Spatial postprocessing of ensemble forecasts for temperature using nonhomogeneous Gaussian regression

TL;DR

This paper enhances ensemble temperature forecast accuracy by developing spatial postprocessing methods, including nonhomogeneous Gaussian regression with local climatology and spatial error correlation models, tested over Germany.

Contribution

It introduces spatial extensions of NGR for temperature, incorporating local climatology and error correlation models, and compares them with BMA methods.

Findings

Spatial NGR improves forecast calibration and sharpness.

Ensemble copula coupling captures spatial dependencies effectively.

Spatial NGR outperforms univariate methods in predictive skill.

Abstract

Statistical postprocessing techniques are commonly used to improve the skill of ensembles of numerical weather forecasts. This paper considers spatial extensions of the well-established nonhomogeneous Gaussian regression (NGR) postprocessing technique for surface temperature and a recent modification thereof in which the local climatology is included in the regression model for a locally adaptive postprocessing. In a comparative study employing 21 h forecasts from the COSMO-DE ensemble predictive system over Germany, two approaches for modeling spatial forecast error correlations are considered: A parametric Gaussian random field model and the ensemble copula coupling approach which utilizes the spatial rank correlation structure of the raw ensemble. Additionally, the NGR methods are compared to both univariate and spatial versions of the ensemble Bayesian model averaging (BMA) postprocessing technique.