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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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Zachary P. Brodeur and Scott Steinschneider

in their characterization ( Dacre et al. 2015 ; Guirguis et al. 2018 ; Hecht and Cordeira 2017 ), classification ( Dettinger et al. 2018 ; Ralph et al. 2019 ), and predictability ( Baggett et al. 2017 ; DeFlorio et al. 2018a , b ; Lavers et al. 2016 , 2017 ). Predictive skill at medium range (1–14 days) and subseasonal to seasonal (S2S; 15–90 days) time scales has become a topic of particular interest because of its relevance to water infrastructure management decisions, such as forecast

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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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Sheng Chen, Jonathan J. Gourley, Yang Hong, P. E. Kirstetter, Jian Zhang, Kenneth Howard, Zachary L. Flamig, Junjun Hu, and Youcun Qi

generating Stage II radar only are automatic and are considered as inputs to the Stage IV precipitation analysis generated at individual RFCs. A major responsibility of each RFC is producing a high-quality precipitation analysis on an hourly or at least 6-hourly basis. The Stage IV product is composed of data from WSR-88Ds, rain gauges, and satellite data, with the capability of manual quality control performed by forecasters. The technique of bias correction called P1 was originally developed by

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H. A. Titley, H. L. Cloke, S. Harrigan, F. Pappenberger, C. Prudhomme, J. C. Robbins, E. M. Stephens, and E. Zsótér

1. Introduction Considering fluvial flood hazards in tropical cyclone (TC) forecasting and warning is important because this is a leading cause of mortality and damages ( Rezapour and Baldock 2014 ). In the United States, drowning from excessive rainfall occurs in more TCs than deaths from any other cause ( Rappaport 2014 ). Many of these fatalities occur outside of landfall counties ( Czajkowski and Kennedy 2010 ) and in inland counties ( Rappaport 2000 ). The U.S. residential losses from TC

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