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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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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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Louise Arnal, Andrew W. Wood, Elisabeth Stephens, Hannah L. Cloke, and Florian Pappenberger

) in the winter and fall due to higher precipitation forecasting skill in strong ENSO phases ( Wood et al. 2005 ). Increasing the seasonal streamflow forecast skill remains a challenge: one that is being tackled by improving IHCs and SCFs using a variety of techniques. Techniques include model developments and data assimilation and can vary in computational expense. However, over the past several decades, it has been shown that operational streamflow forecast quality has not significantly improved

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Nasrin Nasrollahi, Kuolin Hsu, and Soroosh Sorooshian

advantage of multiple remote-sensing devices. Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) products are combined precipitation products that use GEO's IR information to fill the gaps between PMW estimates ( Huffman et al. 2007 ). For example, to overcome the temporal limitations of PMW estimates, National Oceanic and Atmospheric Administration (NOAA) Climate Prediction Center (CPC) morphing technique (CMORPH) uses atmospheric motion vectors derived from GEO

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Alejandro Hermoso, Victor Homar, and Arnau Amengual

ensemble generation strategies should comprehensively sample initial, boundary and model formulation uncertainties to improve quantitative precipitation forecasts of HPEs so as to prevent human and material losses. In this sense, the second part of this study tackles these predictability issues by means of cutting-edge ensemble generation techniques. Acknowledgments This work was sponsored by: FEDER/Ministerio de Ciencia, Innovación y Universidades—Agencia Estatal de Investigación/COASTEPS (CGL2017

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Janice L. Bytheway, Mimi Hughes, Kelly Mahoney, and Rob Cifelli

hydrological variables at high spatial and temporal resolutions. Understanding the uncertainty in the existing available high-resolution QPE will help to quantify improvements made by additional observing systems and experimental model forecasts. Given the large uncertainty in hourly precipitation on the scale of a few kilometers, it is suggested that improving observational capabilities in regions of complex terrain, whether through additional instrumentation, advanced quality control techniques to remove

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

. Y. Cho , J. S. Herd , J. M. Flavin , W. E. Benner , and G. S. Torok , 2007 : The next generation multimission U.S. surveillance radar network . Bull. Amer. Meteor. Soc. , 88 , 1739 – 1752 , https://doi.org/10.1175/BAMS-88-11-1739 . 10.1175/BAMS-88-11-1739 Wijayarathne , D. , P. Coulibaly , S. Boodoo , and D. Sills , 2020 : Evaluation of radar-gauge merging techniques to be used in operational flood forecasting in urban watersheds . Water , 12 , 1494 , https

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Chris Kidd, Erin Dawkins, and George Huffman

paper assesses how well the operational European Centre for Medium-Range Weather Forecasts (ECMWF) forecast model simulates the mean annual and seasonal diurnal rainfall cycles relative to the satellite-derived Tropical Rainfall Measuring Mission (TRMM) Merged Precipitation Analysis (TMPA) and Precipitation Radar (PR) products across the global tropics (40°N–40°S 180°W–180°E) over a 7-yr time period (2004–11). This is the region where models typically show the least accuracy at simulating

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M. Tugrul Yilmaz and Wade T. Crow

matching techniques are perhaps the most common. A handful of studies have applied rescaling based on least squares regression techniques ( Crow et al. 2005 ; Crow and Zhan 2007 ) but failed to offer any clear rationale for this choice. Additionally, signal variance-based rescaling, typically applied as a preprocessing step in triple collocation analysis ( Stoffelen 1998 ), also provides a means to rescale datasets using three independent estimates of the same variable. However, this approach has not

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