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Sarah Ringerud, Christa Peters-Lidard, Joe Munchak, and Yalei You

GPM retrievals, NOAA has implemented a 1D variational (1DVAR) technique—the Microwave Integrated Retrieval System (MiRS)—which does an iterative inversion of the radiative transfer in the same way over all surfaces ( Boukabara et al. 2011 ; Meng et al. 2017 ). This method has the benefit of being fully radiometrically consistent and having no reliance on ancillary model data beyond initial offline development of first-guess parameters from climatology. The downside is that in retrieving all

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F. Joseph Turk, Sarah E. Ringerud, Yalei You, Andrea Camplani, Daniele Casella, Giulia Panegrossi, Paolo Sanò, Ardeshir Ebtehaj, Clement Guilloteau, Nobuyuki Utsumi, Catherine Prigent, and Christa Peters-Lidard

comparison to that identifying the precipitation. In heavy precipitation cases, the retrieval is insensitive to the surface; however, for light precipitation cases the emissivity becomes highly relevant in any physically based retrieval developed for land surfaces. Physical models of emissivity, such as those included in the NOAA Community Radiative Transfer Model (CRTM) ( Liu and Boukabara 2014 ) and the Community Microwave Emission Modeling Platform (CMEM) implemented by the European Centre for Medium

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Nobuyuki Utsumi, F. Joseph Turk, Ziad S. Haddad, Pierre-Emmanuel Kirstetter, and Hyungjun Kim

the precipitation-free scenes inferred from the DPR profiles, together with the Modern-Era Retrospective Analysis for Research and Applications version 2 (MERRA-2) ( Gelaro et al. 2017 ) temperature and water vapor profile, the surface emissivity for GMI’s first nine (89 GHz and below) channels is estimated by the emissivity retrieval method of Mathew et al. (2008) with the successive order of interaction (SOI) radiative transfer model ( Heidinger et al. 2006 ), regardless of the surface. For

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Andrea Camplani, Daniele Casella, Paolo Sanò, and Giulia Panegrossi

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

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Veljko Petković, Marko Orescanin, Pierre Kirstetter, Christian Kummerow, and Ralph Ferraro

documented in work of Kummerow et al. (2015) . GPROF utilizes a Bayesian approach that employs a priori information on the relationship between hydrometeor profiles and corresponding radiances. Using the DPR-combined algorithm as a primary source of precipitation profiles, coupled with radiative transfer models, GPROF computes Tbs for any sensor that forms part of the GPM constellation ( Kummerow et al. 2011 ). The algorithm first groups the entire a priori database by using ancillary information (TPW

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Clément Guilloteau and Efi Foufoula-Georgiou

and converted into brightness temperatures (TBs) for physical interpretation. The physical principles of the radiative transfer of microwaves in the atmosphere are well understood and generally accurately reproduced by numerical models. However, the conversion of observed microwave multispectral signatures into hydrometeor profiles (inverse problem) remains uncertain. This uncertainty derives mostly from the inherent underdetermined nature of the inverse problem, that is, while any given

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Zhe Li, Daniel B. Wright, Sara Q. Zhang, Dalia B. Kirschbaum, and Samantha H. Hartke

Goddard Cumulus Ensemble (GCE; Tao et al. 2014 ) for cloud radiation–microphysics parameterization, the Land Information System (LIS; Peters-Lidard et al. 2007 ) for the land surface spinup fields, and EDAS ( Zupanski et al. 2011 ) for data assimilation. NU-WRF EDAS assimilates precipitation-sensitive radiances using an all-sky radiative transfer simulator ( Matsui et al. 2014 ) and a maximum likelihood ensemble filter (MLEF) to produce a 32-member ensemble used to update the state

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