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

they need the integration with other systems for applications requiring high quantitative precisions, or spatial scales of about 1 km or less, or measurement updated timely and more frequently than 5 min. These are, for instance, desirable temporal and spatial resolutions for nowcasting purposes in hydrology ( WMO 2017 ). This scenario suggests that new measurement techniques and new data merging strategies are needed to improve the rainfall estimation at local scales. Nonconventional techniques

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

algorithm (GPROF; Kummerow et al. 2001 , 2015 ), the Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks (PERSIANN) family of products ( Ashouri et al. 2015 ; Hsu et al. 1997 ), and “cloud morphing”-based techniques such as the CPC morphing technique (CMORPH; Joyce et al. 2004 ; Xie et al. 2017 ), JAXA’s Global Satellite Mapping of Precipitation (GsMAP; Kubota et al. 2007 ), and NASA’s Integrated Multisatellite Retrievals for GPM (IMERG; Huffman et al. 2018

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

progress in GEO sensor technologies along with the advancements in machine learning (ML) techniques, such as support vector machines, random forests, artificial neural network (ANN), deep learning, the new generation of precipitation retrieval algorithms must outperform the current operational products ( Meyer et al. 2016 ; Kuligowski et al. 2016 ; Sadeghi et al. 2019 ; Upadhyaya et al. 2020 ). In recent years, many studies have been conducted to utilize the generation sensor information to improve

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

phenomena in locations and at scales not previously possible. SMPPs use algorithms that merge passive microwave and infrared sensing data from multiple satellites (e.g., Kidd and Levizzani 2011 ; Kidd and Huffman 2011 ; Tapiador et al. 2012 ; Wright 2018 ). Commonly used SMPPs include the TRMM Multisatellite Precipitation Analysis (TMPA; Huffman et al. 2007 ), the Climate Prediction Center (CPC) morphing technique (CMORPH; Joyce et al. 2004 ), and the Precipitation Estimation from Remotely Sensed

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

products [e.g., Climate Prediction Center (CPC) unified precipitation estimates, Global Precipitation Climatology Center (GPCC) precipitation dataset, NCEP–NCAR reanalysis data] have become available at various spatial and temporal resolutions based on different data sources (e.g., ground observations, satellites, radar, reanalysis) and data merging techniques. While such datasets are useful to investigate the spatial and temporal behavior in global precipitation ( Fischer and Knutti 2014 ; Ghosh 2012

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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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