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Extreme Lake-Effect Snow from a GPM Microwave Imager Perspective: Observational Analysis and Precipitation Retrieval Evaluationsignatures at the top of the atmosphere, the version-5 (V5) operational a priori database relies on version-4 (V4) DPR Ku precipitation retrievals over vegetated surfaces, inland waters, and coastlines ( section 2c ) and on the V4 DPR-combined (CMB) algorithm ( Grecu et al. 2016 ) over oceans, sea ice, and sea ice/ocean boundaries. The database is built with one year of GPM data (DPR and CMB), from September 2014 to August 2015. GV-MRMS ( section 2b ) estimates are used over snow-covered surfaces. The
signatures at the top of the atmosphere, the version-5 (V5) operational a priori database relies on version-4 (V4) DPR Ku precipitation retrievals over vegetated surfaces, inland waters, and coastlines ( section 2c ) and on the V4 DPR-combined (CMB) algorithm ( Grecu et al. 2016 ) over oceans, sea ice, and sea ice/ocean boundaries. The database is built with one year of GPM data (DPR and CMB), from September 2014 to August 2015. GV-MRMS ( section 2b ) estimates are used over snow-covered surfaces. The
retrieval space is defined by an a priori database constructed using the GPM Combined Radar-Radiometer (2BCMB) product, which retrieves precipitation profiles that best match all active and passive observations from the GPM Core Observatory satellite ( Grecu et al. 2016 ). The 2BCMB retrievals are currently (through version 6) carried out only in the presence of an active radar signal. The database is constructed from 1 year of 2BCMB profiles and is organized by surface type (including ocean, sea ice
retrieval space is defined by an a priori database constructed using the GPM Combined Radar-Radiometer (2BCMB) product, which retrieves precipitation profiles that best match all active and passive observations from the GPM Core Observatory satellite ( Grecu et al. 2016 ). The 2BCMB retrievals are currently (through version 6) carried out only in the presence of an active radar signal. The database is constructed from 1 year of 2BCMB profiles and is organized by surface type (including ocean, sea ice
Beyond the Pixel: Using Patterns and Multiscale Spatial Information to Improve the Retrieval of Precipitation from Spaceborne Passive Microwave Imagersrain drops, are consistent with the radar observations. Fig . 4. Exploration of the information in the spatial structure of the GMI TBs for an oceanic convective system (South China Sea, at 0445 UTC 9 Oct 2016). (top) Observed TBs at 89, 37, and 10.6 GHz (vertical polarization). (bottom) Near-surface precipitation rates derived from the DPR and from GMI using the GPROF algorithm; the white line corresponds to the cross section shown in Fig. 5 . The ice scattering signature at 89 GHz is shifted to
rain drops, are consistent with the radar observations. Fig . 4. Exploration of the information in the spatial structure of the GMI TBs for an oceanic convective system (South China Sea, at 0445 UTC 9 Oct 2016). (top) Observed TBs at 89, 37, and 10.6 GHz (vertical polarization). (bottom) Near-surface precipitation rates derived from the DPR and from GMI using the GPROF algorithm; the white line corresponds to the cross section shown in Fig. 5 . The ice scattering signature at 89 GHz is shifted to
index, it is in the EPC database for analysis purposes, as a means to efficiently separate the results of this study by fixed surface types. The surface type information obtained from GPROF product provides 14 surface type classes (i.e., ocean, sea ice, five types of vegetation, four types of snow cover, standing water, land/ocean or water coast, and sea ice edge) based on self-similar emissivities derived from TELSEM ( Aires et al. 2011 ; Passive Microwave Algorithm Team Facility 2018 ). In the
index, it is in the EPC database for analysis purposes, as a means to efficiently separate the results of this study by fixed surface types. The surface type information obtained from GPROF product provides 14 surface type classes (i.e., ocean, sea ice, five types of vegetation, four types of snow cover, standing water, land/ocean or water coast, and sea ice edge) based on self-similar emissivities derived from TELSEM ( Aires et al. 2011 ; Passive Microwave Algorithm Team Facility 2018 ). In the
variables and monthly bias correction from gridded observations] making use of the ERA-Interim reanalysis data ( Weedon et al. 2014 ). The CPC combines precipitation from several in situ observation sources (from national and international agencies) and uses an optimal interpolation objective analysis technique ( Chen et al. 2008 ). CFSR by the National Centers for Environmental Prediction (NCEP) is a coupled atmospheric–ocean–land surface–sea ice reanalysis product ( Saha et al. 2014 ). Details such as
variables and monthly bias correction from gridded observations] making use of the ERA-Interim reanalysis data ( Weedon et al. 2014 ). The CPC combines precipitation from several in situ observation sources (from national and international agencies) and uses an optimal interpolation objective analysis technique ( Chen et al. 2008 ). CFSR by the National Centers for Environmental Prediction (NCEP) is a coupled atmospheric–ocean–land surface–sea ice reanalysis product ( Saha et al. 2014 ). Details such as
