Clustering-based spatial interpolation of parametric post-processing models

TL;DR

This paper introduces a clustering-based spatial interpolation method to extend calibrated probabilistic weather forecasts from observation stations to any location, improving the accuracy of ensemble post-processing.

Contribution

It proposes a novel clustering-based interpolation technique for parametric post-processing models, enhancing spatial coverage and forecast calibration.

Findings

Outperforms regional EMOS models and raw ensemble forecasts

Demonstrates improved wind speed forecast accuracy

Shows effectiveness in a case study with ECMWF data

Abstract

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.