Relative effects of statistical preprocessing and postprocessing on a regional hydrological ensemble prediction system

Abstract

Abstract. The relative roles of statistical weather preprocessing and streamflow postprocessing in hydrological ensemble forecasting at 10 short-to medium-range forecast lead times (day 1-7) are investigated. For this purpose, a regional hydrologic ensemble prediction system (RHEPS) is developed and implemented. The RHEPS is comprised by the following components: i) hydrometeorological observations (multisensor precipitation estimates, gridded surface temperature, and gauged streamflow); ii) weather ensemble forecasts (precipitation and near-surface temperature) from the National Centers for Environmental Prediction 11-member Global Ensemble Forecast System 20Results show that the HCLR preprocessed ensemble precipitation forecasts have greater skill than the raw forecasts. These improvements are more noticeable in the warm season at the longer lead times (>3 days). Both postprocessors, ARX(1,1) and QR, show gains in skill relative to the raw ensemble flood forecasts but QR outperforms ARX(1,1). Preprocessing alone has little effect on improving the skill of the ensemble flood forecasts. Indeed, postprocessing alone performs similar, in terms of the relative mean error, skill, and reliability, to the more involved scenario that includes both preprocessing and postprocessing. We conclude that 25 statistical preprocessing may not always be a necessary component of the ensemble flood forecasting chain.

A Hydraulic MultiModel Ensemble Framework for Visualizing Flood Inundation Uncertainty

“…The 11-member GEFSRv2 precipitation forecasts (Siddique et al 2015;Sharma et al 2017) as well as the 11-member streamflow forecasts (Siddique and Mejia 2017) generated with HL-RDHM were previously verified. Further details about the RHEPS are provided elsewhere (see Siddique and Mejia 2017;Sharma et al 2018), including all the different system components and the data used by the system to generate ensemble 1991-2001, 2001-2006, 2006-2011, and 2001-2011. FIGURE 3. Schematic illustrating the major components and subcomponents of the framework used to generate and visualize the ensemble flood inundation forecasts.…”