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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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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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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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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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Li Fang, Xiwu Zhan, Jifu Yin, Jicheng Liu, Mitchell Schull, Jeffrey P. Walker, Jun Wen, Michael H. Cosh, Tarendra Lakhankar, Chandra Holifield Collins, David D. Bosch, and Patrick J. Starks

optical sensor observations for an operational finescale SMAP SM product, this study intercompares algorithms introduced in recent literature using in situ SM measurements. Three downscaling algorithms are introduced including 1) a linear regression algorithm using surface vegetation and temperature observations ( Fang et al. 2013 ), 2) a data mining technique (regression tree), using visible and thermal data ( Gao et al. 2012 ), and 3) enhancement of brightness temperature using oversampling of

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