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preliminary direction on novel uses of ensemble QPFs as forcing to hydrologic models that account for spatial uncertainties in the forecast precipitation fields. This study introduces a new method suitable for operational use called the neighboring pixel ensemble technique (NPET). NPET is a postprocessing algorithm that generates ensemble-based streamflow forecasts accounting for the location uncertainties in QPF forcing fields without the requirement of multiple hydrologic model runs in parallel
preliminary direction on novel uses of ensemble QPFs as forcing to hydrologic models that account for spatial uncertainties in the forecast precipitation fields. This study introduces a new method suitable for operational use called the neighboring pixel ensemble technique (NPET). NPET is a postprocessing algorithm that generates ensemble-based streamflow forecasts accounting for the location uncertainties in QPF forcing fields without the requirement of multiple hydrologic model runs in parallel
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
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
for downstream applications such as hydrological modeling. Techniques generally used to this end are ensemble copula coupling (ECC; Schefzik et al. 2013 ), the Schaake shuffle ( Clark et al. 2004 ), or variants thereof (e.g., Ben Bouallègue et al. 2016 ; Schefzik 2016 ; Scheuerer et al. 2017 ; Bellier et al. 2017 ). This two-step process must, in the perspective of streamflow forecasting, be performed at the spatiotemporal scale of the hydrological model, i.e., at the specific lead times and
for downstream applications such as hydrological modeling. Techniques generally used to this end are ensemble copula coupling (ECC; Schefzik et al. 2013 ), the Schaake shuffle ( Clark et al. 2004 ), or variants thereof (e.g., Ben Bouallègue et al. 2016 ; Schefzik 2016 ; Scheuerer et al. 2017 ; Bellier et al. 2017 ). This two-step process must, in the perspective of streamflow forecasting, be performed at the spatiotemporal scale of the hydrological model, i.e., at the specific lead times and
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
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
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
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
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
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
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
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
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
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
–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
–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
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
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
