabilities. There are two radar-based precipitation products produced from the GPM core spacecraft, the Combined Radar–Radiometer Algorithm (CORRA) ( Grecu et al. 2016 ), and the DPR radar-only algorithm ( Seto et al. 2013 ), both of which have a single
of global precipitation products ( Skofronick-Jackson et al. 2018 ). The GPM Microwave Imager (GMI) observations are taken near coincidentally with the DPR on the core satellite, but the other constellation members have passive MW-only capabilities. There are two radar-based precipitation products produced from the GPM core spacecraft, the Combined Radar–Radiometer Algorithm (CORRA) ( Grecu et al. 2016 ), and the DPR radar-only algorithm ( Seto et al. 2013 ), both of which have a single
Enhancing PMW Satellite Precipitation Estimation: Detecting Convective Classespecially pronounced in satellite observations. Since the first spaceborne passive microwave instruments were launched in early 1970s, satellite precipitation retrievals have exploited the link between upwelling radiation and state of atmospheric column. Leveraging decades of ever-improving algorithms, coverage, and data latency, the Global Precipitation Measurement (GPM) mission ( Skofronick-Jackson et al. 2018 ; Hou et al. 2014 ) represents the most advance satellite precipitation project to date
especially pronounced in satellite observations. Since the first spaceborne passive microwave instruments were launched in early 1970s, satellite precipitation retrievals have exploited the link between upwelling radiation and state of atmospheric column. Leveraging decades of ever-improving algorithms, coverage, and data latency, the Global Precipitation Measurement (GPM) mission ( Skofronick-Jackson et al. 2018 ; Hou et al. 2014 ) represents the most advance satellite precipitation project to date
surprisingly better detection rate (21%) than its calibrator MWCOMB (10%). Similar observations are made with the Snow type, with overall better detection with SCaMPR (23%) than MWCOMB (9%). A possible explanation is that surface emissivity affects microwave observations in the range [10–37] GHz more significantly than infrared observations. Surface emissivity variability associated with surface snow is particularly challenging for microwave observations (e.g., Takbiri et al. 2019 ; Gebregiorgis et al
surprisingly better detection rate (21%) than its calibrator MWCOMB (10%). Similar observations are made with the Snow type, with overall better detection with SCaMPR (23%) than MWCOMB (9%). A possible explanation is that surface emissivity affects microwave observations in the range [10–37] GHz more significantly than infrared observations. Surface emissivity variability associated with surface snow is particularly challenging for microwave observations (e.g., Takbiri et al. 2019 ; Gebregiorgis et al
from multiple instruments are presented in Haese et al. (2017) . They use a stochastic approach called random mixing to generate precipitation fields from a set of rain gauge observations and path-averaged rain rates estimated using commercial microwave links. They apply their method to both synthetic (generated via the COSMO model) and real data in a study area in Germany, adopting an hourly time step. Bianchi et al. (2013) also present a technique to combine measurements from rain gauges
from multiple instruments are presented in Haese et al. (2017) . They use a stochastic approach called random mixing to generate precipitation fields from a set of rain gauge observations and path-averaged rain rates estimated using commercial microwave links. They apply their method to both synthetic (generated via the COSMO model) and real data in a study area in Germany, adopting an hourly time step. Bianchi et al. (2013) also present a technique to combine measurements from rain gauges
to the five different 10° latitude bins indicated in the legend. The extremely variable snow-cover extent and snow radiative properties in the MW are one of the main issues in the detection and quantification of snowfall by passive microwave observations, which remain among the most challenging tasks in global precipitation retrieval ( Bennartz and Bauer 2003 ; Skofronick-Jackson et al. 2004 , 2019 ; Noh et al. 2009 ; Levizzani et al. 2011 ; Kongoli and Helfrich 2015 ; Chen et al. 2016
to the five different 10° latitude bins indicated in the legend. The extremely variable snow-cover extent and snow radiative properties in the MW are one of the main issues in the detection and quantification of snowfall by passive microwave observations, which remain among the most challenging tasks in global precipitation retrieval ( Bennartz and Bauer 2003 ; Skofronick-Jackson et al. 2004 , 2019 ; Noh et al. 2009 ; Levizzani et al. 2011 ; Kongoli and Helfrich 2015 ; Chen et al. 2016
Beyond the Pixel: Using Patterns and Multiscale Spatial Information to Improve the Retrieval of Precipitation from Spaceborne Passive Microwave Imagersof orbiting imagers providing frequent observations of clouds and precipitation all over the globe ( Skofronick-Jackson et al. 2018 ). The passive microwave retrieval of precipitation relies on the measurement of radiances at the top of the atmosphere, which are the product of the surface emission, emission and absorption by liquid rain drops and water vapor and scattering by ice particles. Vertically and horizontally polarized radiances are measured at various frequencies between 5 and 200 GHz
of orbiting imagers providing frequent observations of clouds and precipitation all over the globe ( Skofronick-Jackson et al. 2018 ). The passive microwave retrieval of precipitation relies on the measurement of radiances at the top of the atmosphere, which are the product of the surface emission, emission and absorption by liquid rain drops and water vapor and scattering by ice particles. Vertically and horizontally polarized radiances are measured at various frequencies between 5 and 200 GHz
1. Introduction Accurate, physically based precipitation retrieval over global land surfaces is an important goal of the joint NASA/JAXA Global Precipitation Measurement Mission (GPM) ( Hou et al. 2014 ; Skofronick-Jackson et al. 2017 ). This is a challenging problem for the passive microwave constellation component of GPM, as the hydrometeor signal over radiometrically warm land surfaces in the microwave frequencies means that the measurements used in retrievals are more indirect than over
1. Introduction Accurate, physically based precipitation retrieval over global land surfaces is an important goal of the joint NASA/JAXA Global Precipitation Measurement Mission (GPM) ( Hou et al. 2014 ; Skofronick-Jackson et al. 2017 ). This is a challenging problem for the passive microwave constellation component of GPM, as the hydrometeor signal over radiometrically warm land surfaces in the microwave frequencies means that the measurements used in retrievals are more indirect than over
such as the GPM “constellation” ( Skofronick-Jackson et al. 2017 ). These multisensor observations must therefore be converted into precipitation rates and interpolated onto a consistent spatial and temporal grid. The “workhorse” satellite instruments for precipitation estimates are passive microwave (PMW) radiometers, which observe along a satellite’s “swath,” the relatively narrow band over Earth sampled by the onboard sensor as a satellite moves along its orbit. Infrared (IR) observations from
such as the GPM “constellation” ( Skofronick-Jackson et al. 2017 ). These multisensor observations must therefore be converted into precipitation rates and interpolated onto a consistent spatial and temporal grid. The “workhorse” satellite instruments for precipitation estimates are passive microwave (PMW) radiometers, which observe along a satellite’s “swath,” the relatively narrow band over Earth sampled by the onboard sensor as a satellite moves along its orbit. Infrared (IR) observations from
