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products [e.g., Climate Prediction Center (CPC) unified precipitation estimates, Global Precipitation Climatology Center (GPCC) precipitation dataset, NCEP–NCAR reanalysis data] have become available at various spatial and temporal resolutions based on different data sources (e.g., ground observations, satellites, radar, reanalysis) and data merging techniques. While such datasets are useful to investigate the spatial and temporal behavior in global precipitation ( Fischer and Knutti 2014 ; Ghosh 2012
products [e.g., Climate Prediction Center (CPC) unified precipitation estimates, Global Precipitation Climatology Center (GPCC) precipitation dataset, NCEP–NCAR reanalysis data] have become available at various spatial and temporal resolutions based on different data sources (e.g., ground observations, satellites, radar, reanalysis) and data merging techniques. While such datasets are useful to investigate the spatial and temporal behavior in global precipitation ( Fischer and Knutti 2014 ; Ghosh 2012
phenomena in locations and at scales not previously possible. SMPPs use algorithms that merge passive microwave and infrared sensing data from multiple satellites (e.g., Kidd and Levizzani 2011 ; Kidd and Huffman 2011 ; Tapiador et al. 2012 ; Wright 2018 ). Commonly used SMPPs include the TRMM Multisatellite Precipitation Analysis (TMPA; Huffman et al. 2007 ), the Climate Prediction Center (CPC) morphing technique (CMORPH; Joyce et al. 2004 ), and the Precipitation Estimation from Remotely Sensed
phenomena in locations and at scales not previously possible. SMPPs use algorithms that merge passive microwave and infrared sensing data from multiple satellites (e.g., Kidd and Levizzani 2011 ; Kidd and Huffman 2011 ; Tapiador et al. 2012 ; Wright 2018 ). Commonly used SMPPs include the TRMM Multisatellite Precipitation Analysis (TMPA; Huffman et al. 2007 ), the Climate Prediction Center (CPC) morphing technique (CMORPH; Joyce et al. 2004 ), and the Precipitation Estimation from Remotely Sensed
Extreme Lake-Effect Snow from a GPM Microwave Imager Perspective: Observational Analysis and Precipitation Retrieval Evaluationwaters, coastlines, and sea ice edge. These classes come from a cluster analysis, purely empirical self-grouping of emissivity characteristics ( Prigent et al. 2006 ). The TPW and T2m parameters are obtained from the Global Atmospheric Analysis (GANAL; JMA 2000 ) and the European Centre for Medium-Range Weather Forecasts ( Dee et al. 2011 ) reanalysis datasets for the operational and the climatological GPROF outputs, respectively. For this study, the 1C-R-GMI product (TBs) and the climatological 2A
waters, coastlines, and sea ice edge. These classes come from a cluster analysis, purely empirical self-grouping of emissivity characteristics ( Prigent et al. 2006 ). The TPW and T2m parameters are obtained from the Global Atmospheric Analysis (GANAL; JMA 2000 ) and the European Centre for Medium-Range Weather Forecasts ( Dee et al. 2011 ) reanalysis datasets for the operational and the climatological GPROF outputs, respectively. For this study, the 1C-R-GMI product (TBs) and the climatological 2A
radiative characteristics to satellite microwave radiometric observations via a Bayesian technique. This approach later evolved into the “trained radiometer” or TRAIN algorithm ( Grecu and Olson 2006 ; Grecu et al. 2009 ) wherein the passive microwave algorithm is “trained” using space-borne radar profiles; those reflectivity profiles are in turn linked to heating profiles from CRM simulations in a manner similar to the SLH algorithm. The hydrometeor heating (HH) algorithm ( Yang and Smith 1999a , b
radiative characteristics to satellite microwave radiometric observations via a Bayesian technique. This approach later evolved into the “trained radiometer” or TRAIN algorithm ( Grecu and Olson 2006 ; Grecu et al. 2009 ) wherein the passive microwave algorithm is “trained” using space-borne radar profiles; those reflectivity profiles are in turn linked to heating profiles from CRM simulations in a manner similar to the SLH algorithm. The hydrometeor heating (HH) algorithm ( Yang and Smith 1999a , b
half-hourly gauge–radar QPE from the Ground Validation Multi-Radar Multi-Sensor (GV-MRMS; Petersen et al. 2020 ) suite of products is used in this study as a high-quality reference to evaluate the satellite QPEs. GV-MRMS builds on the MRMS QPE that is derived from 176 WSR-88D radars and more than 18 000 automatic hourly rain gauges over the contiguous United States and Canada ( Zhang et al. 2016 ). Advanced data integration techniques are used to create 3D reflectivity mosaic grids and
half-hourly gauge–radar QPE from the Ground Validation Multi-Radar Multi-Sensor (GV-MRMS; Petersen et al. 2020 ) suite of products is used in this study as a high-quality reference to evaluate the satellite QPEs. GV-MRMS builds on the MRMS QPE that is derived from 176 WSR-88D radars and more than 18 000 automatic hourly rain gauges over the contiguous United States and Canada ( Zhang et al. 2016 ). Advanced data integration techniques are used to create 3D reflectivity mosaic grids and
