提升预测能力:用于预测热带气旋快速加剧的对比学习模型
Chong Wang1,2,3, Nan Yang1,2,3, Xiaofeng Li1,2,3
1Key Laboratory of Ocean Observation and Forecasting, Institute of Oceanology, Chinese Academy of Sciences, Qingdao 266000, China.
概括
预测快速加剧的热带气旋 (TCs) 通过新的对比学习模型得到了改进. 这种模型提高了预测准确度,并减少了这些危险天气事件的错误报警.
科学领域:
- 气象学和大气科学 气象学和大气科学
- 地理空间数据科学数据科学
- 地球科学中的人工智能 地球科学中的人工智能
背景情况:
- 热带气旋 (TCs) 构成重大威胁,快速强化 (RI) 时期特别具有挑战性,以准确预测.
- 预测RI TCs (在24小时内强化至少13 m/s) 的现有模型在检测概率 (POD) 和错误报警率 (FARate) 上有局限性.
研究的目的:
- 开发和评估一种基于对比的新型模型,用于预测热带气旋的快速强化.
- 通过使用集成数据源来提高RI TC预测的准确性并减少虚假警报.
主要方法:
- 基于对比的RI TC预测模型 (RITCF-对比) 的开发.
- 卫星红外图像与大气和海洋数据的整合.
- 解决样本不平衡问题,并将TC的结构特征纳入模型.
主要成果:
- 在西北太平洋的1,149个TC周期 (2020-2021) 中,RITCF-contrastive模型实现了92.3%的POD和8.9%的FARate.
- 与现有的深度学习方法相比,POD有11.7%的改善,FARate减少了三倍.
- 成功解决了样本不平衡,并纳入了关键的TC结构特征.
结论:
- 该RITCF-contrastive模型显著提高了快速加剧的热带气旋的预测.
- 这种方法提供了一种独特而有效的方法来预测危险的天气事件,改进了当前的深度学习技术.
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