端到端数据驱动的天气预报
Anna Allen1, Stratis Markou2, Will Tebbutt3,4
1Department of Computer Science and Technology, University of Cambridge, Cambridge, UK. av555@cam.ac.uk.
Nature
|March 20, 2025
概括
Aardvark Weather是一个全新的端到端机器学习系统,取代了传统的数值天气预测 (NWP) 系统. 这种以数据为导向的方法可以实现准确的全球和本地天气预报,超过现有方法.
科学领域:
- 人工智能
- 大气科学
- 计算科学
背景情况:
- 准确的天气预报对于许多行业和公共安全至关重要.
- 机器学习 (ML) 已经增强了数值天气预测 (NWP),但通常仍然依赖NWP组件.
- 现有的ML模型由于依赖NWP而面临速度和精度的限制.
研究的目的:
- 开发和评估一个端到端,数据驱动的机器学习系统用于天气预测.
- 证明一个单一的ML模型可以取代整个NWP管道.
- 根据已建立的NWP基线评估该系统的性能.
主要方法:
- 开发了一个端到端的数据驱动系统,
- 使用ML模型生成全球网格预报和本地站预报.
- 通过对比Aardvark天气预报与运行NWP基线和最先进的系统.
主要成果:
- 对于多个变量和预测时间, Aardvark 天气的全球预测超过了NWP的操作基线.
- 当地站的预测显示了长达10天的技能,与后处理的NWP和人类辅助系统相竞争.
- 端到端调整进一步提高了当地预测的准确性.
结论:
- 在部署时不依赖NWP即可实现熟练的天气预报.
- 数据驱动的端到端机器模型提供了显著的速度和精度优势.
- 它代表了一代新的天气预测模型,
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