加强热带气旋强度预测,使用可解释的深度学习,整合卫星观测和数值模型输出
Juhyun Lee1, Jungho Im1,2,3, Yeji Shin1,4
1Department of Civil, Urban, Earth, and Environmental Engineering, Ulsan National Institute of Science and Technology, Ulsan, Republic of Korea.
iScience
|May 27, 2024
概括
预测热带气旋 (TC) 强度是很困难的. 结合卫星数据和数值预测的新混合深度学习模型显示,TC强度变化的准确性提高了,可以提前72小时.
科学领域:
- 气象学 天气学
- 气候科学 气候科学
- 人工智能的人工智能
背景情况:
- 由于与环境因素和气候变化不确定性的复杂相互作用,热带气旋 (TC) 强度预测具有挑战性.
- 准确预测TC强度变化,特别是快速强化,对于灾难准备至关重要.
研究的目的:
- 开发和验证用于预测热带气旋强度的混合深度学习模型.
- 评估模型的性能与现有方法相比,特别是对于不同的交付时间和快速加剧事件.
主要方法:
- 提出了一个混合卷积神经网络 (混合-CNN),集成卫星衍生的空间特征和数值预测模型输出.
- 对热带气旋类别和阶段的最佳轨道数据进行验证的混合CNN预报.
- 混合CNN性能与韩国气象管理局 (KMA) 的预测进行了比较.
主要成果:
- 混合CNN预测显示出与基于KMA的预测相比显著改善,技能得分提高了22% (24小时),110% (48小时) 和7% (72小时).
- 对于快速加剧的病例,混合CNN显示了显著的改善:比KMA的预测有62% (24h),87% (48h) 和50% (72h).
- 可解释的深度学习证实了混合CNN在预测TC强度方面的能力.
结论:
- 混合CNN模型在热带气旋强度预测方面提供了一个有希望的进步.
- 通过深度学习将卫星数据与数值模型输出集成,可以提高预测的准确性和可靠性.
- 这种方法为改善热带气旋预警和灾害管理提供了有价值的工具.
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