Rapid intensification forecast of tropical cyclones based on machine learning

LUO Tong; HONG Jiacheng

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

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Journal of Marine Sciences ›› 2024, Vol. 42 ›› Issue (3) : 99-107. DOI: 10.3969/j.issn.1001-909X.2024.03.008

Rapid intensification forecast of tropical cyclones based on machine learning

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

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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.

Key words

western North Pacific / tropical cyclones / rapid intensification / infrared brightness temperature (IR BT) / extremely deep convective clouds / machine learning / typhoon Dujuan(2015) / initeal intensity of TC

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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

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