  Since the start of the operational use of ensemble prediction systems,
ensemble-based probabilistic forecasting has become the most advanced approach
in weather prediction. However, despite the persistent development of the last
three decades, ensemble forecasts still often suffer from the lack of
calibration and might exhibit systematic bias, which calls for some form of
statistical post-processing. Nowadays, one can choose from a large variety of
post-processing approaches, where parametric methods provide full predictive
distributions of the investigated weather quantity. Parameter estimation in
these models is based on training data consisting of past forecast-observation
pairs, thus post-processed forecasts are usually available only at those
locations where training data are accessible. We propose a general
clustering-based interpolation technique of extending calibrated predictive
distributions from observation stations to any location in the ensemble domain
where there are ensemble forecasts at hand. Focusing on the ensemble model
output statistics (EMOS) post-processing technique, in a case study based on
wind speed ensemble forecasts of the European Centre for Medium-Range Weather
Forecasts, we demonstrate the predictive performance of various versions of the
suggested method and show its superiority over the regionally estimated and
interpolated EMOS models and the raw ensemble forecasts as well.
