人工智能天气模型可以预测分布外的灰天热带气旋吗?
Y Qiang Sun1, Pedram Hassanzadeh1,2, Mohsen Zand3
1Department of the Geophysical Sciences, University of Chicago, Chicago, IL 60637.
概括
人工智能天气模型很难预测像5类热带气旋 (TCs) 这样的罕见极端事件,如果类似的事件在训练数据中缺失. 模型无法从较弱的风暴中推断出预测这些灰色天事件.
科学领域:
- 人工智能的人工智能
- 气象学 天气学
- 气候科学 气候科学
背景情况:
- 预测罕见的,高影响天气极端,称为灰天,是AI天气模型的关键挑战.
- 一个关键的问题是,人工智能是否可以从观察到的天气事件中推断出预测未见的极端事件.
研究的目的:
- 调查AI天气模型FourCastNet预测极端天气事件 (第5类热带气旋) 的能力.
- 确定FourCastNet是否可以从较弱的事件推断出来预测更强的,未被观察到的极端事件.
主要方法:
- 在 ERA5 数据 (1979-2015年) 上训练了FourCastNet,包括和没有3-5类热带气旋 (TCs),全球和盆地.
- 在2018-2023年5类TC上测试模型性能,评估预测准确性.
主要成果:
- 在没有极端TC的情况下训练的模型无法准确预测5类TC,表明缺乏推断能力.
- 在一个特定的盆地没有TC训练的模型在预测该盆地内的5类TC方面表现出一些技能,这表明了跨盆地概括.
结论:
- 目前的AI天气模型,如FourCastNet,在没有特定的训练数据的情况下,无法可靠地预测灰色天事件.
- 需要新的AI学习策略来准确预测最罕见和最具影响力的极端天气.
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