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T. J. Bellerby

G. , and Zhu Y. , 2003 : Probability and ensemble forecasts. Forecast Verification, I. T. Jolliffe and D. B. Stephenson, Eds., Wiley, 137–164. Turk, F. J. , and Miller S. D. , 2005 : Toward improving estimates of remotely-sensed precipitation with MODIS/AMSR-E blended data techniques . IEEE Trans. Geosci. Remote Sens. , 43 , 1059 – 1069 . Ushio, T. , and Coauthors , 2009 : A Kalman filter approach to the Global Satellite Mapping of Precipitation (GSMaP) from combined passive

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Ryan R. Neely III, Louise Parry, David Dufton, Lindsay Bennett, and Chris Collier

radar networks, the ability to create maps of precipitation on national scales at 5-min frequencies with subkilometer resolutions has become routine. These reveal important microphysical and dynamical information and are an invaluable tool for flood forecasters ( Herzegh and Jameson 1992 ; Zrnić and Ryzhkov 1999 ; Ogden et al. 2000 ; Lascaux et al. 2007 ; Cifelli and Chandrasekar. 2010 ; Gourley et al. 2010 ; Berne and Krajewski 2013 ; Antonini et al. 2017 ). The effectiveness of dual

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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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Blandine Bianchi, Peter Jan van Leeuwen, Robin J. Hogan, and Alexis Berne

1. Introduction The problem of accurate measurement of rainfall intensity has been long investigated because it has important implications in meteorology, agriculture, environmental policies, monitoring of sewage systems in urban areas, and weather forecasting. Over past decades, various techniques have been developed for monitoring rainfall, but its strong spatial and temporal variability still represents a significant source of uncertainty. In this study, a variational approach is proposed to

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Joseph Bellier, Michael Scheuerer, and Thomas M. Hamill

Bunkers , M. J. , B. A. Klimowski , J. W. Zeitler , R. L. Thompson , and M. L. Weisman , 2000 : Predicting supercell motion using a new hodograph technique . Wea. Forecasting , 15 , 61 – 79 ,<0061:PSMUAN>2.0.CO;2 . 10.1175/1520-0434(2000)015<0061:PSMUAN>2.0.CO;2 Buschow , S. , J. Pidstrigach , and P. Friederichs , 2019 : Assessment of wavelet-based spatial verification by means of a stochastic precipitation model (wv_verif v0

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

) project, which is designed and optimized to improve NWS forecasters’ ability to monitor and forecast flash flooding ( Gourley et al. 2017 ). The parameterization of EF5’s water balance models using geospatial datasets is described by Clark et al. (2017) . Vergara et al. (2016) describes a regionalization technique to estimate the routing parameters in the model channels, which results in a priori estimates for routing parameters at all grid cells across the continental United States (CONUS). The EF

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Marouane Temimi, Ricardo Fonseca, Narendra Nelli, Michael Weston, Mohan Thota, Vineeth Valappil, Oliver Branch, Hans-Dieter Wizemann, Niranjan Kumar Kondapalli, Youssef Wehbe, Taha Al Hosary, Abdeltawab Shalaby, Noor Al Shamsi, and Hajer Al Naqbi

’s physical properties, and hence their representation in numerical models is very important for an accurate simulation of the surface and near-surface fields. An accurate modeling of land–atmosphere interactions strongly depends on how accurate the surface properties, in particular the predominant soil texture and LULC, are represented in the model. Göndöcs et al. (2015) investigated the sensitivity of the Weather Research and Forecasting (WRF; Skamarock et al. 2008 ) Model’s response to a more

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Viviana Maggioni, Humberto J. Vergara, Emmanouil N. Anagnostou, Jonathan J. Gourley, Yang Hong, and Dimitrios Stampoulis

ensemble prediction system for flood forecasting. Atger (2001) showed that the ensemble prediction system performs better than a single forecast based on the same model. He also demonstrated that the impact of reducing the number of ensemble members is rather small (i.e., differences between 51 members and 21 members are not significant). Moreover, Verbunt et al. (2007) corroborated that probabilistic flood forecasts have advantages compared to the deterministic forecast for a particular flood

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Philippe Lucas-Picher, Fredrik Boberg, Jens H. Christensen, and Peter Berg

observationally based datasets. Therefore, systematic errors from simulated climate datasets are often reduced with postprocessing using bias correction techniques (e.g., Piani et al. 2010 ; Berg et al. 2012 ) before being provided to CIMs. However, deviations in the sequence of events cannot be corrected with such postprocessing. A method that is not widely adopted by the RCM community, but is popular in the numerical weather prediction and data assimilation communities to generate small-scale predictions

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Joseph A. Santanello Jr., Sujay V. Kumar, Christa D. Peters-Lidard, Ken Harrison, and Shujia Zhou

initialization? The answer would provide insight as to the first-order influence of the land surface on ambient weather (e.g., temperature, humidity, and precipitation) and coupled LA components of a prediction system [e.g., planetary boundary layer (PBL) growth and convective initiation]. This question is addressed here by combining LSM calibration and spinup approaches to produce best estimates of land surface fluxes for coupling with the Advanced Research Weather Research and Forecasting Model (WRF

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