Similarity‐based semilocal estimation of post‐processing models

Weather forecasts are typically given in the form of forecast ensembles obtained from multiple runs of numerical weather prediction models with varying initial conditions and physics parameterizations. Such ensemble predictions tend to be biased and underdispersive and thus require statistical post‐processing. In the ensemble model output statistics approach, a probabilistic forecast is given by a single parametric distribution with parameters depending on the ensemble members. The paper proposes two semilocal methods for estimating the ensemble model output statistics coefficients where the training data for a specific observation station are augmented with corresponding forecast cases from stations with similar characteristics. Similarities between stations are determined by using either distance functions or clustering based on various features of the climatology, forecast errors and locations of the observation stations. In a case‐study on wind speed over Europe with forecasts from the ‘Grand limited area model ensemble prediction system’, the similarity‐based semilocal models proposed show significant improvement in predictive performance compared with standard regional and local estimation methods. They further allow for estimating complex models without numerical stability issues and are computationally more efficient than local parameter estimation.