FuXiWeather2: 为操作性全球天气预报学习准确的大气状态估计
FuXiWeather2: Learning accurate atmospheric state estimation for operational global weather forecasting
- Shanghai Academy of Artificial Intelligence for Science（上海人工智能科学研究院）
- Artificial Intelligence Innovation and Incubation Institute（人工智能创新与孵化院）
- Fudan University（复旦大学）
- FuXi Intelligent Computing Technology Co., Ltd.（FuXi智能计算技术有限公司）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本文提出FuXiWeather2模型，通过结合真实观测和再分析数据，提升大气状态估计的精度与稳定性，实现分钟级高分辨率全球分析和10天预报，优于现有系统。
AI中文摘要：
数值天气预测长期以来受到数据同化和数值建模固有计算瓶颈的限制。尽管机器学习加速了预报，现有模型大多作为再分析产品的
英文摘要：
Numerical weather prediction has long been constrained by the computational bottlenecks inherent in data assimilation and numerical modeling. While machine learning has accelerated forecasting, existing models largely serve as "emulators of reanalysis products," thereby retaining their systematic biases and operational latencies. Here, we present FuXiWeather2, a unified end-to-end neural framework for assimilation and forecasting. We align training objectives directly with a combination of real-world observations and reanalysis data, enabling the framework to effectively rectify inherent errors within reanalysis products. To address the distribution shift between NWP-derived background inputs during training and self-generated backgrounds during deployment, we introduce a recursive unrolling training method to enhance the precision and stability of analysis generation. Furthermore, our model is trained on a hybrid dataset of raw and simulated observations to mitigate the impact of observational distribution inconsistency. FuXiWeather2 generates high-resolution ($0.25^{\circ}$) global analysis fields and 10-day forecasts within minutes. The analysis fields surpass the NCEP-GFS across most variables and demonstrate superior accuracy over both ERA5 and the ECMWF-HRES system in lower-tropospheric and surface variables. These high-quality analysis fields drive deterministic forecasts that exceed the skill of the HRES system in 91\% of evaluated metrics. Additionally, its outstanding performance in typhoon track prediction underscores its practical value for rapid response to extreme weather events. The FuXiWeather2 analysis dataset is available at https://doi.org/10.5281/zenodo.18872728.
