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Jon Olav Skøien, Konrad Bogner, Peter Salamon, and Fredrik Wetterhall

therefore to consider the effect of different data transformation methods on forecast skill in general, and for floods in particular. This includes Box–Cox transformations, similar to what Hemri et al. (2015) used when analyzing multivariate postprocessing techniques for probabilistic hydrological forecasting, but also other transformations such as normal score and transformations based on the general extreme value (GEV) distributions. Section 2 of this paper describes the dataset used for

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Michael Scheuerer and Thomas M. Hamill

of observed precipitation ( Park et al. 2008 ; Hamill et al. 2008 ; Bougeault et al. 2010 ). To obtain reliable probabilistic guidance from ensemble precipitation forecasts, a number of statistical postprocessing techniques have been proposed, including nonparametric methods such as the analog method ( Hamill and Whitaker 2006 ; Hamill et al. 2015 ) or decision-tree methods ( Herman and Schumacher 2018 ; Whan and Schmeits 2018 ), and parametric approaches such as extended logistic regression

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Chih-Chiang Wei

economic losses and casualties ( Hsu and Wei 2007 ). Therefore, a useful scheme for quantitative precipitation forecast (QPF) during typhoon periods is highly desired ( Chang et al. 1993 ; Lee et al. 2006 ; Wei and Hsu 2008a ). In Taiwan, Wang et al. (1986) first developed a technique using the climatology average method (a simple statistical approach developed from the spatial distribution of typhoon center) to forecast typhoon rainfalls over land in Taiwan. This method was adopted to be one of

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Sarah A. Baker, Andrew W. Wood, and Balaji Rajagopalan

climatological correction without considering forecast skill. The authors demonstrate that forecast calibration techniques (e.g., the Bayesian joint probability method) are needed to account for skill in the course of adjusting both forecast mean and forecast spread. Some statistical postprocessing techniques employ additional information from large-scale climate fields to improve dynamical model forecasts. Many studies have focused on improving seasonal precipitation and temperature forecasts ( DelSole and

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Adam Winstral, Tobias Jonas, and Nora Helbig

probability density functions at 31 stations in the north-central United States from National Centers for Environmental Prediction–National Center for Atmospheric Research (NCEP–NCAR) reanalyses data (~200 km resolution), while Curry et al. (2012) sought statistical relationships between climate forecast variables and similar reanalysis data to derive monthly Weibull distribution parameters. Huang et al. (2015) used a combination of physical and statistical nondynamic downscaling techniques to test if

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Allen B. White, Daniel J. Gottas, Arthur F. Henkel, Paul J. Neiman, F. Martin Ralph, and Seth I. Gutman

averaged using the consensus technique described in White et al. (2002) . A 2-h window, centered on each forecast time, was used to match the observation data with the CNRFC forecast. This was done in an attempt to maximize the comparison sample population while still maintaining an accurate verification time. However, because the CNRFC forecasts are generated every 6 h, this selection process ignores two-thirds of the data collected between successive forecasts. When more than one radar snow level

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Chengzu Bai, Mei Hong, Dong Wang, Ren Zhang, and Longxia Qian

–runoff forecasting is a crucial precondition for hydrometeorological research and operational flood forecasting, especially in some undermonitored river basins. Tremendous efforts have been made over the last few decades to recover missing data and to improve hydrological predictions. Most of the missing data recovery methods, such as kriging interpolation, polynomial interpolation, optimal interpolation, Kalman filtering, the successive corrections method, fractal interpolation, and phase space reconstruction

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Qiuhong Tang, Andrew W. Wood, and Dennis P. Lettenmaier

areal precipitation and mean areal temperature for use in its National Weather Service River Forecast System (NWSRFS). We hypothesize that an alternative approach that interpolates the percentiles of the observations instead, with a subsequent step to retrieve the climatological values matching the percentiles at each target location, not only suffices to estimate the forcing adequately at the target areas or locations but is more robust in the face of missing observations. We propose this technique

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Meysam Ghamariadyan and Monzur A. Imteaz

). He et al. (2015) conducted appreciable research that contains a preprocessing and input selection methodology. They used monthly rainfall and climate indices for predicting monthly rainfall in Australia. Goyal (2014) used the wavelet regression (WR) method for forecasting rainfall in India. He found that the WR technique outperforms ANN models in forecasting the monthly rainfall. The performance of the discrete wavelet artificial neural network was assessed by Ramana et al. (2013) in one

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Chih-Chiang Wei and Jinsheng Roan

This paper focuses on addressing the rainfall retrieval problem for quantitative precipitation forecast over land during tropical cyclones. This study applies a machine learning technique—support vector regression (SVR)—for rainfall rate retrievals. The feasibility of SVR and SIL–SVR by comparing them with traditional regression method and SIL approaches is examined. The SSM/I on board the satellites and WRA measurements of Taiwan were used to estimate quantitative precipitation over the Tanshui

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