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Yixin Wen, Qing Cao, Pierre-Emmanuel Kirstetter, Yang Hong, Jonathan J. Gourley, Jian Zhang, Guifu Zhang, and Bin Yong

of the range-dependent error in radar-rainfall estimates due to the vertical profile of reflectivity . J. Hydrol. , 402 , 306 – 316 . Lakshmanan, V. , Fritz A. , Smith T. , Hondl K. , and Stumpf G. J. , 2007 : An automated technique to quality control radar reflectivity data . J. Appl. Meteor. Climatol. , 46 , 288 – 305 . Maddox, R. , Zhang J. , Gourley J. J. , and Howard K. , 2002 : Weather radar coverage over the contiguous United States . Wea. Forecasting , 17

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Trent W. Ford, Liang Chen, and Justin T. Schoof

example of what was described by Cohen (2016) as “weather whiplash,” the evolution from one climate extreme to one of the opposite sign in a relatively short time period. Previous studies have documented transitions in precipitation extremes, herein referred to as simply transitions, in many regions globally using a wide variety of statistical and modeling techniques (e.g., Ji et al. 2018 ; Swain et al. 2018 ; Chen et al. 2019 ). Christian et al. (2015) found that annual precipitation extremes

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Mohammad Reza Ehsani, Ali Behrangi, Abishek Adhikari, Yang Song, George J. Huffman, Robert F. Adler, David T. Bolvin, and Eric J. Nelkin

and over the ocean. Besides, the traditional gauge measurement techniques for snowfall measurement exhibit high uncertainties and errors; correction factors for wind-induced undercatch can lead to uncertainties as high as 100%, especially in sparsely gauged regions of high latitudes ( Behrangi et al. 2019 ; Fuchs et al. 2001 ; Goodison et al. 1998 , Kidd et al. 2017 ; Panahi and Behrangi 2019 ; Yang et al. 2005 ). Precipitation retrieval from satellite data is an important topic and has been

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Alyssa M. Stansfield, Kevin A. Reed, Colin M. Zarzycki, Paul A. Ullrich, and Daniel R. Chavas

) provides the 6-hourly observed TC track data for the same time period as the model simulations, 1985–2014 ( Fig. 3 , top). Additionally, 6-hourly 10-m wind, sea level pressure, and geopotential height data from the fifth major global reanalysis produced by the European Centre for Medium-Range Weather Forecasts (ERA5; Copernicus Climate Change Service 2017 ) with 31-km horizontal grid spacing for 1985–2014 are used for TC track ( Fig. 3 , bottom), size, and precipitation analyses. Since reanalysis is

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Jefferson S. Wong, Xuebin Zhang, Shervan Gharari, Rajesh R. Shrestha, Howard S. Wheater, and James S. Famiglietti

-Interim (WFDEI) was developed to provide datasets of subdaily (3-hourly) and daily meteorological data, with global coverage at 0.5° spatial resolution (~50 km) from 1979 to 2012 ( Weedon et al. 2014 ). WFDEI has been updated to provide datasets up to 2016. Using the same methodology as WATCH ( Weedon et al. 2011 ), WFDEI was constructed based on the European Centre for Medium-Range Weather Forecasts (ECMWF) interim reanalysis product ( Dee et al. 2011 ), combined with the Climatic Research Unit (CRU

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Yanan Meng, Jianhua Sun, Yuanchun Zhang, and Shenming Fu

-stationary satellite data and CMORPH (the Climate Prediction Center morphing technique) precipitation data. Feng et al. (2019) obtained the characteristics of MCSs in the United States using satellite, precipitation, and radar data, and pointed out that long-lived and intense MCSs account for over 50% of warm season precipitation in the Great Plains. Some studies have examined the variations in the cloud parameters, precipitation, and synoptic circulations of MCSs and have reported the relationships among those

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F. Chen, W. T. Crow, L. Ciabatta, P. Filippucci, G. Panegrossi, A. C. Marra, S. Puca, and C. Massari

1. Introduction Satellite-based precipitation estimates (SPE) are increasingly being applied to important environmental applications such as numerical weather prediction, flood forecasting, and agricultural drought monitoring. A potential SPE of interest is the H23 gridded precipitation product generated by the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) Satellite Application Facility on Support to Operational Hydrology and Water Management (H-SAF). The H

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Cheng Tao, Yunyan Zhang, Qi Tang, Hsi-Yen Ma, Virendra P. Ghate, Shuaiqi Tang, Shaocheng Xie, and Joseph A. Santanello

1. Introduction Accurate representations of the land–atmosphere (LA) coupling processes are critical for weather forecasts and climate predictions ( Seneviratne et al. 2006 , 2010 ; Santanello et al. 2018 ). A lack of quantitative understanding of the nature and characteristics of LA coupling remains (e.g., Betts 2004 ; Ek and Holtslag 2004 ; Guillod et al. 2014 ; Santanello et al. 2018 ), owing to the multivariate and multiscale interactive processes between the land surface, planetary

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

variations of soil moisture is essential for climate predictability on seasonal to annual time scales ( van den Hurk et al. 2012 ; Sospedra-Alfonso and Merryfield, 2018 ), flood and drought forecasts ( Sheffield et al. 2014 ; Wanders et al. 2014 ), and climate impact studies ( Seneviratne et al. 2010 ). Soil moisture can be estimated in three ways: in situ measurements, satellite remote sensing, and model-based simulations. Each of these techniques has its own specific properties and limitations. In

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Jiali Ju, Heng Dai, Chuanhao Wu, Bill X. Hu, Ming Ye, Xingyuan Chen, Dongwei Gui, Haifan Liu, and Jin Zhang

first term on the right-hand side is the partial variance contributed by θ i and the second term represents the partial variance caused by the model inputs except θ i . The first-order sensitivity index is thus defined as S i = V θ i ⁡ [ E θ ~ i ⁡ ( Δ | θ i ) ] / V ⁡ ( Δ ) . This index measures the percentage of output uncertainty contributed by θ i and estimates its relative importance compared to other uncertain inputs. This variance decomposition technique has been recursively applied by

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