Spatial ensemble post-processing with standardized anomalies

Abstract

To post-process ensemble predictions for a particular location, statistical methods are often used, especially in complex terrain such as the Alps. When expanded to several stations, the post-processing has to be repeated at every station individually, thus losing information about spatial coherence and increasing computational cost. Therefore, the ensemble post-processing is modified and applied simultaneously at multiple locations. We transform observations and predictions to standardized anomalies. Seasonal and site-specific characteristics are eliminated by subtracting a climatological mean and dividing by the climatological standard deviation from both observations and numerical forecasts. This method allows us to forecast even at locations where no observations are available. The skill of these forecasts is comparable to forecasts post-processed individually at every station and is even better on average.