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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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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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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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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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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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Rodric Mérimé Nonki, André Lenouo, Christopher J. Lennard, Raphael M. Tshimanga, and Clément Tchawoua

P i is the value of the considered climate variable at station i . The daily streamflow data measured at Garoua port hydrometric station {latitude 7°18′N, longitude 13°E, elevation 174 m [Institut Géographique National (IGN)], station 5170106} were obtained from the Institut de Recherche pour le Développement [IRD, previously Office de la Recherche Scientifique et Technique Outre-Mer (ORSTOM)] database available at the University of Montpellier Unité Mixte de Recherche (UMR) Hydrosciences

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Sanjib Sharma, Michael Gomez, Klaus Keller, Robert E. Nicholas, and Alfonso Mejia

LISFLOOD-FP ( Bates et al. 2013 ) to generate flood inundation projections. Finally, the flood inundation projections are used to analyze flood hazards and exposure. Next, we describe the datasets, models, and techniques involved in our overall workflow ( Fig. 1 ). Fig . 1. Flowchart illustrating the general methodological approach for flood inundation risk projections. The approach starts with the climate model outputs, which are used to drive the hydrologic model and generate streamflow projections

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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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