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 larae-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.

Meteorological uncertainty and rainfall downscaling / von Hardenberg, J.; Ferraris, L.; Rebora, N.; Provenzale, A.. - In: NONLINEAR PROCESSES IN GEOPHYSICS. - ISSN 1023-5809. - 14:3(2007), pp. 193-199. [10.5194/npg-14-193-2007]

Meteorological uncertainty and rainfall downscaling

von Hardenberg, J.;
2007

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 larae-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.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11583/2814920