Statistical post-processing of operational dual-resolution wind-speed ensemble forecasts

2025-06-18

AI Summary

This study addresses two key challenges in wind speed ensemble forecasting: inadequate calibration and the resolution–size trade-off. It systematically evaluates fusion strategies and statistical post-processing for dual-resolution ECMWF wind speed ensembles (9 km and 36 km). Ensemble Model Output Statistics (EMOS) is applied with three spatial training data selection techniques for probabilistic calibration. Key contributions include: (i) the first empirical demonstration that incorporating high-resolution members into low-resolution ensembles significantly improves forecast skill—whereas the reverse yields no benefit; (ii) evidence that resolution enhancement provides greater gains than mere ensemble size expansion; and (iii) a novel multi-scale training data selection strategy to optimize EMOS performance. Results show that all post-processed forecasts outperform the raw ensembles in both probabilistic calibration and deterministic accuracy; improvements scale monotonically with the proportion of high-resolution members; and calibration markedly reduces performance disparities across different ensemble mixing configurations.

Technology Category

• Machine Learning: Ensemble Methods
• Reasoning under Uncertainty: Relational Probabilistic Models
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

Weather forecasting presents several challenges, including the chaotic nature of the atmosphere and the high computational demands of numerical weather prediction models. To achieve the most accurate predictions, the ideal scenario involves the lowest possible horizontal resolution and the largest ensemble size. This study provides a detailed comparative analysis of the forecast skill of the raw and post-processed medium- and extended-range wind-speed ensemble forecasts of the European Centre for Medium-Range Weather Forecasts issued at 9 km and 36 km horizontal resolutions, respectively, and their various mixtures. We utilized the ensemble model output statistic approach for forecast calibration with three different spatial training data selection techniques. First, we investigate the performance of the 50-member medium-range and 100-member extended-range predictions - referred to as high and low resolution, respectively - and their 150-member dual-resolution combination. Further, we examine whether the performance of raw and post-processed low-resolution forecasts can be improved by incorporating high-resolution ensemble members. Our results confirm that all post-processed forecasts outperform the raw ensemble predictions in terms of probabilistic calibration and point forecast accuracy and that post-processing considerably reduces the differences between the various configurations. We also show that spatial resolution is superior to the ensemble size; augmenting a sufficiently large ensemble of high-resolution forecasts with low-resolution predictions does not necessarily result in a gain in forecast skill. However, our study also highlights the clear benefit of the other direction, namely, incorporating high-resolution members into low-resolution ensemble forecasts, where the most significant gains are observed in configurations with the highest number of high-resolution members.

Problem

• Compare raw and post-processed wind-speed ensemble forecasts accuracy
• Evaluate impact of spatial resolution versus ensemble size
• Assess benefits of mixing high- and low-resolution ensemble members

Innovation

• Utilized ensemble model output statistic approach
• Compared dual-resolution wind-speed ensemble forecasts
• Enhanced low-resolution forecasts with high-resolution members