FuXi-ENS:一种机器学习模型,用于高效,准确的天气预报.
Xiaohui Zhong1, Lei Chen1,2, Hao Li1,2
1Artificial Intelligence Innovation and Incubation Institute, Fudan University, Shanghai, China.
Science advances
|October 31, 2025
概括
一个新的机器学习模型FuXi-ENS能够准确地预测未来15天的全球天气. 这种先进的系统通过优化整体预测来增强概率天气预测,优于当前的标准.
科学领域:
- 气象学 天气学
- 机器学习 机器学习
- 计算科学 计算科学
背景情况:
- 总体预报对于量化不确定性和概率天气预报至关重要.
- 传统的全球集体预测系统面临着计算上的局限性,限制了集体大小和场景多样性.
- 机器学习 (ML) 在降低计算成本和改进确定性预测方面显示出前景.
研究的目的:
- 介绍Fuxi-ENS,这是一个先进的ML模型,旨在用于全球整体天气预报.
- 应对将ML应用于组合预测的挑战,特别是初始条件和模型中的不确定性.
- 以0.25°的空间分辨率产生每6小时一次的全球整体天气预报,可提前15天.
主要方法:
- 用于FuXi-ENS模型的变化自编码器框架.
- 优化了一个损失函数,将连续排列概率得分 (CRPS) 和Kullback-Leibler分歧结合起来.
- 启用了流量依赖性扰动,以提高预测准确度.
主要成果:
- 与ECMWF合奏相比,FuXi-ENS表现出优越的性能.
- 在关键预测指标中取得了更好的结果,包括CRPS和Brier分数.
- 成功生成了高分辨率的全球整体预测,降低了计算需求.
结论:
- 富西-ENS代表了ML驱动的天气预报的重大进步.
- 该模型有效量化了预测不确定性,并改善了概率预测.
- 富西-ENS提供了一个计算效率高,准确的替代传统集体系统.
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