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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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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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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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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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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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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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Zhe Zhang, Youcun Qi, Donghuan Li, Ziwei Zhu, Meilin Yang, Nan Wang, Yin Yang, and Qiyuan Hu

QPE. Furthermore, hydrological disasters such as flood, debris flow, and urban waterlogging are usually attributed to the heavy precipitation caused by strong convection. Therefore, accurately identifying convective precipitation is practically helpful for hydrological forecasting. Previous studies have proposed different algorithms to discriminate convective and stratiform precipitation. Steiner et al. (1995 , hereafter SHY95) proposed a convection and stratiform separation algorithm by

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Ning Zhang, Steven M. Quiring, and Trent W. Ford

, the longer latency is problematic for applications requiring more rapid updates, including flash flood forecasting and field condition monitoring for agriculture. In addition, soil moisture products based entirely on remote sensing observations do not represent soil moisture conditions in the primary root zone. Although we do not examine root zone soil moisture in this study, the methods are easily applicable for blending root zone soil moisture from in situ and model sources. Last, many blended

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Martin G. De Kauwe, Christopher M. Taylor, Philip P. Harris, Graham P. Weedon, and Richard. J. Ellis

failure or screening for pixel contamination by cloud and/or dust. One solution might be to gap-fill the time series using an interpolation technique; however, this can result in bias because of the suppression of high-frequency components ( Schulz and Mudelsee 2002 ). Alternatively, a model may be used to estimate missing data points, using a sequential filtering algorithm such as a Kalman filter to update model forecasts when observations are available. However, this solution requires the necessary

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