| Volumes and Issues Contents of Issue 3 |
www.nonlin-processes-geophys.net/14/193/2007/
doi:10.5194/npg-14-193-2007
© Author(s) 2007. This work is licensed
under a Creative Commons License.
Meteorological uncertainty and rainfall downscaling
1ISAC-CNR, Corso Fiume 4, 10133 Torino, Italy
2CIMA – Università di Genova, via Cadorna 7, 17100 Savona, Italy
Abstract. We explore the sources of forecast uncertainty in a mixed dynamical-stochastic ensemble prediction chain for small-scale precipitation, suitable for hydrological applications. To this end, we apply the stochastic downscaling method RainFARM to each member of ensemble limited-area forecasts provided by the COSMO-LEPS system. Aim of the work is to quantitatively compare the relative weights of the meteorological uncertainty associated with large-scale synoptic conditions (represented by the ensemble of dynamical forecasts) and of the uncertainty due to small-scale processes (represented by the set of fields generated by stochastic downscaling). We show that, in current operational configurations, small- and large-scale uncertainties have roughly the same weight. These results can be used to pinpoint the specific components of the prediction chain where a better estimate of forecast uncertainty is needed.
Full Article (PDF, 693 KB)
Citation: von Hardenberg, J., Ferraris, L., Rebora, N., and Provenzale, A.: Meteorological uncertainty and rainfall downscaling, Nonlin. Processes Geophys., 14, 193-199, doi:10.5194/npg-14-193-2007, 2007. Bibtex EndNote Reference Manager XML
