Clustering Methods for Statistical Downscaling in Short-Range Weather Forecasts

Abstract

In this paper an application of clustering algorithms for statistical downscaling in short-range weather forecasts is presented. The advantages of this technique compared with standard nearest-neighbors analog methods are described both in terms of computational efficiency and forecast skill. Some validation results of daily precipitation and maximum wind speed operative downscaling (lead time 1-5 days) on a network of 100 stations in the Iberian Peninsula are reported for the period 1998-99. These results indicate that the weighting clustering method introduced in this paper clearly outperforms standard analog techniques for infrequent, or extreme, events (precipitation 20 mm; wind 80 km h 1 ). Outputs of an operative circulation model on different local-area or large-scale grids are considered to characterize the atmospheric circulation patterns, and the skill of both alternatives is compared.