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Pradeep V. Mandapaka and Edmond Y. M. Lo

The GPM mission consists of the Core Observatory ( CO ) deployed in February 2014 by NASA and JAXA, and a constellation of satellites from partner agencies. The CO mainly carries the GPM Microwave Imager and the Dual-Frequency Precipitation Radar, and provides high-resolution information about precipitation intensity, type, and micro and macro structures (e.g., Hou et al. 2014 ; Skofronick-Jackson et al. 2017 ). Further, the CO serves as a reference to intercalibrate observations from a

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Akarsh Asoka and Vimal Mishra

observations from more than 6500 gauge stations and the inverse distance weighting method ( Pai et al. 2014 ). Gridded precipitation from the IMD is widely used for hydroclimatic studies in India ( Mishra et al. 2014 ; Shah and Mishra 2016 ). We obtained monthly Terrestrial Water Storage Anomaly (TWSA) at 0.25° spatial resolution from Gravity Recovery and Climate Experiment (GRACE), which is available from Centre for Space Research (CSR GRACE RL06 Mascon Solutions) for 2002 to 2016 ( Save 2019 ; Save et

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Satish Bastola and Vasubandhu Misra

Prediction Center's morphing method (CMORPH) and the National Centers for Environmental Prediction (NCEP) Stage IV, are selected. CMORPH ( Joyce et al. 2004 ) combines estimates from low-orbiter satellite microwave observations with geostationary infrared data to produce global precipitation analyses at 3-hourly intervals and at 0.25° horizontal-grid resolution. CMORPH data are available over the domain of 60°S–60°N. Similarly, hourly data from NCEP Stage IV, produced by the National Weather Service

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Yanhong Gao, Fei Chen, and Yingsha Jiang

precipitation datasets ( Sun et al. 2018 ), such as interpolations based on station records, combining satellite and station observations, and climate modeling. However, most stations in the TP are located in the central and eastern part, and there is no single station situated above 4800 m in the western TP. Furthermore, precipitation records at high altitudes are always somehow problematic due to various limitations, such as precipitation under catchments ( Yang et al. 1998 ; Scaff et al. 2015 ; Pan et

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Wen Li Zhao, Guo Yu Qiu, Yu Jiu Xiong, Kyaw Tha Paw U, Pierre Gentine, and Bao Yu Chen

. C. Izaurralde , D. Ort , A. M. Thomson , and D. W. Wolfe , 2011 : Climate impacts on agriculture: Implications for crop production . Agron. J. , 103 , 351 – 370 , . 10.2134/agronj2010.0303 Hu , G. , and L. Jia , 2015 : Monitoring of evapotranspiration in a semi-arid inland river basin by combining microwave and optical remote sensing observations . Remote Sens. , 7 , 3056 – 3087 , . 10.3390/rs

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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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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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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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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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