Generation of Scenarios from Calibrated Ensemble Forecasts with a Dual-Ensemble Copula-Coupling Approach

Abstract

Abstract Probabilistic forecasts in the form of ensembles of scenarios are required for complex decision-making processes. Ensemble forecasting systems provide such products but the spatiotemporal structures of the forecast uncertainty is lost when statistical calibration of the ensemble forecasts is applied for each lead time and location independently. Nonparametric approaches allow the reconstruction of spatiotemporal joint probability distributions at a small computational cost. For example, the ensemble copula coupling (ECC) method rebuilds the multivariate aspect of the forecast from the original ensemble forecasts. Based on the assumption of error stationarity, parametric methods aim to fully describe the forecast dependence structures. In this study, the concept of ECC is combined with past data statistics in order to account for the autocorrelation of the forecast error. The new approach, called d-ECC, is applied to wind forecasts from the high-resolution Consortium for Small-Scale Modeling (COSMO) ensemble prediction system (EPS) run operationally at the German Weather Service (COSMO-DE-EPS). Scenarios generated by ECC and d-ECC are compared and assessed in the form of time series by means of multivariate verification tools and within a product-oriented framework. Verification results over a 3-month period show that the innovative method d-ECC performs as well as or even outperforms ECC in all investigated aspects.