Bivariate Postprocessing of Wind Vectors

TL;DR

This paper introduces three new bivariate postprocessing methods for wind vector forecasts, demonstrating improved calibration and accuracy over existing approaches through a case study of German stations.

Contribution

The paper presents three novel bivariate postprocessing models, including vine copula, gradient boosted EMOS, and distributional regression network, advancing multivariate weather forecast calibration.

Findings

Bivariate DRN and vine copula outperform EMOS in verification scores.

New methods improve calibration and sharpness of wind vector forecasts.

Case study confirms the effectiveness of the proposed approaches.

Abstract

To quantify the uncertainty in numerical weather prediction (NWP) forecasts, ensemble prediction systems are utilized. Although NWP forecasts continuously improve, they suffer from systematic bias and dispersion errors. To obtain well calibrated and sharp predictive probability distributions, statistical postprocessing methods are applied to NWP output. Recent developments focus on multivariate postprocessing models incorporating dependencies directly into the model. We introduce three novel bivariate postprocessing approaches, and analyze their performance for joint postprocessing of bivariate wind vector components for 60 stations in Germany. Bivariate vine copula based models, a bivariate gradient boosted version of ensemble model output statistics (EMOS), and a bivariate distributional regression network (DRN) are compared to bivariate EMOS. The case study indicates that the novel bivariate methods improve over the bivariate EMOS approaches. The bivariate DRN and the most flexible version of the bivariate vine copula approach exhibit the best performance in terms of verification scores and calibration.