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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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Klaus Vormoor and Thomas Skaugen

Norway, observed meteorological data are usually only available as daily values, whereas meteorological forecast data are available on almost any desired temporal resolution. Since most hydrological models need to be calibrated with historical data, we need to close the gap in temporal resolution between historical and forecasted meteorological data and introduce appropriate techniques to refine historical meteorological data into a subdaily resolution. The Norwegian Water Resources and Energy

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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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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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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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Ben S. Pickering, Ryan R. Neely III, Judith Jeffery, David Dufton, and Maryna Lukach

1. Introduction All meteorologists agree that precipitation must be recorded accurately, yet there is no consensus on the best method to do so. There are many ways to measure precipitation, both in situ or remote sensing. For remote sensing techniques, the sample volume of any single remote sensing measurement contains a population of hydrometeors that must be derived statistically from the measurement. As such, spatial variability smaller than the measurement scale is lost and important

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