Machine learning-based correlation analysis of decadal cyclone intensity with sea surface temperature: data and tutorial
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| Format: | Preprint |
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2025
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| _version_ | 1866915338742923264 |
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| author | Wu, Jingyang Chandra, Rohitash |
| author_facet | Wu, Jingyang Chandra, Rohitash |
| contents | The rising number of extreme climate events in the past decades has motivated the need for a thorough consideration of tropical cyclone genesis and intensity, given the sea-surface temperature (SST). In this paper, we present an analysis of the relationship between the increasing global SST with cyclone genesis using linear regression machine learning models. We extract and curate a dataset of tropical cyclones across selected ocean basins with their associated SST over the past 40 years. We provide correlation analysis using linear regression and visualisation strategies. Our preliminary results show a strong positive correlation between SST and high wind speed across selected ocean basins via linear regression and machine learning models. Our dataset and available open-source code offer a novel perspective for the investigation of the genesis and intensity of tropical cyclones. Alongside the time and position of each cyclone, we also provide the related Saffir-Simpson category, season, wind speed, and SST for 15 days before and after the tropical cyclone genesis. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2506_09254 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Machine learning-based correlation analysis of decadal cyclone intensity with sea surface temperature: data and tutorial Wu, Jingyang Chandra, Rohitash Atmospheric and Oceanic Physics Applications The rising number of extreme climate events in the past decades has motivated the need for a thorough consideration of tropical cyclone genesis and intensity, given the sea-surface temperature (SST). In this paper, we present an analysis of the relationship between the increasing global SST with cyclone genesis using linear regression machine learning models. We extract and curate a dataset of tropical cyclones across selected ocean basins with their associated SST over the past 40 years. We provide correlation analysis using linear regression and visualisation strategies. Our preliminary results show a strong positive correlation between SST and high wind speed across selected ocean basins via linear regression and machine learning models. Our dataset and available open-source code offer a novel perspective for the investigation of the genesis and intensity of tropical cyclones. Alongside the time and position of each cyclone, we also provide the related Saffir-Simpson category, season, wind speed, and SST for 15 days before and after the tropical cyclone genesis. |
| title | Machine learning-based correlation analysis of decadal cyclone intensity with sea surface temperature: data and tutorial |
| topic | Atmospheric and Oceanic Physics Applications |
| url | https://arxiv.org/abs/2506.09254 |