[论文解读] FuXi-2.0: Advancing machine learning weather forecasting model for practical applications

Xiaohui Zhong, Lei Chen+4 more|arXiv (Cornell University)|Sep 11, 2024

Hydrological Forecasting Using AI被引用 7

一句话总结

FuXi-2.0 提供前5天每小时全球天气预报，之后为每6小时一次，结合广泛的大气与海洋变量，在关键实际情景中优于 ECMWF HRES。

ABSTRACT

Machine learning (ML) models have become increasingly valuable in weather forecasting, providing forecasts that not only lower computational costs but often match or exceed the accuracy of traditional numerical weather prediction (NWP) models. Despite their potential, ML models typically suffer from limitations such as coarse temporal resolution, typically 6 hours, and a limited set of meteorological variables, limiting their practical applicability. To overcome these challenges, we introduce FuXi-2.0, an advanced ML model that delivers 1-hourly global weather forecasts and includes a comprehensive set of essential meteorological variables, thereby expanding its utility across various sectors like wind and solar energy, aviation, and marine shipping. Our study conducts comparative analyses between ML-based 1-hourly forecasts and those from the high-resolution forecast (HRES) of the European Centre for Medium-Range Weather Forecasts (ECMWF) for various practical scenarios. The results demonstrate that FuXi-2.0 consistently outperforms ECMWF HRES in forecasting key meteorological variables relevant to these sectors. In particular, FuXi-2.0 shows superior performance in wind power forecasting compared to ECMWF HRES, further validating its efficacy as a reliable tool for scenarios demanding precise weather forecasts. Additionally, FuXi-2.0 also integrates both atmospheric and oceanic components, representing a significant step forward in the development of coupled atmospheric-ocean models. Further comparative analyses reveal that FuXi-2.0 provides more accurate forecasts of tropical cyclone intensity than its predecessor, FuXi-1.0, suggesting that there are benefits of an atmosphere-ocean coupled model over atmosphere-only models.

研究动机与目标

• 在风/太阳、航空和海运等领域推动对高时间分辨率机器学习天气预报的需求
• 开发 FuXi-2.0 以实现前5天的每小时全球预报，之后为每6小时的全球预报
• 扩展输出变量集合，包含大气与海洋成分以支持耦合建模
• 在多种实际变量和情景下对比评估 FuXi-2.0、ECMWF HRES 与 Pangu-Weather

提出的方法

• 两模型框架：一个6小时预报生成器和一个1小时内插器以确保连续的1小时预报
• 基于Transformer的内插以减少迭代次数并保持预报连续性
• 包括5个高层大气变量，覆盖13个气压层和23个地表变量以支持风、太阳能、航空和海洋应用
• 加入海洋变量以实现大气-海洋耦合并改善热带气旋强度预报
• 以ERA5作为参照进行评估，并与ECMWF HRES 和 Pangu-Weather 对比1小时预报至90小时

实验结果

研究问题

• RQ1 FuXi-2.0 能否在前5天提供持续时间覆盖的1小时预报，并在之后提供可靠的6小时预报？
• RQ2 FuXi-2.0 的预报在风、太阳、航空和海运等关键变量上是否优于 ECMWF HRES 和 Pangu-Weather？
• RQ3 FuXi-2.0 的大气-海洋耦合是否比单大气模型提升热带气旋强度预报？
• RQ4 扩展变量集合对不同预测时效下的预报准确性和活动性有何影响？

主要发现

• FuXi-2.0 在RMSE和ACC方面对常用变量在至多90小时的预报中超越 ECMWF HRES
• FuXi-2.0 提供基于1小时输出的风力发电预测，优于 HRES
• FuXi-2.0 的耦合大气-海洋输出相较于 FuXi-1.0（仅大气）带来更准确的热带气旋强度预报
• 预测活动性分析显示 FuXi-2.0 能保持现实的变动性而不过度平滑，与 Pangu-Weather 更平滑的预报形成对比
• FuXi-2.0 在风/太阳、航空和海运等关键实际变量方面，相较 Pangu-Weather 和 HRES 显示出优势