Spatially adaptive Bayesian estimation for Probabilistic Temperature Forecasts

To account for forecast uncertainty in numerical weather prediction (NWP) models it has become common practice to employ ensemble prediction systems generating probabilistic forecast ensembles by multiple runs of the NWP model, each time with variations in the details of the numerical model and/or initial and boundary conditions. However, forecast ensembles typically exhibit biases and dispersion errors as they are not able to fully represent uncertainty in NWP models. Therefore, statistical postprocessing models are employed to correct ensembles for biases and dispersion errors in conjunction with recently observed forecast errors.

For incorporating dependencies in space, this work proposes a spatially adaptive extension of the state-of-the-art Ensemble Model Output Statistics (EMOS) model. The new approach, named Markovian EMOS (MEMOS), introduces a Markovian dependence structure on the model parameters by employing Gaussian Markov random fields. For fitting the MEMOS model in a Bayesian fashion the recently developed Integrated Nested Laplace Approximation (INLA) approach is utilized, allowing for fast and accurate approximation of the posterior distributions of the parameters. To obtain physically coherent forecasts the basic MEMOS model is provided with an additional spatial dependence structure induced by the Ensemble Copula Coupling (ECC) approach, which makes explicit use of the rank order structure of the raw ensemble.

The method is applied to temperature forecasts of the European Centre for Medium-Range Weather Forecasts (ECMWF) over Europe, where it exhibits comparable or improved performance over univariate EMOS variants.