Evaluation of statistical downscaling in short range precipitation forecasting

Abstract

The objective of this study is to compare several statistical downscaling methods for the development of an operational short-term forecast of precipitation in the area of Bilbao (Spain). The ability of statistical downscaling methods nested inside numerical simulations run by both coarse and regional model simulations is tested with several selections of predictors and domain sizes. The selection of predictors is performed both in terms of sound physical mechanisms and also by means of “blind” criteria, such as “give the statistical downscaling methods all the information they can process”. Results show that the use of statistical downscaling methods improves the ability of the mesoscale and coarse resolution models to provide quantitative precipitation forecasts. The selection of predictors in terms of sound physical principles does not necessarily improve the ability of the statistical downscaling method to select the most relevant inputs to feed the precipitation forecasting model, due to the fact that the numerical models do not always fulfil conservation laws or because precipitation events do not reflect simple phenomenological laws. Coarse resolution models are able to provide information usable in combination with a statistical downscaling method to achieve a quantitative precipitation forecast skill comparable to that obtained by other systems currently in use.