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Klaus Vormoor and Thomas Skaugen

: Estimating the time dependence of air temperature using daily maxima and minima: A comparison of three methods . J. Atmos. Oceanic Technol. , 5 , 736 – 742 . Bárdossy, A. , 1998 : Generating precipitation time series using simulated annealing . Water Resour. Res. , 34 , 1737 – 1744 . Beldring, S. , 2003 : Estimation of parameters in a distributed precipitation-runoff model for Norway . Hydrol. Earth Syst. Sci. , 7 , 304 – 316 . Bergström, S. , 1995 : The HBV model. Computer Models of

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Rui Wang, Xin Yan, Zhenguo Niu, and Wei Chen

most noticeable impact on open water is reflected in water surface temperature ( Yan and Zheng 2015 ). Recent studies have focused on the relationship between ocean surface temperature and global climate change ( Chen et al. 2018 ; Carrillo et al. 2018 ; Yan et al. 2016 ; Shirvani et al. 2015 ), while there are relatively few studies on inland water surface temperature (IWST). According to a National Aeronautics and Space Administration (NASA) and a National Science Foundation–funded study

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Erin Dougherty, Erin Sherman, and Kristen L. Rasmussen

the warm sector of extratropical cyclones coming off the Pacific Ocean ( Neiman et al. 2008 ). They are defined in a variety of ways, based upon their scale and intensity. Neiman et al. (2008) defined them as narrow plumes of integrated water vapor (IWV) exceeding 2 cm and over 2000 km long and less than 1000 km wide. They are also defined by integrated water vapor transport (IVT), with one common form of this equation given by Gao et al. (2015) : (1) IVT = ⁡ ( 1 g ∫ 1000 500 q u   d p ) 2

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Yixin Wen, Terry Schuur, Humberto Vergara, and Charles Kuster

decreasing precipitation errors resulting from degraded spatial and temporal sampling, and therefore improve flash flood monitoring. Acknowledgments The authors thank Drs. J. J. Gourley and Zac Flamig for helpful discussions during the preparation of this manuscript; John Krause for processing polarimetric radar QPE; and Dr. Pierre Kirstetter for help on probabilistic approach. Funding was provided by the Spectrum Efficient National Surveillance Radar (SENSR) program and the NOAA/Office of Oceanic and

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Prabhakar Shrestha

water storage. The model was then initialized with spinup soil–vegetation states, and a 10-yr transient run from 2008 to 2017 was conducted using the offline atmospheric forcing data. The vegetation phenology (consisting of monthly data) was updated yearly based on the MODIS remote sensing product. The model was integrated at hourly frequency, and the outputs were generated at 5-day intervals. d. Observations The near-surface soil moisture from the Soil Moisture and Ocean Salinity (SMOS; Kerr et al

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Jessica C. A. Baker, Dayana Castilho de Souza, Paulo Y. Kubota, Wolfgang Buermann, Caio A. S. Coelho, Martin B. Andrews, Manuel Gloor, Luis Garcia-Carreras, Silvio N. Figueroa, and Dominick V. Spracklen

downloaded from the CMIP6 Earth System Grid Federation (ESGF) archives ( ) at monthly resolution (surface SM data only were downloaded at daily resolution and converted to monthly means, due to monthly output being unavailable for this variable). MM (medium resolution in atmosphere and ocean) simulations (N216) have a horizontal resolution equivalent to approximately 60 km in the midlatitudes ( Roberts et al. 2019 ). HadGEM3 uses the Unified Model global

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Zhangkang Shu, Jianyun Zhang, Junliang Jin, Lin Wang, Guoqing Wang, Jie Wang, Zhouliang Sun, Ji Liu, Yanli Liu, Ruimin He, Cuishan Liu, and Zhenxin Bao

:// . More details about the model configuration can be found at . Notably, until 2018, only the ECMWF was coupled to an ocean wave and dynamic ocean circulation model while the other models only describe land and atmospheric processes. Table 1. Information of the numerical prediction products studied in this work. Note: bold indicates the parameters of the dataset selected in this work. BVs: bred vectors; EOF: empirical orthogonal function; EnKF

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Dayal Wijayarathne, Paulin Coulibaly, Sudesh Boodoo, and David Sills

collected every 5–10 min with resolution 0.25 km in range and 0.5° in azimuth. The Digital Precipitation Array (DPA) QPE accumulation on the 4.7625-km Hydrologic Rainfall Analysis Projection (HRAP) grid was selected for the analysis after a NEXRAD QPE evaluation for GTA watersheds by Wijayarathne et al. (2020a) . National Oceanic and Atmospheric Administration (NOAA) Weather and Climate Toolkit (WCT), ArcGIS 10.6.1, MATLAB, and Python scripts were used for further processing of NEXRAD QPEs

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E. C. Massoud, H. Lee, P. B. Gibson, P. Loikith, and D. E. Waliser

, or the historical and the future long-term mean daily precipitation, are similar for all the model averages produced. We would like to point out that there could be concern about using the TRMM precipitation data product over CONUS when there are other longer (arguably more reliable) in situ products available, such as CPC. However, for precipitation over the oceans and for higher temporal resolution, TRMM data can provide some benefits worth mentioning. These two advantages of TRMM will enable

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Hernan A. Moreno, Enrique R. Vivoni, and David J. Gochis

precipitation forecasts relative to observed rainfall fields derived from a calibrated radar product for two summer convection periods (hereafter called storm events). Subsequently, we investigate the distributed flood forecasting skill and its dependence with lead time and catchment scale for the ensemble rainfall forecasts. In addition, we investigate how precipitation errors are transmitted to streamflow uncertainty at internal watershed sites as a flood wave progresses downstream. For these events and

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