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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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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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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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M. H. J. van Huijgevoort, P. Hazenberg, H. A. J. van Lanen, A. J. Teuling, D. B. Clark, S. Folwell, S. N. Gosling, N. Hanasaki, J. Heinke, S. Koirala, T. Stacke, F. Voss, J. Sheffield, and R. Uijlenhoet

cells in total) were considered by the models. Model forcing was provided by the WATCH forcing data (WFD) developed by Weedon et al. (2011) . The WFD consist of gridded time series of meteorological variables (e.g., rainfall, snowfall, temperature, and wind speed) both on a subdaily and daily basis for 1958–2001 with a resolution of 0.5° × 0.5°. The WFD originate from modification (bias correction and downscaling) of the 40-yr European Centre for Medium-Range Weather Forecasts (ECMWF) Re

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Eli J. Dennis and Ernesto Hugo Berbery

use of a soil texture map paired with a lookup table is a practical solution for enabling large-scale land surface modeling and a standard practice at operational forecast centers either coupled or uncoupled. The lookup table is an important constraint since it assumes a uniform hydraulic behavior for each soil category anywhere in the world. In recent years, the soil sciences community has been working intensely to advance the development of pedotransfer functions (PTFs) that should improve

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Rolf H. Reichle, Qing Liu, Joseph V. Ardizzone, Wade T. Crow, Gabrielle J. M. De Lannoy, Jianzhi Dong, John S. Kimball, and Randal D. Koster

fields, including surface (0–5 cm) and root-zone (0–100 cm) soil moisture, soil temperature, and surface fluxes. The L4_SM product also provides important data assimilation diagnostics, including the assimilated Tb observations and corresponding model forecasts. Here, we use 3-hourly instantaneous surface and root-zone soil moisture and brightness temperature from the L4_SM “analysis-update” files ( Reichle et al. 2018a ). We further use 3-hourly time-average total runoff data (including surface

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