Stochastic versus Dynamical Downscaling of Ensemble Precipitation Forecasts

Abstract

Abstract The assessment of hydrometeorological risk in small basins requires the availability of skillful, high-resolution quantitative precipitation forecasts to predict the probability of occurrence of severe, localized precipitation events. Large-scale ensemble prediction systems (EPS) currently provide forecast scenarios down to a resolution of about 50 km. High-resolution, nonhydrostatic, limited-area ensemble prediction systems provide dynamically based forecasts by extending these scenarios to smaller scales, typically on the order of 10 km. This work explores an alternative approach to the use of limited-area ensemble prediction systems, by directly applying a stochastic downscaling technique to large-scale ensemble forecasts. The performances of these two different approaches for three well-predicted precipitation events in northwestern Italy during 2006 are compared. Ensemble forecasts provided by the ECMWF EPS, downscaled using the Rainfall Filtered Autoregressive Model (RainFARM) stochastic technique, and ensemble forecasts obtained from the Consortium for Small-Scale Modeling Limited-Area Ensemble Prediction System (COSMO-LEPS) are considered. A dense network of rain gauges is used for verification. It is found that the probabilistic forecast skill of stochastically downscaled ensembles may be comparable with that of dynamically downscaled ensembles, using a range of standard forecast skill measures. Stochastic downscaling is suggested as a tool for benchmarking the performance of dynamical ensemble downscaling systems.