Improving Numerical Weather Forecasts by Bayesian Hierarchical Modelling

Joshua Lovegrove () and Stefan Siegert ()
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Joshua Lovegrove: University of Exeter
Stefan Siegert: University of Exeter

A chapter in Statistical Modeling Using Bayesian Latent Gaussian Models, 2023, pp 193-218 from Springer

Abstract: Abstract Numerical predictions of weather and climate pose interesting problems for spatial and spatio-temporal statistical modelling, especially to quantify and correct systematic differences between forecasts and observation. We review the state of the art of statistical postprocessing of predictions produced by atmospheric simulation models and report encouraging results on the application of Bayesian hierarchical models to improve on the existing statistical postprocessing methodology. In particular, we show that after fitting postprocessing parameters at each grid point by maximum likelihood estimation, a spatial smoothing of the parameter estimates is justified in a Bayesian hierarchical modelling context and offers improvements of out-of-sample forecasts.

Date: 2023
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DOI: 10.1007/978-3-031-39791-2_6

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