Abstract
An analog-based ensemble technique, the analog ensemble (AnEn), has been applied successfully to generate probabilistic predictions of meteorological variables, wind and solar power, energy demand, and the optimal bidding in the day-ahead energy market. The AnEn method uses a historical time series of past forecasts from a meteorological model or other prediction systems and observations of the quantity to be predicted. For each forecast lead time, the ensemble set of predictions is a set of observations from the past. These observations are those concurrent with the past forecasts at the same lead time, chosen across the past runs most similar to the current forecast. Recent applications have demonstrated that the AnEn introduces a conditional negative bias when predicting events in the right tail of the forecast distribution of wind speed, particularly when the training dataset is short. 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.
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