Speaker
Elisa Perrone
(Eindhoven University of Technology)
Description
Weather forecasts are often expressed as an ensemble of forecasts obtained via multiple runs of deterministic physical models. Ensemble forecasts are affected by systematic errors and biases and have to be corrected via suitable statistical techniques. In this work, we focus on the statistical correction of multivariate weather forecasts based on empirical copulas.
We present the most common copula-based techniques in the weather forecasting context, and we analyze a case study of joint temperature forecasts for three stations in Austria. Finally, we discuss potential limitations of the methodology, especially when ties appear in the ensemble.
Primary author
Elisa Perrone
(Eindhoven University of Technology)