Combining predictive distributions for statistical post-processing of ensemble forecasts

Abstract

Statistical post-processing techniques are now widely used to correct systematic biases and errors in calibration of ensemble forecasts obtained from multiple runs of numerical weather prediction models. A standard approach is the ensemble model output statistics (EMOS) method, a distributional regression approach where the forecast distribution is given by a single parametric law with parameters depending on the ensemble members. Choosing an appropriate parametric family for the weather variable of interest is a critical, however, often non-trivial task, and has been the focus of much recent research. In this article, we assess the merits of combining predictive distributions from multiple EMOS models based on different parametric families. In four case studies with wind speed and precipitation forecasts from two ensemble prediction systems, we study whether state of the art forecast combination methods are able to improve forecast skill.

33 pages, 8 figures, 2 tables

References in corpus (5)

• Probabilistic wind speed forecasting on a grid based on ensemble model output statistics
• Censored and shifted gamma distribution based EMOS model for probabilistic quantitative precipitation forecasting
• Mixture EMOS model for calibrating ensemble forecasts of wind speed
• Similarity-based semi-local estimation of EMOS models
• Probabilistic temperature forecasting based on an ensemble AR modification

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• Probabilistic Temperature Forecasting with a Heteroscedastic Autoregressive Ensemble Postprocessing model
• Postprocessing of point predictions for probabilistic forecasting of day-ahead electricity prices: The benefits of using isotonic distributional regression
• Truncated generalized extreme value distribution based EMOS model for calibration of wind speed ensemble forecasts
• Calibration of wind speed ensemble forecasts for power generation
• Statistical post-processing of ensemble forecasts of temperature in Santiago de Chile
• Statistical post-processing of dual-resolution ensemble forecasts
• Distributional regression and its evaluation with the CRPS: Bounds and convergence of the minimax risk
• Improving probabilistic forecasts of extreme wind speeds by training statistical post-processing models with weighted scoring rules
• Aggregating predictions from experts: a scoping review of statistical methods, experiments, and applications
• Self-Attentive Ensemble Transformer: Representing Ensemble Interactions in Neural Networks for Earth System Models