Improving the analog ensemble wind speed forecasts for rare events

This underestimation increases when the predicted event occurs less frequently in the available historical data. A new bias correction for the AnEn using wind observations from more than 500 U.S. stations is tested to reduce the AnEn's underestimation of rare events. It is shown that the conditional negative bias introduced by the AnEn in its standard application is significantly reduced by our novel approach.

Also, the overall probabilistic AnEn performances improve when predicting wind speed higher than 10 m s(-1) as demonstrated by lower values of the continuous ranked probability score. These improvements can be attributed to an increased reliability achieved by introducing the proposed bias correction algorithm.