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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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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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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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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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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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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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Matthew D. Cann and Allen B. White

spatial density precipitation profilers in varying geographic locations in the Northern Coast Ranges of California within the context of past studies, and to determine how frequently NBB rain contains zero bright bands and the roles that echo top height and ice may play in NBB rain intensity and orographic enhancement. In section 2 we describe the observing system used to gather the data. In section 3 we describe the techniques used to objectively categorize and analyze the data. Section 4

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Anil Kumar, Robert A. Houze Jr., Kristen L. Rasmussen, and Christa Peters-Lidard

based on observations is consistent with the available data for this storm, physical insight into the storm's dynamics and precipitation-producing processes can best be derived from a numerical model given the remote nature of the region and limited observations of the flash flood. The purpose of this paper is, therefore, to provide such insight via a simulation with the Advanced Research Weather Research and Forecasting Model (ARW-WRF, hereafter just WRF; Skamarock et al. 2008 ) coupled with NASA

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Jian Zhang, Lin Tang, Stephen Cocks, Pengfei Zhang, Alexander Ryzhkov, Kenneth Howard, Carrie Langston, and Brian Kaney

-improved identification of nonhydrometeor returns over the single-polarization (SP) radar techniques. Subsequently, the DP QPE (also called “DPR” for digital precipitation rate; https://training.weather.gov/wdtd/courses/dualpol/documents/DualPolRadarPrinciples.pdf ) had less contamination from anomalous propagation clutter and biological scatters than PPS. The DPR QPE, based on reflectivity Z , differential reflectivity Z DR , and specific differential phase K DP , provided improved precipitation estimates (less

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Jianzhi Dong, Wade T. Crow, and Rolf Reichle

. Third, statistical merging approaches are not impacted by hydrological modeling uncertainties that afflict rain/no-rain correction techniques based on data assimilation. Finally, it has the flexibility to ingest rain/no-rain estimates from all the possible sources (e.g., from both cloud temperature and data assimilation based estimates) and to effectively leverage such multisource information for improving rain/no-rain time series estimates. However, the application of any statistical merging

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