distance from the coastline. c. GV-MRMS The evaluation of SPPs requires deriving high-quality reference rainfall datasets at the satellite product pixel spatial and temporal resolution. In this study as a reference dataset the high-resolution, ground-based, radar–rain gauge corrected precipitation dataset GV-MRMS ( Kirstetter et al. 2012 , 2018 ) is used. GV-MRMS builds on MRMS that uses advanced data integration techniques to create high-resolution 3D reflectivity mosaic grids and quantitative
distance from the coastline. c. GV-MRMS The evaluation of SPPs requires deriving high-quality reference rainfall datasets at the satellite product pixel spatial and temporal resolution. In this study as a reference dataset the high-resolution, ground-based, radar–rain gauge corrected precipitation dataset GV-MRMS ( Kirstetter et al. 2012 , 2018 ) is used. GV-MRMS builds on MRMS that uses advanced data integration techniques to create high-resolution 3D reflectivity mosaic grids and quantitative
smooth interpolation techniques which effect is similar to that of low-pass filtering. The issue of nonorthogonal errors across products is particularly critical when using techniques such as total least squares or triple collocation for QPEs evaluation (e.g., Alemohammad et al. 2015 ; Li et al. 2018 ; Lu et al. 2021 ), and more generally when ranking QPEs based on their consistency with supposedly “independent” data. For example, a QPE that would correctly reproduce the fine-scale variability of
smooth interpolation techniques which effect is similar to that of low-pass filtering. The issue of nonorthogonal errors across products is particularly critical when using techniques such as total least squares or triple collocation for QPEs evaluation (e.g., Alemohammad et al. 2015 ; Li et al. 2018 ; Lu et al. 2021 ), and more generally when ranking QPEs based on their consistency with supposedly “independent” data. For example, a QPE that would correctly reproduce the fine-scale variability of
Beyond the Pixel: Using Patterns and Multiscale Spatial Information to Improve the Retrieval of Precipitation from Spaceborne Passive Microwave Imagers-1839441. The authors thank Prof. Christian Kummerow, Dr. Dave Randel, and Dr. Wesley Berg from the Precipitation Group at the Colorado State University as well as Dr. Joseph Turk from NASA Jet Propulsion Laboratory for the insightful discussions and shared information which contributed to the present article. APPENDIX A Acronyms AMSR-2 Advanced Microwave Scanning Radiometer 2 CMORPH Climate Prediction Center morphing technique DMSP Defense Meteorological Satellite Program DPR Dual
-1839441. The authors thank Prof. Christian Kummerow, Dr. Dave Randel, and Dr. Wesley Berg from the Precipitation Group at the Colorado State University as well as Dr. Joseph Turk from NASA Jet Propulsion Laboratory for the insightful discussions and shared information which contributed to the present article. APPENDIX A Acronyms AMSR-2 Advanced Microwave Scanning Radiometer 2 CMORPH Climate Prediction Center morphing technique DMSP Defense Meteorological Satellite Program DPR Dual
resolutions are critical for near-real-time applications such as rapid monitoring and forecasting of high-impact societal events like flash floods, debris flows, and shallow landslides. Such resolution can be obtained primarily from satellite sensors on board geostationary Earth orbit (GEO) platforms. NOAA’s Advanced Baseline Imager (ABI) sensor on board the latest generation of Geostationary Operational Environmental Satellites (GOES-R Series) provides 3 times more spectral channels, 4 times the
resolutions are critical for near-real-time applications such as rapid monitoring and forecasting of high-impact societal events like flash floods, debris flows, and shallow landslides. Such resolution can be obtained primarily from satellite sensors on board geostationary Earth orbit (GEO) platforms. NOAA’s Advanced Baseline Imager (ABI) sensor on board the latest generation of Geostationary Operational Environmental Satellites (GOES-R Series) provides 3 times more spectral channels, 4 times the
indication of the limitation associated with the forward-only propagated PMW estimates. Looking at the IMERG components, PMW ( Fig. 1e ) shows overestimation over the Gulf Coast and the Northwest coastline. As expected, morph ( Fig. 1g ) shows a similar spatial distribution compared to PMW but with significantly lower conditional mean rainfall. This could be due to smoothing effects of forward- and backward-propagation techniques applied on PMW estimates to calculate morph dataset. Hence, even though the
indication of the limitation associated with the forward-only propagated PMW estimates. Looking at the IMERG components, PMW ( Fig. 1e ) shows overestimation over the Gulf Coast and the Northwest coastline. As expected, morph ( Fig. 1g ) shows a similar spatial distribution compared to PMW but with significantly lower conditional mean rainfall. This could be due to smoothing effects of forward- and backward-propagation techniques applied on PMW estimates to calculate morph dataset. Hence, even though the
