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Zeinab Takbiri, Ardeshir Ebtehaj, Efi Foufoula-Georgiou, Pierre-Emmanuel Kirstetter, and F. Joseph Turk

blizzard storm with the mesoscale MM5 model and a delta-Eddington-type radiative transfer (RT) model to produce a storm-scale database for snowfall retrieval using AMSU-B observations. Noh et al. (2009) used a large number of snowfall profiles from airborne, surface, and satellite radars, as well as an atmospheric RT model ( Liu 1998 ) to generate a regional database for snowfall retrievals using the AMSU-B data. The study used the NESDIS Microwave Land Surface Emissivity Model ( Weng et al. 2001

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Liao-Fan Lin, Ardeshir M. Ebtehaj, Alejandro N. Flores, Satish Bastola, and Rafael L. Bras

1. Introduction Numerical climate and land–atmosphere models are widely used for providing land–atmospheric predictions at different time scales. These models typically capture both atmospheric thermodynamic processes and cloud microphysics to predict the dynamics of land–atmosphere water and energy fluxes. To improve the predictions of land–atmosphere state variables and parameters, a common practice is to assimilate observations from in situ gauges, radiosondes, and satellite measurements

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Clément Guilloteau and Efi Foufoula-Georgiou

profiles associated with collocated GMI radiometric measurements. The first database contains only profiles over vegetated land surfaces, excluding, in particular, coastal areas and snow-covered areas. For this, we rely on the surface type classification used in the current operational implementation (V05) of the GPROF algorithm ( Aires et al. 2011 ). The vegetated surface classes account for 70% of all land surfaces at the latitudes covered by the GPM Core Observatory . The second database contains

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Gail Skofronick-Jackson, Walter A. Petersen, Wesley Berg, Chris Kidd, Erich F. Stocker, Dalia B. Kirschbaum, Ramesh Kakar, Scott A. Braun, George J. Huffman, Toshio Iguchi, Pierre E. Kirstetter, Christian Kummerow, Robert Meneghini, Riko Oki, William S. Olson, Yukari N. Takayabu, Kinji Furukawa, and Thomas Wilheit

precipitation is lower than the other algorithms while there are interesting differences among the diverse approaches over land. Land surfaces tend to complicate the retrieval process and the various algorithms use different approaches to mitigate surface (emissivity and clutter) issues. Fig . 5. Zonal precipitation averages (mm day −1 ) for the full annual cycle during 2015. The five estimates are GPM DPR (dual-frequency radar; red), GPM GPROF (GMI passive radiometer; blue), GPM Ku band (single

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Clément Guilloteau, Efi Foufoula-Georgiou, Christian D. Kummerow, and Veljko Petković

). The final GPROF estimate is a Bayesian weighted average of the precipitation rates associated with each of the retained dictionary vectors. The dictionary consists of a large number (several million) of observed DPR reflectivity profiles and associated microwave TBs simulated by a physical radiative transfer model. In practice, 14 different dictionaries are used over oceans and over different types of land surface. Because the observed vector of microwave TBs depends on the density, size

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Yonghe Liu, Jinming Feng, Zongliang Yang, Yonghong Hu, and Jianlin Li

, 2006 : Development of a 50-year high-resolution global dataset of meteorological forcings for land surface modeling . J. Climate , 19 , 3088 – 3111 , . 10.1175/JCLI3790.1 Shin , Y. , and B. P. Mohanty , 2013 : Development of a deterministic downscaling algorithm for remote sensing soil moisture footprint using soil and vegetation classifications . Water Resour. Res. , 49 , 6208 – 6228 , . 10.1002/wrcr.20495 Sunyer

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Md. Abul Ehsan Bhuiyan, Efthymios I. Nikolopoulos, and Emmanouil N. Anagnostou

other precipitation datasets ( Beck et al. 2019 ). In addition, to obtain the best possible precipitation estimates at global scale, MSWEP accounted a gauge-correction scheme that minimizes timing mismatches when applying the daily gauge corrections ( Beck et al. 2019 ). Bhuiyan et al. (2017) developed a machine learning–based multisource data blending technique and have used it to evaluate the impact of land surface conditions (e.g., vegetation cover and soil moisture) on passive microwave

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Daniel Watters, Alessandro Battaglia, Kamil Mroz, and Frédéric Tridon

produce the DPR-only product and the combined DPR and GMI product, which are stored as level-2A DPR and level-2B CMB data files, respectively, freely available from NASA (2017) . The most recent version, version 5 (V05) released in May 2017, is used in this work. Level-2 data provide the precipitation rate at the surface plus additional parameters and flags, such as freezing-level altitude, range bin for the clutter-free bottom, and land surface type (ocean, land, coast, inland water; Skofronick

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Sara Q. Zhang, T. Matsui, S. Cheung, M. Zupanski, and C. Peters-Lidard

the atmospheric initial condition. Land surface initial conditions (soil moisture and skin temperature) are derived from LIS spinup ( Kumar et al. 2008 ) of the Noah land surface model (LSM) with the MERRA-Land meteorological forcing ( Reichle 2012 ). In subsequent data assimilation cycles, the analysis produced by assimilating observations is used to issue ensemble 3-h forecasts, with new perturbations derived from updated analysis error covariance. The ensemble forecasts are used to update the

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Kamil Mroz, Mario Montopoli, Alessandro Battaglia, Giulia Panegrossi, Pierre Kirstetter, and Luca Baldini

’s AutoSnow product ( Romanov et al. 2000 ). The GMI GPROF version 05 product ( Iguchi and Meneghini 2016c ) tested in this study, considers different sources of precipitation retrievals to build its a priori database depending on the surface type detected in the GMI IFOV. The Ku-band and the DPR combined version 04 algorithms (Ku-V04, CORRA-V04) are used to build the database over “land” (i.e., vegetated surfaces, inland waters, and coastlines) and “ocean” (i.e., oceans, sea ice, and sea ice

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