Ensemble model output statistics for wind vectors

TL;DR

This paper introduces a bivariate EMOS technique for postprocessing ensemble wind vector forecasts, improving calibration and sharpness over raw ensemble methods.

Contribution

It proposes a novel bivariate EMOS model that accounts for wind vector bias correction and directional correlation, enhancing forecast accuracy.

Findings

Forecasts were well-calibrated and sharp.

Significant improvement over raw ensemble forecasts.

Effective in a case study over the North American Pacific Northwest.

Abstract

A bivariate ensemble model output statistics (EMOS) technique for the postprocessing of ensemble forecasts of two-dimensional wind vectors is proposed, where the postprocessed probabilistic forecast takes the form of a bivariate normal probability density function. The postprocessed means and variances of the wind vector components are linearly bias-corrected versions of the ensemble means and ensemble variances, respectively, and the conditional correlation between the wind components is represented by a trigonometric function of the ensemble mean wind direction. In a case study on 48-hour forecasts of wind vectors over the North American Pacific Northwest with the University of Washington Mesoscale Ensemble, the bivariate EMOS density forecasts were calibrated and sharp, and showed considerable improvement over the raw ensemble and reference forecasts, including ensemble copula coupling.