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Trent W. Ford, Steven M. Quiring, Chen Zhao, Zachary T. Leasor, and Christian Landry

, influencing the climate on monthly to seasonal time scales ( Dirmeyer et al. 2009 ; Lorenz et al. 2010 ; Orth and Seneviratne 2014 ). Therefore, accurate soil moisture information is critical for subseasonal-to-seasonal climate prediction as well as forecasting extreme events at those time scales ( Mahanama et al. 2008 ; Guo et al. 2011 ; Ford et al. 2018 ). In addition to playing an integral role in the global climate system, soil moisture is often used as an indicator of agricultural drought

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Naoki Mizukami, Victor Koren, Michael Smith, David Kingsmill, Ziya Zhang, Brian Cosgrove, and Zhengtao Cui

using BBH Ptype data may be more clearly identified for each event. Techniques for streamflow data assimilation with the lumped model (e.g., Seo et al. 2009 ) and distributed model (e.g., Lee et al. 2011 ) are progressing. Anticipating that further analyses would lead to more consistent improvements, use of the BBH data (spatially constant or variable) for precipitation typing in real time hydrologic forecasting would be computationally feasible. Gridded data such as multisensor QPE have been used

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Yabin Gou and Haonan Chen

1. Introduction Radar quantitative precipitation estimation (QPE) is critical for extreme rainfall monitoring, forecast, and decision-making during contingent flash floods, mudslides, and debris flows. However, weather radars often suffer from complete or partial beam blockage (PBB) induced by surrounding terrains ( Giangrande and Ryzhkov 2005 ; Zhang et al. 2013 ; Chen et al. 2020 ). The PBB effect is more evident in mountainous areas and/or complex urban environments because of the

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Mengye Chen, Zhi Li, Shang Gao, Xiangyu Luo, Oliver E. J. Wing, Xinyi Shen, Jonathan J. Gourley, Randall L. Kolar, and Yang Hong

suggested to be a good approximation to the physics-based model and the computation time was reduced by 30 times ( Ghimire et al. 2013 ). It was further tested to prove the method was as efficient as other classes of models implementing HPC techniques ( Bates et al. 2010 ). Integrating hydrologic model and hydraulic models has the benefit of utilizing present-day computational resources to model dynamic representations of extreme hydrometeorological events ( Anselmo et al. 1996 ). A recent study

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Mohammed Ombadi, Phu Nguyen, Soroosh Sorooshian, and Kuo-lin Hsu

the present study. The weights w k are estimated by maximizing the log-likelihood function of the pdf in the left-hand side using historical observations. Put simply, y H and f k H are substituted for y and f k , respectively, in Eq. (2) in order to estimate w k . Several techniques such as the expectation-maximization algorithm ( Dempster et al. 1977 ) can be used to converge to a local maximum of the log-likelihood function. Here, we use a differential evolution–Markov chain (DE

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Kevin Werner, David Brandon, Martyn Clark, and Subhrendu Gangopadhyay

system, such as the El Niño–Southern Oscillation (ENSO) state, that a forecaster may have. The ESP system includes two weighting methods to account for the current climate state or forecasted climate conditions. One method is a preadjustment technique that applies shifts to the temperature and precipitation inputs based on climate forecasts. The current NWS practice is to use climate forecasts produced at the Climate Prediction Center (CPC). The second method is a post-ESP technique that allows a

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Jorge L. Peña-Arancibia, Albert I. J. M. van Dijk, Luigi J. Renzullo, and Mark Mulligan

estimates derived from satellite data or modeled through retrospective weather forecast model analysis (reanalysis) provide estimates that are independent from gauge networks. Both types of precipitation estimates have being increasingly used in hydrological applications [e.g., for reanalysis ( Dedong et al. 2007 ; Li et al. 2009 ; Yan et al. 2010; Miguez-Macho and Fan 2012 ) and for satellite ( Shrestha et al. 2008 ; Behrangi et al. 2011 ; Khan et al. 2012 ; among many others)]. Previous studies

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J. C. Albert C. Peralta, Gemma Teresa T. Narisma, and Faye Abigail T. Cruz

over 60°S–60°N from 1983 to the near-present ( Ashouri et al. 2015 ). The precipitation estimation technique combines the PERSIANN algorithm on GridSat-B1 satellite data and an artificial neural network estimation method trained using the National Centers for Environmental Prediction (NCEP) high-resolution Doppler radar data. The resulting estimate is adjusted to match the Global Precipitation Climatology Project (GPCP) monthly product version 2.2 ( Adler et al. 2003 ) when regridded to the latter

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Bong-Chul Seo, Witold F. Krajewski, and Alexander Ryzhkov

1. Introduction Quantitative precipitation estimation (QPE) using weather radar has become common and important for many meteorological and hydrological applications such as severe weather warnings and flood forecasting and management (e.g., Zhang et al. 2016 ; Krajewski et al. 2017 ). Since its initial deployment in the early 1990s, the QPE algorithm for the U.S. Weather Surveillance Radar-1988 Doppler (WSR-88D) network has evolved according to its hardware and polarimetric upgrades (e

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Luis E. Pineda and Patrick Willems

December–May daily rainfall over this region. Therefore, an emerging question is whether such skillful seasonal forecasts can be translated into regionwide predictions of daily rainfall statistics, which, if anticipated with a useful lead time, can be used to warn the likelihood of high-impact weather (floods and droughts) by extending hydrological forecasting with rainfall–runoff hydraulic models to longer times. In the PAEP, traditional approaches to seasonal hydroclimatic forecasting have only

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