Copula-based statistical post-processing for multi-site temperature forecasts

Modern weather forecasts are typically in the form of an ensemble of forecasts obtained from multiple runs of numerical weather prediction models. Ensemble forecasts are usually biased and affected by dispersion errors, and they should be statistically corrected to gain accuracy. This is often done following a two-step approach: first, we correct the univariate forecasts, and then, we reconstruct the dependence structure non-parametrically via empirical copulas. The parametric correction of the dependence structure is limited to Gaussian copula-based methods. In this work, we propose a novel approach based on a more general parametric class of copulas called Archimedean copulas. We test the new method in both a simulated scenario and a case-study setting for multi-site temperature forecasts from the ALADIN-LAEF ensemble system in Austria. Our findings show that the state-of-the-art non-parametric techniques perform well in the simulation study. However, Archimedean copulas outperform the existing techniques, especially Gaussian copula approaches, and output well-calibrated forecasts in the real-case study. Our analysis demonstrates the usefulness of including advanced parametric copula methods in the post-processing context and the need of a more realistic simulated framework to test new methodology.