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Lu Yi, Bin Yong, Junxu Chen, Ziyan Zheng, and Ling Li

assimilation. Assimilating unconventional observations of the satellite-based precipitation product based on the 4D-Var data assimilation method provides another possible way to improve the performance of a regional climate model for precipitation simulation ( Lin et al. 2015 ; Mahfouf et al. 2005 ; Pan et al. 2017 ; Yi et al. 2018b ). However, the impact of 4D-Var assimilation with precipitation observation on the accuracy of evaporation, which is also another important input in most hydrology models

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H. Gao, E. F. Wood, T. J. Jackson, M. Drusch, and R. Bindlish

: Intercomparison of water and energy budgets for five Mississippi subbasins between ECMWF reanalysis (ERA-40) and NASA Data Assimilation Office fvGCM for 1990–1999. J. Geophys. Res , 108 . 8618, doi:10.1029/2002JD003127 . Bindlish, R. , Jackson T. J. , Wood E. F. , Gao H. , Starks P. , Bosch D. , and Lakshmi V. , 2003 : Soil moisture estimates from TRMM Microwave Imager observations over the Southern United States. Remote Sens. Environ , 85 , 507 – 515 . 10.1016/S0034-4257(03)00052-X

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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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Xiang Gao, Alexander Avramov, Eri Saikawa, and C. Adam Schlosser

water content within a diameter of a few hectometers (~660 m at sea level) and to a depth of a few decimeters ( Zreda et al. 2008 ), thereby averaging soil moisture heterogeneities. Satellite remote sensing, mostly by microwave sensors, can provide near-surface soil moisture of global coverage at coarse-scale, moderate temporal resolution. Currently several satellite missions provide global surface soil moisture products, including the Soil Moisture Active Passive (SMAP) ( Entekhabi et al. 2010a

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Bong-Chul Seo, Witold F. Krajewski, and Alexander Ryzhkov

), for S-band radar. The Iowa Flood Center (IFC) generates statewide streamflow predictions based on a distributed hydrologic model known as the Hillslope Link Model, driven by a real-time radar-based precipitation product (see Krajewski et al. 2017 ). This product is a composite of seven WSR-88D radars covering Iowa and has time and space resolutions of five minutes and approximately 0.5 km, respectively. The product is not adjusted with rain gauge observations. Major QPE challenges that affect IFC

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Jianzhi Dong, Wade T. Crow, and Rolf Reichle

. (2017) . The L3 daily 0.25° precipitation product (TRMM_3B42RT_Daily) was considered as the second independent precipitation product ( Huffman et al. 2007 ). It is retrieved from a variety of low-Earth-orbit passive microwave observations using the Goddard Profiling Algorithm and produced by averaging the near-real-time, 3-hourly TRMM Multisatellite Precipitation Analysis (TMPA) 3B42RT product without gauge-based correction. Note that the most recent Integrated Multisatellite Retrievals for GPM

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Sharon E. Nicholson, Douglas Klotter, and Adam T. Hartman

merges precipitation estimates from several passive microwave (PMW) sensors with microwave-calibrated infrared-based precipitation estimates and then performs bias adjustment using monthly accumulated rain gauge analysis from GPCC ( Huffman et al. 2007 , 2015 ; Huffman and Bolvin 2014 ). TRMM 3B43 has a spatial resolution of 0.25°. Data are available for 1998–2019. IMERG V06B combines PMW satellite retrievals from whatever constellation of satellites is operating in Earth orbit, at a given time and

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Rui Wang, Xin Yan, Zhenguo Niu, and Wei Chen

thermal data, such as Scanning Multichannel Microwave Radiometer (SMMR) and Special Sensor Microwave Imager (SSM/I), are increasingly used to monitor the surface temperature of inland water bodies. They are usually Advanced Very High Resolution Radiometer (AVHRR), Along Track Scanning Radiometer (ATSR), Landsat, MODIS, and infrared photography. MODIS temperature data are the best among the current remote sensing data of temperature data, with high spatial and temporal resolution, large spectral range

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David A MacLeod, Rutger Dankers, Richard Graham, Kiswendsida Guigma, Luke Jenkins, Martin C. Todd, Augustine Kiptum, Mary Kilavi, Andrew Njogu, and Emmah Mwangi

reanalysis data The Climate Hazards Group Infrared Precipitation with Station Data (CHIRPS), are used to assess drivers of extreme rainfall, verify subseasonal forecasts and to explore the drivers of fluvial flooding. CHIRPS merges microwave and infrared satellite data with in situ gauge observations to produce daily rainfall estimates at 0.05° from 1981 to the present ( Funk et al. 2015 ). To evaluate the driving tropical modes of variability and the associated atmospheric circulation we use

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R. Garreaud

profiles measured by the Santo Domingo radiosonde (coastal site at 33°S, the only upper-air station in central Chile), finding that the intensity of the low-level zonal flow (i.e., the flow against the mountains) accounts for ~40% of the rainfall variance. The later value is comparable to its counterpart in mountainous coast of California, where Neiman et al. (2002) found that 55% of the variance of rainfall is accounted for wind variations based on hourly wind profile observations. Orographic

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