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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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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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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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Sungmin O, Emanuel Dutra, and Rene Orth

aforementioned DSST studies. For the first time, we extend the scope of such model evaluation by considering a diverse set of state-of-the-art models. Three different models with widely varying complexities are employed, namely, physically based, conceptual, and empirical models: the Hydrology-Tiled European Centre for Medium Range Weather Forecasting (ECMWF) Scheme for Surface Exchanges over Land (HTESSEL; Balsamo et al. 2009 ), the Simple Water Balance Model (SWBM; Koster and Mahanama 2012 ; Orth and

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Huihui Zhang, Hugo A. Loáiciga, Da Ha, and Qingyun Du

modeling and exploring the associations between impact factors ( Zhang and Wang 2008 ). It is very efficient in forecasting. The genetic algorithm (GAs) is a metaheuristic technique inspired by natural evolution. The GA was introduced by Holland (1975) . It has been widely used to optimize neural networks ( Mohsen et al. 2007 ). BP applies the Levenberg–Marquardt optimization algorithm (LM). The GA improves the performance of the LM ( Zheng et al. 2019 ) by finding a suboptimal solution from a global

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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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Dazhi Xi, Ning Lin, and James Smith

is less satisfactory for regions with a large surface roughness gradient. Lu et al. (2018) studied TCRM with a different approach. Rather than investigating the estimated climatology, they compared rainfall generated from TCRM with that from the Weather Research and Forecast (WRF) Model for two historical TCs. They found that TCRM can generate rainfall features similar to those in the full physics model WRF, and when coupled with a hydrology model, TCRM can generate rainfall flood peaks as

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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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