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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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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

Open access
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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Rebecca Gugerli, Marco Gabella, Matthias Huss, and Nadine Salzmann

radars improves the coarse distribution of rain gauge estimates while the higher absolute accuracy of rain gauges improves the overall precipitation estimates. Rain gauge adjustment of radar-derived estimates was already attempted in the 1970s and 1980s (e.g., Cain and Smith 1976 ; Koistinen and Puhakka 1981 ; Collier et al. 1983 ; Collier 1986 ). Several refined gauge-adjustment techniques have since confirmed the improvement of the combination of radar and rain gauge networks ( Koistinen et al

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Hanqing Chen, Bin Yong, Weiqing Qi, Hao Wu, Liliang Ren, and Yang Hong

1. Introduction Accurate estimation of precipitation is essential for climate analysis, hydrological simulation, drought monitoring, flood forecasting, landslide warning, and related emergency management ( Kidd and Levizzani 2011 ; Maggioni et al. 2016 ). At present, high-quality precipitation estimation mainly depends on rain gauge networks and ground-based radars although satellite technology and satellite-based retrievals have made great progress in recent years. The widely used satellite

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Weiwei Lu, Huimin Lei, Wencong Yang, Jingjing Yang, and Dawen Yang

.” Subsequently, the objective synoptic analysis technique (OSAT) method, proposed by Ren et al. (2006) was applied to separate the gridded precipitation time series into TC rainfall and non-TC rainfall ( section 2 ). Because of its convenience and high accuracy, this method has been widely used in China to distinguish TC precipitation ( Wang et al. 2008 ; Chang et al. 2012 ; Yang et al. 2018 ; Wang et al. 2019 ). Finally, according to the ratio of TC rainfall to total rainfall from the beginning of the

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