In the last few years, AI-based models have become the centre of attention in weather forecasting due to their increasing accuracy and efficiency. Pioneering among weather services, ECMWF has developed its Artificial Intelligence Forecasting System (AIFS) model, which was first to provide data-driven ensemble forecasts in June 2024. Since July 2025, the AIFS ensemble model has been operational and runs in parallel with ECMWF's physics-based Integrated Forecasting System (IFS), which is considered the gold standard in weather prediction. The new AIFS model can generate forecasts ten times faster than the classical numerical weather prediction model, while consuming approximately a thousand times less energy. We present the results of our systematic assessment of the performance of the IFS and AIFS models by comparing the accuracy of raw and post-processed medium-range 10-m wind-speed ensemble forecasts generated operationally by the two models for the period between July and November 2025 for more than 9000 synoptic observation stations across the globe. The post-processed case involves the parametric ensemble model output statistics (EMOS) as well as the non-parametric quantile regression (QR) approach to correct any systematic inaccuracies in the raw forecasts. The predictive performance of raw IFS ensemble forecasts proves to be substantially superior to the skill of the raw AIFS predictions for all investigated forecast horizons. As expected, post-processing significantly improves the skill of both IFS and AIFS predictions, and, across most verification metrics, EMOS is superior to QR, especially for short lead times. Compared to the raw ensemble, the differences in skill between the matching IFS and AIFS predictions are substantially decreased by post-processing and are mostly significant at short lead times, when the IFS forecasts outperform their AIFS counterparts.

翻译：在过去几年中，基于AI的模型因其日益提升的准确性和效率，已成为天气预报领域的关注焦点。作为气象服务领域的先驱，欧洲中期天气预报中心（ECMWF）开发了其人工智能预报系统（AIFS）模型，并于2024年6月率先提供了数据驱动的集合预报。自2025年7月起，AIFS集合模型已投入业务运行，并与ECMWF基于物理的综合预报系统（IFS）并行运作，后者被认为是天气预报的黄金标准。新AIFS模型生成预报的速度比传统数值天气预报模型快十倍，同时能耗约降低一千倍。我们通过比较两个模型在2025年7月至11月期间，针对全球超过9000个地面观测站的业务化中期10米风速集合预报的原始及后处理精度，系统评估了IFS与AIFS模型的性能。后处理案例中，我们采用了参数化集合模型输出统计（EMOS）以及非参数化分位数回归（QR）方法来校正原始预报中的系统性误差。结果表明，在所有研究的预报时效内，原始IFS集合预报的预测性能显著优于原始AIFS预报。正如预期，后处理显著提升了IFS和AIFS预报的技能，且在大多数验证指标中，EMOS优于QR，尤其是在短预报时效内。与原始集合相比，后处理大幅降低了匹配的IFS与AIFS预报之间的技能差异，且该差异主要在短预报时效内显著，此时IFS预报的表现优于AIFS对应预报。