通过对神经天气模型的统计后处理来改进对流风暴的预测
Antoine Leclerc1,2, Erwan Koch1, Monika Feldmann3
1Expertise Center for Climate Extremes (ECCE), University of Lausanne, Lausanne, Switzerland.
npj natural hazards
|November 10, 2025
概括
准确的恶劣天气预警非常重要. 这项研究使用先进的神经天气模型 (NWM) 和深度学习来预测风暴,改善极端天气事件的预警系统.
科学领域:
- 气象学 天气学
- 大气科学 大气科学
- 人工智能的人工智能
背景情况:
- 及时警告恶劣天气对于减轻灾害影响至关重要.
- 神经天气模型 (NWM) 提供了计算高效的全球大气预报.
- 后处理NWM输出可以预测局部的恶劣天气现象,如风暴.
研究的目的:
- 使用Pangu-Weather NWM提前三天预测每小时的风暴.
- 评估各种统计和深度学习后处理方法对风吹预测的有效性.
- 提高极端风暴事件早期预警系统的准确性和响应能力.
主要方法:
- 使用Pangu-Weather神经天气模型 (NWM) 进行大气环境预测.
- 应用了统计和深度学习技术的层次结构,用于NWM数据的后处理.
- 在瑞士的各个地区采用了通用的极值分布来进行强有力的概率预测.
- 利用卷积神经网络 (CNN) 来分析预测大气数据中的空间模式.
主要成果:
- 深度学习后处理,特别是在空间模式上使用CNN,产生了最好的风暴预测.
- 拟议的方法在各种预测时间和风暴速度方面胜过了直接预测方法.
- 概率预测被限制使用统计稳定性的通用极值分布.
- 该研究表明,NWM对于准确的极端风预报的附加值.
结论:
- 神经天气模型与深度学习后处理集成,显著提高极端风力预报能力.
- 开发的方法改善了每小时风暴的预测,对于恶劣天气预警至关重要.
- 这些进步有助于设计更具响应性和有效的危险天气预警系统.
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