Spatially adaptive post-processing of ensemble forecasts for temperature

TL;DR

This paper introduces a spatially adaptive post-processing method for ensemble temperature forecasts that improves local calibration and sharpness, using Gaussian random fields and local temperature averages, and demonstrates its effectiveness over Germany.

Contribution

The paper extends the non-homogeneous Gaussian regression model with a spatially adaptive approach using Gaussian random fields for improved temperature forecast calibration.

Findings

Outperforms other post-processing methods in calibration and sharpness.

Provides locally calibrated probabilistic temperature forecasts.

Efficient and suitable for operational use.

Abstract

We propose an extension of the non-homogeneous Gaussian regression (NGR) model by Gneiting et al. (2005) that yields locally calibrated probabilistic forecasts of tem- perature, based on the output of an ensemble prediction system (EPS). Our method represents the mean of the predictive distributions as a sum of short-term averages of local temperatures and EPS-driven terms. For the spatial interpolation of temperature averages and local forecast uncertainty parameters we use a Gaussian random field model with an intrinsically stationary component that captures large scale fluctuations and a location-dependent nugget effect that accounts for small scale variability. Based on the dynamical forecasts by the COSMO-DE-EPS and observational data over Germany we evaluate the performance of our method and and compare it with other post-processing approaches such as geostatistical model averaging. Our method yields locally calibrated and sharp probabilistic forecasts and compares favorably with other approaches. It is reasonably simple, computationally efficient, and therefore suitable for operational usage in the post-processing of temperature ensemble forecasts.