A similarity-based implementation of the Schaake shuffle

TL;DR

The paper introduces the SimSchaake method, a novel similarity-based implementation of the Schaake shuffle, which improves multivariate weather forecast postprocessing by leveraging historical observation patterns.

Contribution

It presents the SimSchaake approach that uses similarity criteria to select past observations for better dependence structure modeling in ensemble forecast postprocessing.

Findings

SimSchaake outperforms reference ensembles in temperature forecasting.

The method effectively captures dependence structures in multivariate weather data.

Improves forecast calibration and reliability.

Abstract

Contemporary weather forecasts are typically based on ensemble prediction systems, which consist of multiple runs of numerical weather prediction models that vary with respect to in the initial conditions and/or the the parameterization of the atmosphere. Ensemble forecasts are frequently biased and show dispersion errors and thus need to be statistically postprocessed. However, current postprocessing approaches are often univariate and apply to a single weather quantity at a single location and for a single prediction horizon only, thereby failing to account for potentially crucial dependence structures. Non-parametric multivariate postprocessing methods based on empirical copulas, such as ensemble copula coupling or the Schaake shuffle, can address this shortcoming. A specific implementation of the Schaake shuffle, called the SimSchaake approach, is introduced. The SimSchaake method aggregates univariately postprocessed ensemble forecasts using dependence patterns from past observations. Specifically, the observations are taken from historical dates at which the ensemble forecasts resembled the current ensemble prediction with respect to a specific similarity criterion. The SimSchaake ensemble outperforms all reference ensembles in an application to ensemble forecasts for surface temperature from the European Centre for Medium-Range Weather Forecasts.