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Abby Stevens, Rebecca Willett, Antonios Mamalakis, Efi Foufoula-Georgiou, Alejandro Tejedor, James T. Randerson, Padhraic Smyth, and Stephen Wright

1. Introduction Seasonal prediction of regional hydroclimate is typically based on deterministic physical models or statistical techniques, yet both approaches exhibit limited predictive ability ( Wang et al. 2009 ; National Academies of Sciences, Engineering, and Medicine 2016 ). Precipitation predictions based on deterministic physical models (regional climate models) exhibit high uncertainty due to imperfect physical conceptualizations, sensitivity to initial and boundary conditions, and

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

correlated to regional climate conditions. Subsequent versions of GPROF addressed this by constraining the TRMM (ocean only) GPROF retrievals by two environmental parameters, namely total precipitable water (TPW) and sea surface temperature (SST) ( Kummerow et al. 2011 ). Moving forward to GPM, these same techniques were adapted to land surfaces, by replacing the SST with the 2 m air temperature commonly available from forecast and reanalysis models. In a series of papers describing and testing the Cloud

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Giuseppe Mascaro

several locations (e.g., Papalexiou and Koutsoyiannis 2013 ; Blanchet et al. 2016 ). If historical rainfall records are available at multiple sites, regional IDF curves are often generated by (i) spatially interpolating i ( T R , τ ) or parameters of the statistical distributions from local or at-site estimations, or (ii) applying regionalization techniques that merge rain gauges into homogeneous regions to increase robustness in the estimate of the statistical distribution parameters ( Hosking

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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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Efi Foufoula-Georgiou, Clement Guilloteau, Phu Nguyen, Amir Aghakouchak, Kuo-Lin Hsu, Antonio Busalacchi, F. Joseph Turk, Christa Peters-Lidard, Taikan Oki, Qingyun Duan, Witold Krajewski, Remko Uijlenhoet, Ana Barros, Pierre Kirstetter, William Logan, Terri Hogue, Hoshin Gupta, and Vincenzo Levizzani

) forecasting (the gap between weather forecasts and seasonal climate predictions) using models and observations and assessment of uncertainty propagation to impact studies such as floods, droughts and ecological changes. IPC12 also aimed to provide a forum to explore new data analytic and machine learning (ML) methodologies, taking advantage of the unprecedented explosion of Earth observations from space and climate model outputs, for improved estimation and prediction. It also brought together scientists

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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

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

. Combining both direct (gauges) and remote (radar/radiometer) measurement techniques, using ground and in-orbit observations complemented by the state-of-the-art atmosphere simulations, the GPM constellation offers full global coverage of rain and snow every 30 min at a resolution of only 0.1° and a latency of only a few hours. Freely available precipitation products are implemented across a spectrum of decision-making scientific tools, ranging from hydrology to world health. To ensure user demands for

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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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