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Randal D. Koster, Anthony M. DeAngelis, Siegfried D. Schubert, and Andrea M. Molod

global information on soil moisture conditions that can be assimilated into preforecast analysis systems (e.g., Carrera et al. 2019 ). With more accurate estimates of initial soil moisture state obtained through such techniques, the potential for the identification of forecasts of opportunity and the application of bias correction procedures need not be limited to areas with dense rain gauge networks. All this being said, the Europe region outlined in Fig. 5c shows promise for some supplemental

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Mohammadvaghef Ghazvinian, Yu Zhang, and Dong-Jun Seo

distribution (CSGD; Scheuerer and Hamill 2015 ; Baran and Nemoda 2016 ). These techniques represent the discontinuous–continuous nature of precipitation using left-censored distributions and rely on heteroscedastic distributional regression to derive distribution parameters. Unlike logistic regression and its extended version ( Wilks 2009 ), EMOS variants offer the flexibility of incorporating ensemble attributes such as spread, as well as other forecast variables as additional predictors. Perhaps the

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

. 2004 ; Verbunt et al. 2007 ; Anagnostou et al. 2010 ; Moreno et al. 2012 ) and thus in the use of radar nowcasting techniques for predicting the timing, location, and magnitude of precipitation as input to hydrologic models. Uncertainties inherent in radar nowcasting QPFs are a consequence of the difficulty to forecast rainfall fields for extended periods given that extrapolation functions lose their correlation structures at large lead times (e.g., Sharif et al. 2006 ; Vivoni et al. 2007b

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Ryan A. Zamora, Benjamin F. Zaitchik, Matthew Rodell, Augusto Getirana, Sujay Kumar, Kristi Arsenault, and Ethan Gutmann

multivariable regression, and this regression is applied to predict a downscaled value on the day of interest. In comparison with commonly used statistical disaggregation techniques like bias correction and spatial disaggregation (BCSD; Wood et al. 2004 ), GARD allows the downscaling process to be informed by multiple variables at multiple scales, potentially taking advantage of skill in an atmospheric forecast even when the model’s prediction of a particular target variable (e.g., local precipitation) has

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Qian Cao, Shraddhanand Shukla, Michael J. DeFlorio, F. Martin Ralph, and Dennis P. Lettenmaier

forecasting by providing the basis for postprocessing methods (e.g., bias correction and calibration techniques) that provide adjustments to real-time predictions. Primary factors that impact forecast quality and ability to evaluate the performance of the hindcast in a forecast system configuration include hindcast period, ensemble size and ensemble strategy (e.g., initial times) ( Merryfield et al. 2020 ). However, there are tradeoffs in the system configuration due to practical constraints. A few SubX

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Rui Mei, Guiling Wang, and Huanghe Gu

appears in the semiarid transition zones between arid and humid regions, including the U.S. Great Plains, among others. Following GLACE1, phase 2 of the project (GLACE2) focuses on quantifying the degree to which realistic land surface initialization contributes to the skill of subseasonal forecasts for precipitation and near-surface air temperature. The skill index as defined in GLACE2 emphasizes the temporal variability rather than the mean climatology of these climate variables concerned. The idea

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Wade T. Crow, Concepcion Arroyo Gomez, Joaquín Muñoz Sabater, Thomas Holmes, Christopher R. Hain, Fangni Lei, Jianzhi Dong, Joseph G. Alfieri, and Martha C. Anderson

1. Introduction During the growing season, soil moisture (SM) typically controls the partitioning of available energy between sensible and latent heat flux at the soil–atmosphere interface and thereby influences the energetic relationship between the land surface and the lower atmosphere. Furthermore, SM time series contain significant temporal persistence that can be exploited to forecast this relationship out in time. Therefore, the realistic initialization of SM states in the land surface

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Fabio Oriani, Simon Stisen, Mehmet C. Demirel, and Gregoire Mariethoz

; Caraway et al. 2014 ) and forecast ( Wu 2009 ; Hu et al. 2013 ), relies on the use of nearby-station measurements, aggregated statistics, or other predictor variables to identify similar rainfall patterns in the historical record. The associated rainfall amounts at the station of interest are then randomly sampled or used for conditional inference. These techniques are not usually suitable to handle variable missing-data configurations, since they train the model with a fixed set of predictor

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Dimitrios Stampoulis, Emmanouil N. Anagnostou, and Efthymios I. Nikolopoulos

radar network is a very costly task, and in certain places of the world, this is not practical. Moreover, depending on the topography, there are regions where radar networks may not be deployed, such as high-elevation areas or areas with complex terrain in general. The only way to measure rainfall over these regions is through remote sensing from space. Finally, satellite techniques are constantly improving and are very promising with regards to detecting rainfall under different conditions (i

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

.1080/07011784.2020.1854119 . Wijayarathne , D. , S. Boodoo , P. Coulibaly , and D. Sills , 2020a : Evaluation of radar quantitative precipitation estimates (QPEs) as an input of hydrological models for hydrometeorological applications . J. Hydrometeor. , 21 , 1847 – 1864 , https://doi.org/10.1175/JHM-D-20-0033.1 . 10.1175/JHM-D-20-0033.1 Wijayarathne , D. , P. Coulibaly , S. Boodoo , and D. Sills , 2020b : Evaluation of radar-gauge merging techniques to be used in operational flood forecasting in urban

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