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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

1. Introduction Soil moisture is important because it connects the land surface water, energy, and carbon cycles ( Seneviratne et al. 2010 ). Accurate, long-term, global observations of soil moisture conditions are critical for a wide range of science investigations and applications ( Balsamo et al. 2018 ; Santanello et al. 2018 ). A variety of global satellite soil moisture data products are available based on microwave observations, including from the Advanced Microwave Scanning Radiometer

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Janice L. Bytheway, Mimi Hughes, Kelly Mahoney, and Rob Cifelli

, consecutive images from geostationary infrared (GEO-IR) satellite data are used to estimate motion vectors and changes of size of precipitation features. Using this information, time-weighted interpolation is performed both forwards and backward in time between LEO overpasses, determining the shape and location of the precipitation field during periods with no passive microwave observations. This is referred to as the “morphing” process ( Joyce et al. 2004 ). In version 1.0, the entire CMORPH dataset is

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Yalei You, Nai-Yu Wang, Ralph Ferraro, and Patrick Meyers

1. Introduction Passive microwave observations have more direct physical relation with the hydrometers in the atmosphere relative to infrared and visible observations, which capture the cloud-top features ( Barrett and Beaumont 1994 ; Petty 1995 ; Kidd and Levizzani 2011 ). Therefore, precipitation estimates from passive microwave radiometers are more accurate and are essential for accuracy of satellite-based, high-resolution, near-global precipitation datasets ( Hou et al. 2014 ; Yong et al

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Trent W. Ford, Steven M. Quiring, Chen Zhao, Zachary T. Leasor, and Christian Landry

. Cherneski , J. L’Heureux , and T. Hawden , 2015 : Monitoring agricultural risk in Canada using L-band passive microwave soil moisture from SMOS . J. Hydrometeor. , 16 , 5 – 18 , https://doi.org/10.1175/JHM-D-14-0039.1 . 10.1175/JHM-D-14-0039.1 Chen , F. , and Coauthors , 1996 : Modeling of land surface evaporation by four schemes and comparison with FIFE observations . J. Geophys. Res. , 101 , 7251 – 7268 , https://doi.org/10.1029/95JD02165 . 10.1029/95JD02165 Chen , F. , and

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Viviana Maggioni, Humberto J. Vergara, Emmanouil N. Anagnostou, Jonathan J. Gourley, Yang Hong, and Dimitrios Stampoulis

infrared geostationary satellite imagery and used microwave data for training a neural network that assigns precipitation estimates to the infrared temperature. The PERSIANN-CCS dataset, produced by the University of California, Irvine, covers 50°S–50°N and provides estimates of rainfall at spatial and temporal resolutions of 0.04° × 0.04° and 30 min, respectively. Ground observations of runoff measured at the outlets of each of the five basins are also employed in the study and considered as a

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Yanbo Nie and Jianqi Sun

techniques. Most gauge-based products cover longer periods than satellite-based products. Satellite-based HRPPs provide high-resolution observations in regions with scarce ground observations of precipitation, such as desserts, oceans, and mountainous regions, which give these products an edge over gauge-based HRPPs. However, in contrast to gauge-based products, which are derived from directly measured precipitation, the accuracy of remotely sensed HRPPs is affected by some sources of uncertainties, such

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Anne Felsberg, Gabriëlle J. M. De Lannoy, Manuela Girotto, Jean Poesen, Rolf H. Reichle, and Thomas Stanley

as defined above. b. Soil moisture and water storage estimates 1) Satellite data The study period in this paper is constrained by the availability of landslide data and satellite observations from GRACE and L-band microwave missions, as illustrated in Fig. 2 . Because of the complementarity of SMOS and GRACE and their relatively long period of overlap, this paper will focus on SMOS and GRACE from 1 January 2011 through 31 July 2016 (hereafter referred to as the SMOS-GRACE study period). We

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Nina Raoult, Catherine Ottlé, Philippe Peylin, Vladislav Bastrikov, and Pascal Maugis

.2043032 Kolassa , J. , R. Reichle , and C. S. Draper , 2017 : Merging active and passive microwave observations in soil moisture data assimilation . Remote Sens. Environ. , 191 , 117 – 130 , https://doi.org/10.1016/j.rse.2017.01.015 . 10.1016/j.rse.2017.01.015 Komma , J. , G. Blöschl , and C. Reszler , 2008 : Soil moisture updating by ensemble Kalman filtering in real-time flood forecasting . J. Hydrol. , 357 , 228 – 242 , https://doi.org/10.1016/j.jhydrol.2008.05.020 . 10.1016/j

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E. J. Barton, C. M. Taylor, C. Klein, P. P. Harris, and X. Meng

observations (lower uncertainties, less missing data) compared to SM, the bulk of our analysis uses LST. Previous works (e.g., T11 ; T15 ) have used LST anomalies (LSTAs) as a proxy for surface wetness conditions. Locally positive (negative) LSTAs are indicative of locally drier (wetter) conditions compared to the surroundings. This study employs LSTA observations from the 10-km AMSR2 downscaled LPRM Level 2 product (overpass time 1530 LT, UTC + 8; de Jeu and Owe 2014 ; Owe et al. 2008 ). The microwave

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Yali Luo, Weimiao Qian, Renhe Zhang, and Da-Lin Zhang

borders of the provinces stationed around the analysis domain, along with Yangtze, Huai, and Yellow rivers (green lines) and the location of Wangjiaba reservoir (red cross). Shadings represent terrain heights. However, there has been the lack of such high-resolution precipitation observations to reveal detailed spatial and temporal distributions of the mei-yu precipitation in east China, and similarly for heavy precipitating MCSs occurring elsewhere around the world. Clearly, such high-resolution data

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