  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
postprocessing. In the ensemble model output statistics (EMOS) approach, a
probabilistic forecast is given by a single parametric distribution with
parameters depending on the ensemble members. This article proposes two
semi-local methods for estimating the EMOS 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 using either distance functions or clustering based on various
features of the climatology, forecast errors, ensemble predictions 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 proposed similarity-based semi-local models show significant
improvement in predictive performance compared to 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.
