Probabilistic forecasts in the form of ensemble of scenarios are required for
complex decision making processes. Ensemble forecasting systems provide such
products but the spatio-temporal structures of the forecast uncertainty is lost
when statistical calibration of the ensemble forecasts is applied for each lead
time and location independently. Non-parametric approaches allow the
reconstruction of spatio-temporal joint probability distributions at a low
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 ensemble system COSMO-DE-EPS
run operationally at the German weather service. Scenarios generated by ECC and
d-ECC are compared and assessed in the form of time series by means of
multivariate verification tools and in a product oriented framework.
Verification results over a 3 month period show that the innovative method
d-ECC outperforms or performs as well as ECC in all investigated aspects