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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; ) 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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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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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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Wenyi Xie, Xiankui Zeng, Dongwei Gui, Jichun Wu, and Dong Wang

(MODFLOW-2005). USGS Techniques and Methods 6-D1, 240 pp., . 10.3133/tm6D1 Marsh , P. , 1999 : Snowcover formation and melt: Recent advances and future prospects . Hydrol. Processes , 13 , 2117 – 2134 ,<2117::AID-HYP869>3.0.CO;2-9 . 10.1002/(SICI)1099-1085(199910)13:14/15<2117::AID-HYP869>3.0.CO;2-9 Martinec , J. , 1975 : Snowmelt runoff model for stream flow forecasts . Hydrol. Res. , 6 , 145 – 154

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James Cleverly, Chao Chen, Nicolas Boulain, Randol Villalobos-Vega, Ralph Faux, Nicole Grant, Qiang Yu, and Derek Eamus

partitioning ET into soil and plant components for olive orchards in a semi-arid region . Agric. Water Manage. , 97 , 1769 – 1778 , doi:10.1016/j.agwat.2010.06.009 . Hutley, L. B. , Leuning R. , Beringer J. , and Cleugh H. A. , 2005 : The utility of the eddy covariance techniques as a tool in carbon accounting: Tropical savanna as a case study . Aust. J. Bot. , 53 , 663 – 675 , doi:10.1071/BT04147 . Isaac, P. R. , Leuning R. , Hacker J. M. , Cleugh H. A. , Coppin P. A. , Denmead

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