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Lisa Milani, Mark S. Kulie, Daniele Casella, Pierre E. Kirstetter, Giulia Panegrossi, Veljko Petkovic, Sarah E. Ringerud, Jean-François Rysman, Paolo Sanò, Nai-Yu Wang, Yalei You, and Gail Skofronick-Jackson

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

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

precipitation content, so additional assumptions about the vertical distribution of hydrometeors are required to calculate the surface precipitation rates desired by users of the data ( Smith et al. 1994 ). GPM, with collocated radiometer and dual-frequency (Ku/Ka band, or 14 and 35 GHz) precipitation radar (DPR), along with a constellation of partner radiometers, is an excellent tool for exploring these issues and relationships, with application toward the improvement of global retrievals. The operational

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Alberto Ortolani, Francesca Caparrini, Samantha Melani, Luca Baldini, and Filippo Giannetti

operational limitations of the standard KF, namely, (i) the nonlinearity of many dynamical systems and (ii) the high computational effort required for the storage and forward integration of the forecast error covariance in large systems. In the EnKF, the covariance is estimated from the generation of an ensemble (statistical sample) of state replications. The EnKF has proved to be a robust estimator even in presence of deviation from Gaussianity assumption ( Katzfuss et al. 2016 ). Many successful

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Yingzhao Ma, V. Chandrasekar, Haonan Chen, and Robert Cifelli

the contribution of lateral terrestrial water flow on regionally hydrological cycle. Coupled with the height above nearest drainage (HAND) technique, the National Water Model (NWM) system with its core component as WRF-Hydro offers an operational framework for real-time and forecast flood guidance across the contiguous United States ( Johnson et al. 2019 ). As noted above, the WRF-Hydro system has been implemented for a wide range of research and operational prediction problems over the world

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

produced by the model’s dynamical equations and parameterizations, which are constrained through the assimilation of satellite radiances ( Benjamin et al. 2019 ). Hence, we refer to this as the “physics-based” approach. A number of datasets, particularly reanalyses such as the Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2; Gelaro et al. 2017 ) from NASA and ERA5 ( Hersbach et al. 2018 ) from the European Centre for Medium-Range Weather Forecasts assimilate PMW TBs

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Phu Nguyen, Mohammed Ombadi, Vesta Afzali Gorooh, Eric J. Shearer, Mojtaba Sadeghi, Soroosh Sorooshian, Kuolin Hsu, David Bolvin, and Martin F. Ralph

the daily and subdaily scales are more important from the standpoint of operational watershed hydrology and water resources management for applications such as flood forecasting. Furthermore, since PDIR-Now is an IR-based precipitation dataset, it is intended to be particularly advantageous in providing timely and adequate precipitation estimates when other datasets based on PMW and multisensor fusion are not available. With these considerations in mind, analysis of PDIR-Now at the daily and

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

current operational algorithm is allowed to use radar-observed convective fraction information (bright blue) as an additional ancillary parameter (see section 4a ). As expected, a better match to the reference suggests that the information on convective fraction might be a key to mitigating PMW biases seen in Fig. 1 . However, radar observations of precipitation from space are sparse, typically limited to research missions (e.g., TRMM and GPM) and intended to serve as a reference rather than a

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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

search of the a priori dataset with the 2-m air temperature (T2m) and TPW conditions, interpolated from (for near-real-time products) an operational global weather forecast model (later reprocessing of the GPROF data utilize these same quantities interpolated from the ERA model reanalysis). These three terms (surface classification index, T2m and TPW) are used to stratify the large a priori dataset for each passive MW radiometer in the constellation. Since these three terms can also be obtained at

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Shruti A. Upadhyaya, Pierre-Emmanuel Kirstetter, Jonathan J. Gourley, and Robert J. Kuligowski

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

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Chandra Rupa Rajulapati, Simon Michael Papalexiou, Martyn P. Clark, Saman Razavi, Guoqiang Tang, and John W. Pomeroy

Forecast System Reanalysis (CFSR) v2, and 5) Water and Global Change (WATCH) Forcing Data–ERA-Interim (WFDEI) version 14 August 2018. The PERSN-CDR, MSWEP, and WFDEI datasets combine information from observations, satellites, and reanalysis. The CPC uses only observations and the CFSR is purely a reanalysis product. PERSN-CDR is derived from the satellite data (Gridsat-B1), adjusted using the precipitation data from Global Precipitation Climatology Project ( Ashouri et al. 2015 ; Nguyen et al. 2018

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