A similarity-based implementation of the Schaake shuffle

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.

References in corpus (5)

• Wind Energy: Forecasting Challenges for Its Operational Management
• Spatial postprocessing of ensemble forecasts for temperature using nonhomogeneous Gaussian regression
• Probabilistic quantitative precipitation field forecasting using a two-stage spatial model
• Joint probabilistic forecasting of wind speed and temperature using Bayesian model averaging
• Multivariate discrete copulas, with applications in probabilistic weather forecasting

Cited by in corpus (6)

• Comparison of multivariate post-processing methods using global ECMWF ensemble forecasts
• Generative machine learning methods for multivariate ensemble post-processing
• Combining low-dimensional ensemble postprocessing with reordering methods
• Statistical post-processing of ensemble forecasts of temperature in Santiago de Chile
• Generating Synthetic Rainfall Fields by R-vine Copulas Applied to Seamless Probabilistic Predictions
• Ensemble size dependence of the logarithmic score for forecasts issued as multivariate normal distributions