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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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Samantha H. Hartke, Daniel B. Wright, Dalia B. Kirschbaum, Thomas A. Stanley, and Zhe Li

modeling ( Maggioni et al. 2011 ; Nikolopoulos et al. 2010 ; White and Singham 2012 ). Though ensemble prediction models have been developed for some applications, flood forecasting in particular ( Cloke and Pappenberger 2009 ), assembling such ensembles can be nontrivial, while for complex physics-based models, the requisite multiple simulations may not be computationally feasible for real-time applications. An alternative approach is to directly ingest precipitation distributions generated by SMPP

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

1. Introduction It is still a challenge to provide accurate and timely flood warning in many parts of the western United States, partially due to the difficulty of having accurate estimates of heavy precipitation and associated timely reliable streamflow predictions ( Chen et al. 2020 ; White et al. 2019 ; Ralph et al. 2016 ). To improve monitoring and forecasting of precipitation, streamflow, and coastal flooding in the western United States, especially the San Francisco Bay Area (hereafter

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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

1. Introduction For many hydrological, climate, and weather forecasting applications, an important quantity is the amount of precipitation that falls on Earth’s surface over a given time interval, i.e., the surface precipitation rate. A fully global satellite-based precipitation estimate that can transition across changing Earth surface conditions and complex land–water boundaries is an important capability for proper evaluation of the precipitation produced or diagnosed in weather and climate

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

the best state vector at a given time, to have optimal initial conditions for a forecasting model ( Caya et al. 2005 ). The paper is organized as follows: in section 2 the basic physical principles of rainfall estimation from attenuation of broadcast signals are recalled; in section 3 various approaches to obtain rainfall fields from this type of measurement are illustrated. Section 4 describes the data assimilation framework based on the EnKF formulation. Sections 5 and 6 report the

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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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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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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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