Stochastic versus Dynamical Downscaling of Ensemble Precipitation Forecasts

Abstract

The assessment of hydrometeorological risk in small basins requires the availability of skillful, highresolution 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.

Hydrologic evaluation of spatially disaggregated global ensemble rainfall forecasts

“…(), Cloke and Pappenberger () and Brussolo et al . () for studies using the aforementioned scores in the hydro‐meteorological forecasting context presented here. In this study, we used all of these probabilistic evaluation techniques, except the Brier score.…”

“…(), Brussolo et al . () and Silvestro et al ., . A multi‐fractal cascade process described in Deidda (), which is referred to as the ‘STRAIN’ model, was applied by Ferraris et al .…”

“…Taking into account several possible realizations to produce probabilistic small-scale fields makes a better usage of random positioning. This can be achieved, for example, by performing several applications of the stochastic FFG technique to produce different downscaled possibilities of the initial field (Rebora et al, 2006b), and/or by applying the technique to ensemble forecasts (Brussolo et al, 2009;Gaborit et al, 2013a). Badas et al (2006) implemented a FFG (the multi-fractal cascade 'STRAIN' model) taking into account the orographic phenomenon to refine the positioning of disaggregated rainfall amounts.…”

“…The technique developed by Rebora et al (2006a) consists in a transformed gaussian process and is called 'RainFARM'. It was applied by Rebora et al (2006b), von Hardenberg et al (2007, Brussolo et al (2009) and Silvestro et al, 2011. A multi-fractal cascade process described in Deidda (2000), which is referred to as the 'STRAIN' model, was applied by Ferraris et al (2002), Taramasso et al (2005), Siccardi et al (2005) and Deidda et al (2006).…”

“…The former implies the use of a Limited-Area Model (LAM) imbedded in a global one, while the latter involves small-scale meteorological variables' formulation based on large-scale general circulation model outputs and statistical analyses. Statistical downscaling has been widely applied (see Fowler et al, 2007 for a review) because it is (in general) simpler to implement, is not computer intensive and can achieve similar or even better performances compared to dynamical downscaling (see Hay and Clark, 2003;Wood et al, 2004;Brussolo et al, 2009). Furthermore, statistical downscaling can be applied to dynamically downscaled forecasts (Bindlish and Barros, 2000;Ferraris et al, 2002;Rebora et al, 2006a, b;von Hardenberg et al, 2007).…”