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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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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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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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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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Nina Raoult, Catherine Ottlé, Philippe Peylin, Vladislav Bastrikov, and Pascal Maugis

(European Space Agency Climate Change Initiative Soil Moisture) combined product ( Dorigo et al. 2017 ). Soil moisture observations and retrievals can be used not only to evaluate the different processes in the model but also to calibrate the associated parameters, using for example data assimilation (DA) techniques. DA refers to the act of combining models and observations, while using the available knowledge about their respective uncertainties ( Tarantola 2005 ). This can be used to improve the

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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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Ryan Gonzalez and Christian D. Kummerow

constrained by the amount of initial snowfall at the gauge site. Parameter-Elevation Regressions on Independent Slopes Model (PRISM) is considered to be a high-quality precipitation dataset in the mountains for the contiguous United States ( Daly et al. 1994 ). PRISM uses a climate-elevation regression technique to distribute climatological precipitation data observed mostly by National Weather Service Cooperative Observer Program (COOP) precipitation gauges. Lundquist et al. (2015) showed the PRISM

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Yafang Zhong, Jason A. Otkin, Martha C. Anderson, and Christopher Hain

ALEXI ET estimates compare well with ground-based data ( Anderson et al. 1997 , 2012 ; Li et al. 2008 ). The USCRN soil observations have a national coverage and consistent measurement techniques across the United States, with measurements made at multiple soil depths from 5 to 100 cm ( Bell et al. 2013 ). It is worth mentioning that this is not a mechanism study of soil moisture–ET coupling, which may require in situ ET observations such as from the FLUXNET towers ( Baldocchi et al. 2001 ). This

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