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Maxime Turko, Marielle Gosset, Modeste Kacou, Christophe Bouvier, Nanee Chahinian, Aaron Boone, and Matias Alcoba

1. Introduction During the last 10 years, rainfall measurement from commercial microwave link (CML) network has gradually emerged as a useful complement to traditional rainfall measurement based on gauges, weather radar or satellites. Uijlenhoet et al. (2018) and Chwala and Kunstmann (2019) provide a good review of the state of the art and the research developed since the pioneering work of Messer et al. (2006) and Leijnse et al. (2007b) . The CML technique is based on the analysis of

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Fidele Karamage, Yuanbo Liu, and Yongwei Liu

monthly runoff simulations. A consistent time series of gridded monthly calibrated runoff data was obtained based on a second-order polynomial regression (PR) ( BSL 2018 ; Billo 2007 ; Morrison 2015 ) between monthly downscaled (gridded) observed and CN-based runoff simulated data. PR includes explicit mathematical formulations and is acceptable for streamflow forecasting ( Rezaie-Balf and Kisi 2018 ; Giustolisi and Savic 2006 ). The statistical method used for runoff calibration was evaluated

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Pravat Jena, Sourabh Garg, and Sarita Azad

subsequently brings forth the importance of the evaluation of gridded data. Further, satellite estimates are currently being used as an alternate source of data for monitoring and validation purposes since they are available at high spatiotemporal scales as models ( Dinku et al. 2014 ). Some of these products are, Climate Prediction Center Morphing technique (CMORPH), Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks–Climate Data Record (PERSIANN-CDR), Tropical

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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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