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ABSTRACT
An analog-based ensemble model output statistics (EMOS) is proposed to improve EMOS for the calibration of ensemble forecasts. Given a set of analog predictors and corresponding weights, which are optimized with a brute-force continuous ranked probability score (CRPS) minimization, forecasts similar to a current ensemble forecast (i.e., analogs) are searched. The best analogs and the corresponding observations form the training dataset for estimating the EMOS coefficients. To test the new approach for renewable energy applications, wind speed measurements at 100-m height from six measurement towers and wind ensemble forecasts at 100-m height from the European Centre for Medium-Range Weather Forecasts (ECMWF) Ensemble Prediction System (EPS) are used. The analog-based EMOS is compared against EMOS, an adaptive and recursive wind vector calibration (AUV), and an analog ensemble applied to ECMWFEPS. It is shown that the analog-based EMOS outperforms EMOS, AUV, and the analog ensemble at all measurement sites in terms of CRPS and Brier score for common and rare events. The CRPS improvements relative to EMOS reach up to 11% and are statistically significant at almost all sites. The reliability of the analog-based EMOS ensemble for rare events is better compared to EMOS and AUV and is similar compared to the analog ensemble.
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1. Introduction
One goal of ensemble forecasting is the quantification of flow-dependent forecast uncertainties. Ensemble forecasts directly taken as output from model-based ensemble prediction systems (EPSs) require postprocessing (calibration) to remove systematic errors and to increase both their reliability and statistical consistency. A variety of methods have recently been developed for the statistical calibration of ensemble forecasts (Gneiting et al. 2005; Raftery et al. 2005; Hamill and Whitaker 2006; Pinson 2012; Alessandrini et al. 2013, among others).
To perform well, each method requires appropriate training data consisting of past forecasts and measurements. Pinson (2012) developed a recursive and adaptive wind vector calibration (AUV) in which only the last forecast-measurement pair is used to update the model coefficients, while the weight of training data from previous model updates exponentially decreases. Hamill and Whitaker (2006) proposed an analog-based approach where the N closest forecasts (analogs) to a current model-based ensemble forecast are searched over a training period and where analyses that correspond to the forecast analogs constitute an...