基于机器学习的热带气旋快速增强预报

罗通; 洪加诚

doi:10.3969/j.issn.1001-909X.2024.03.008

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海洋学研究 ›› 2024, Vol. 42 ›› Issue (3) : 99-107. DOI: 10.3969/j.issn.1001-909X.2024.03.008

研究论文

基于机器学习的热带气旋快速增强预报

罗通 ¹^,²^,³ ,
洪加诚 ¹^,²^,³^,^*

作者信息 +

Rapid intensification forecast of tropical cyclones based on machine learning

LUO Tong ¹^,²^,³ ,
HONG Jiacheng ¹^,²^,³^,^*

Author information +

文章历史 +

摘要

极深对流云是热带气旋(tropical cyclone,TC)快速增强的前兆,为预报西北太平洋TC快速增强,该研究开发了一种使用极深对流云相关数据的机器学习模型。该机器学习模型整合了飓风强度统计预报快速增强指数(Statistical Hurricane Intensity Prediction Scheme-Rapid Intensification Index, SHIPS-RII)数据与TC中心300 km半径范围内极深对流云的覆盖面积。基于2011—2019年的数据,对24 h内TC增强超过30 kn和35 kn的快速增强事件分别进行了预报,相较于仅使用SHIPS-RII数据的模型,该机器学习模型在皮尔斯技能得分(PSS)方面分别提升了5.66%和9.58%,在检测概率指标(POD)方面分别提升了8.41%和8.55%。用该模型对典型台风杜鹃(Dujuan,2015)进行预报,其结果证明整合了极深对流云覆盖面积的模型在快速增强预报中具有优势,主要体现在TC初始强度较强时发生的快速增强预报。该模型对于强台风的预报具有较大的应用潜力。

Abstract

Extremely deep convective clouds are a precursor to the rapid intensification (RI) of tropical cyclones (TCs). To predict the RI of TCs in the western North Pacific (WNP), a machine learning (ML) model using data related to extremely deep convective clouds was developed. The ML model integrates the Statistical Hurricane Intensity Prediction Scheme-Rapid Intensification Index (SHIPS-RII) data and the coverage area of extremely deep convective clouds within a 300 km radius of the center of the TC. Based on data from 2011 to 2019, the model forecasted RI events that increased by more than 30 kn and 35 kn within 24 h. Compared to models using only SHIPS-RII data, this ML model showed an improvement of 5.66% and 9.58% in the Peirce Skill Score (PSS), and a relative increase of 8.41% and 8.55% in the probability of detection (POD). This model is used to forecast typical typhoon Dujuan (2015), and the results show that the model integrating the coverage area of extremely deep convective clouds has advantages in RI predictions, which is mainly reflected in RI forecasts when the initial intensity is strong. The model has great application potential for forecasting strong typhoons.

导出引用

罗通, 洪加诚. 基于机器学习的热带气旋快速增强预报[J]. 海洋学研究. 2024, 42(3): 99-107 https://doi.org/10.3969/j.issn.1001-909X.2024.03.008

LUO Tong, HONG Jiacheng. Rapid intensification forecast of tropical cyclones based on machine learning[J]. Journal of Marine Sciences. 2024, 42(3): 99-107 https://doi.org/10.3969/j.issn.1001-909X.2024.03.008

中图分类号： P732.4

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