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Yanhong Gao, Fei Chen, and Yingsha Jiang

were generated, such as NOAA’s Climate Prediction Center morphing technique (CMORPH) ( Joyce et al. 2004 ), the Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks–Climate Data Record (PERSIANN-CDR) ( Ashouri et al. 2015 ) products, and the Asian Precipitation–Highly Resolved Observational Data Integration Toward Evaluation of Water Resources (APHRODITE) ( Yatagai et al. 2009 , 2012 ). Satellite remote sensing provides precipitation information for a broader

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Julie M. Thériault, Nicolas R. Leroux, and Roy M. Rasmussen

.g., Groisman et al. 1991 ; Yang et al. 1995 ; Thériault et al. 2012 ) have shown snowfall undercatch to increase with increasing wind speed as a result. In addition, observations show a significant variability in undercatch for a given wind speed due to the wide variety of snow crystal types present in the atmosphere ( Yang et al. 1995 ), as well as with snowfall intensity ( Colli et al. 2020 ). Accurately measuring snowfall precipitation is of importance for hydrological forecasting, water management

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Hua Su, Robert E. Dickinson, Kirsten L. Findell, and Benjamin R. Lintner

. Fig . 16. Flowchart of the mechanisms that explain the observed negative correlation between April snow depth and early warm-season precipitation. Our findings demonstrate that spring snow conditions may contribute to forecasting the early warm-season precipitation over northern continental interior regions. Such snow datasets could become increasingly available via enhanced observational capacity and improved data assimilation techniques ( De Lannoy et al. 2010 ; Su et al. 2010 ). However, the

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Sho Kawazoe and William J. Gutowski Jr.

-rain-producing mesoscale convective systems . Mon. Wea. Rev. , 133 , 961 – 976 . Schumacher, R. S. , and Johnson R. H. , 2006 : Characteristics of U.S. extreme rain events during 1999–2003 . Wea. Forecasting , 21 , 69 – 85 . Shepard, D. S. , 1984 : Computer mapping: The SYMAP interpolation algorithm. Spatial Statistics and Models, G. L Gaile and C. J. Willmott, Eds., D. Reidel, 133–145. von Storch, H. , Langenberg H. , and Feser F. , 2000 : A spectral nudging technique for dynamical downscaling

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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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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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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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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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