Natural Three-Dimensional Predictor Domains for Statistical Precipitation Downscaling

Abstract

he paper presents an approach for conditional airmass classification based on local precipitation rate distributions. The method seeks, within the potential region, three-dimensional atmospheric predictor domains with high impact on the local-scale phenomena. These predictor domains are derived by an algorithm consisting of a clustering method, namely, self-organizing maps, and a nonlinear optimization method, simulated annealing. The findings show that the resulting spatial structures can be attributed to well-known atmospheric processes. Since the optimized predictor domains probably contain relevant information for precipitation generation, these grid points may also be potential inputs for nonlinear downscaling methods. Based on this assumption, the potential of these optimized large-scale predictors for downscaling has been investigated by applying an artificial neural network as a nonparametric statistical downscaling model. Compared to preset local predictors, using the optimized predictors improves the accuracy of the downscaled time series, particularly in summer and autumn. However, optimizing predictors by a conditional classification does not guarantee that a predictor increases the explained variance of the downscaling model. To study the contribution of each predictor to the output variance, either individually or by interactions with other parameters, the sources of uncertainty have been estimated by global sensitivity analysis, which provides model-free sensitivity measures. It is shown that predictor interactions play an important part in the modeling process and should be taken into account in the predictor screening.