Generation of scenarios from calibrated ensemble forecasts with a dual ensemble copula coupling approach

TL;DR

This paper introduces a new method called d-ECC that enhances ensemble forecast scenario generation by incorporating autocorrelation of forecast errors, improving the realism of spatio-temporal uncertainty structures.

Contribution

The study combines ensemble copula coupling with past data statistics to account for error autocorrelation, advancing the generation of realistic multivariate forecast scenarios.

Findings

d-ECC outperforms ECC in most verification metrics

d-ECC produces more realistic temporal dependence structures

Method is computationally efficient and applicable to operational wind forecasts

Abstract

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.