Spatially adaptive, Bayesian estimation for probabilistic temperature forecasts

TL;DR

This paper introduces a Bayesian spatially adaptive postprocessing method for temperature forecasts that leverages Gaussian Markov random fields and efficient inference techniques, improving calibration over traditional models.

Contribution

It presents a novel Bayesian EMOS approach using SPDE and INLA for spatially varying bias correction in temperature forecast postprocessing.

Findings

Good predictive performance in Germany temperature forecasts

Effective bias correction with spatially varying coefficients

Computational efficiency achieved through SPDE and INLA

Abstract

Uncertainty in the prediction of future weather is commonly assessed through the use of forecast ensembles that employ a numerical weather prediction model in distinct variants. Statistical postprocessing can correct for biases in the numerical model and improves calibration. We propose a Bayesian version of the standard ensemble model output statistics (EMOS) postprocessing method, in which spatially varying bias coefficients are interpreted as realizations of Gaussian Markov random fields. Our Markovian EMOS (MEMOS) technique utilizes the recently developed stochastic partial differential equation (SPDE) and integrated nested Laplace approximation (INLA) methods for computationally efficient inference. The MEMOS approach shows good predictive performance in a comparative study of 24-hour ahead temperature forecasts over Germany based on the 50-member ensemble of the European Centre for Medium-Range Weather Forecasting (ECMWF).