闪电快速的对流展望:用全球基于人工智能的天气模型预测严重的对流环境
Monika Feldmann1, Tom Beucler2, Milton Gomez2
1Institute of Geography Oeschger Centre for Climate Change Research University of Bern Bern Switzerland.
概括
像GraphCast和Pangu-Weather这样的新人工智能天气模型显示出强大的技能,可以在10天内预测严重的雷暴环境. 这些先进的人工智能模型与关键恶劣天气参数的当前运营预测相匹配或超过.
科学领域:
- 气象学 天气学
- 人工智能的人工智能
- 大气科学 大气科学
背景情况:
- 严重的对流风暴带来了重大风险,需要准确的预测.
- 当前的人工智能天气模型提供快速的中期预报,但需要评估来预测严重雷暴至关重要的复杂大气条件.
研究的目的:
- 评估领先的人工智能天气模型 (GraphCast,Pangu-Weather,FourCastNet) 对严重雷暴环境的预测技能.
- 将人工智能模型的性能与已建立的数值天气预测系统进行对流参数的比较.
主要方法:
- 评估了GraphCast,Pangu-Weather和FourCastNet的对流参数,最长可达10天的交付时间.
- 将AI模型预测与再分析数据和ECMWF的IFS操作模型进行了比较.
- 进行案例研究和季节性分析以确定预测准确度.
主要成果:
- 在评估的AI模型中,GraphCast和Pangu-Weather表现出最高的性能.
- 这些人工智能模型与IFS模型在大气不稳定性和剪切性方面的性能相匹配或超过.
- 人工智能模型为恶劣天气环境提供了关键变量的准确预测.
结论:
- 人工智能天气模型显示了改善恶劣天气预报的巨大潜力.
- GraphCast和Pangu-Weather为快速,经济高效的恶劣天气环境预报提供了一个有希望的途径.
- 对人工智能模型的进一步基于过程的评估是危险驱动应用的基础.
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