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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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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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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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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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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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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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Emily A. Slinskey, Paul C. Loikith, Duane E. Waliser, Bin Guan, and Andrew Martin

of AR frequency, physical characteristics, and impacts across the CONUS summarized over the seven NCA regions. AR detection is based on IVT magnitude thresholds, as well as a number of geometric and directional criteria following the technique described in Guan and Waliser (2015) and updated in Guan et al. (2018) . Seasonal climatologies of AR frequency across the CONUS reveal ARs in the Northwest and Southwest are most common in the winter and autumn ( Figs. 2a,d ). Although considerably less

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Catherine E. Finkenbiner, Stephen P. Good, Scott T. Allen, Richard P. Fiorella, and Gabriel J. Bowen

techniques have captured the spatial and temporal patterns of precipitation characteristics ( Kuhn et al. 2007 ; Gao et al. 2018 ), temporally downscale precipitation datasets ( Gyasi-Agyei 2011 ; So et al. 2017 ), to forecast precipitation events ( Bárdossy and Pegram 2009 ; Khedun et al. 2014 ) and across other hydrological disciplines (e.g., temperature and rainfall dynamics ( Cong and Brady 2012 ; Schölzel and Friederichs 2008 ), extreme-value stochastic rainfall events ( Kuhn et al. 2007 ; Laux

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Rasool Porhemmat, Heather Purdie, Peyman Zawar-Reza, Christian Zammit, and Tim Kerr

the 90th percentile at each site over the period of observation. In the case where large snowfall events were associated with snowstorms longer than 24 h, analysis was conducted for the total period of the storm rather than individual snowfall days. The meteorological fields were obtained from the European Centre for Medium-Range Weather Forecasts (ECMWF) interim reanalysis (ERA-Interim) data ( Dee et al. 2011 ). Meteorological observations on land and ocean are assimilated into numerical weather

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Joel R. Norris, F. Martin Ralph, Reuben Demirdjian, Forest Cannon, Byron Blomquist, Christopher W. Fairall, J. Ryan Spackman, Simone Tanelli, and Duane E. Waliser

layer wind obtained from the Global Forecast System to fill time and space gaps between satellite swaths ( Wimmers and Velden 2011 ). Morphed Integrated Microwave Imagery was used only to characterize the synoptic overview and not for water budget calculations. The Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2) ( Gelaro et al. 2017 ), provided information on the large-scale synoptic environment in which the atmospheric river occurred. The MERRA-2 reanalysis

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