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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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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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Lu Yi, Bin Yong, Junxu Chen, Ziyan Zheng, and Ling Li

1. Introduction The coupled land–atmosphere model based on the regional climate model and hydrological model is an important tool to extend the forecast period of local flood ( Bosilovich and Sun 1999 ; Wu and Zhang 2013 ). In a coupled land–atmosphere model, the regional climate model can provide a hydrological model with continuous spatiotemporal variation fields of hydrological variables such as precipitation, evaporation, temperature, and radiation. The hydrological model has more refined

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