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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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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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Michael Notaro, Yafang Zhong, Pengfei Xue, Christa Peters-Lidard, Carlos Cruz, Eric Kemp, David Kristovich, Mark Kulie, Junming Wang, Chenfu Huang, and Stephen J. Vavrus

Regional Climates for Impacts Studies” regional climate model, Zhang et al. (2020) projected that wintertime precipitation in the Great Lakes Basin would increase during this century. The Weather Research and Forecasting (WRF; Skamarock et al. 2008 ) Model is a commonly used regional climate model for the Great Lakes Basin. According to Shi et al. (2010) , the nested WRF Model with 1-km grid spacing accurately simulated snowfall and cloud patterns from Canadian snowstorms. Wright et al. (2013

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Kichul Jung, Taha B. M. J. Ouarda, and Prashanth R. Marpu

) have been adopted generally in a wide range of hydrological issues, such as rainfall runoff modeling, hydrological forecasting, and flood quantile estimation in regional frequency analysis ( Daniell 1991 ; Muttiah et al. 1997 ; Govindaraju 2000 ; Luk et al. 2001 ; Dawson and Wilby 2001 ; Shu and Burn 2004 ; Dawson et al. 2006 ; Shu and Ouarda 2007 ; Chokmani et al. 2008 ; Turan and Yurdusev 2009 ; Besaw et al. 2010 ; Aziz et al. 2014 ). ANN models in RFA can provide the functional

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Andrea Manrique-Suñén, Annika Nordbo, Gianpaolo Balsamo, Anton Beljaars, and Ivan Mammarella

, because adding a new surface type to the system does not imply high computational cost. Limitations of the tiling concept are related to the imposition of a horizontally well-mixed atmosphere above the different tiles at a certain height (blending height), which might not be valid for heterogeneities with large horizontal length scales ( Koster and Suarez 1992 ). The Hydrology Tiled European Centre for Medium-Range Weather Forecasts (ECMWF) Scheme for Surface Exchanges (HTESSEL) is the land surface

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Seokhyeon Kim, Alfonso Anabalón, and Ashish Sharma

3.3a uses various data as 1) European Centre for Medium-Range Weather Forecasts (ECMWF) interim reanalysis (ERA-Interim) data for radiation and air temperature; 2) Multi-Source Weighted-Ensemble Precipitation v1.0 for precipitation; 3) Global Snow Monitoring for Climate Research (GLOBSNOW) L3A v2 and National Snow and Ice Data Center (NSIDC) v01 for snow water equivalents; and 4) Land Parameter Retrieval Model–based vegetation optical depth. 3) GLDAS Four Global Land Data Assimilation System

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