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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

fields, including surface (0–5 cm) and root-zone (0–100 cm) soil moisture, soil temperature, and surface fluxes. The L4_SM product also provides important data assimilation diagnostics, including the assimilated Tb observations and corresponding model forecasts. Here, we use 3-hourly instantaneous surface and root-zone soil moisture and brightness temperature from the L4_SM “analysis-update” files ( Reichle et al. 2018a ). We further use 3-hourly time-average total runoff data (including surface

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Zhe Zhang, Youcun Qi, Donghuan Li, Ziwei Zhu, Meilin Yang, Nan Wang, Yin Yang, and Qiyuan Hu

QPE. Furthermore, hydrological disasters such as flood, debris flow, and urban waterlogging are usually attributed to the heavy precipitation caused by strong convection. Therefore, accurately identifying convective precipitation is practically helpful for hydrological forecasting. Previous studies have proposed different algorithms to discriminate convective and stratiform precipitation. Steiner et al. (1995 , hereafter SHY95) proposed a convection and stratiform separation algorithm by

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Martin G. De Kauwe, Christopher M. Taylor, Philip P. Harris, Graham P. Weedon, and Richard. J. Ellis

failure or screening for pixel contamination by cloud and/or dust. One solution might be to gap-fill the time series using an interpolation technique; however, this can result in bias because of the suppression of high-frequency components ( Schulz and Mudelsee 2002 ). Alternatively, a model may be used to estimate missing data points, using a sequential filtering algorithm such as a Kalman filter to update model forecasts when observations are available. However, this solution requires the necessary

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Wenyi Xie, Xiankui Zeng, Dongwei Gui, Jichun Wu, and Dong Wang

(MODFLOW-2005). USGS Techniques and Methods 6-D1, 240 pp., https://pubs.usgs.gov/tm/tm6d1/ . 10.3133/tm6D1 Marsh , P. , 1999 : Snowcover formation and melt: Recent advances and future prospects . Hydrol. Processes , 13 , 2117 – 2134 , https://doi.org/10.1002/(SICI)1099-1085(199910)13:14/15<2117::AID-HYP869>3.0.CO;2-9 . 10.1002/(SICI)1099-1085(199910)13:14/15<2117::AID-HYP869>3.0.CO;2-9 Martinec , J. , 1975 : Snowmelt runoff model for stream flow forecasts . Hydrol. Res. , 6 , 145 – 154

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James Cleverly, Chao Chen, Nicolas Boulain, Randol Villalobos-Vega, Ralph Faux, Nicole Grant, Qiang Yu, and Derek Eamus

partitioning ET into soil and plant components for olive orchards in a semi-arid region . Agric. Water Manage. , 97 , 1769 – 1778 , doi:10.1016/j.agwat.2010.06.009 . Hutley, L. B. , Leuning R. , Beringer J. , and Cleugh H. A. , 2005 : The utility of the eddy covariance techniques as a tool in carbon accounting: Tropical savanna as a case study . Aust. J. Bot. , 53 , 663 – 675 , doi:10.1071/BT04147 . Isaac, P. R. , Leuning R. , Hacker J. M. , Cleugh H. A. , Coppin P. A. , Denmead

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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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