Multi-Resolution Analysis of the Convective Structure of Tropical Cyclones for Short-Term Intensity Guidance
Fuente:
arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866909864603680768 |
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| author | Cucuzzella, Elizabeth McNeely, Tria Wood, Kimberly Lee, Ann B. |
| author_facet | Cucuzzella, Elizabeth McNeely, Tria Wood, Kimberly Lee, Ann B. |
| contents | Accurate tropical cyclone (TC) short-term intensity forecasting with a 24-hour lead time is essential for disaster mitigation in the Atlantic TC basin. Since most TCs evolve far from land-based observing networks, satellite imagery is critical to monitoring these storms; however, these complex and high-resolution spatial structures can be challenging to qualitatively interpret in real time by forecasters. Here we propose a concise, interpretable, and descriptive approach to quantify fine TC structures with a multi-resolution analysis (MRA) by the discrete wavelet transform, enabling data analysts to identify physically meaningful structural features that strongly correlate with rapid intensity change. Furthermore, deep-learning techniques can build on this MRA for short-term intensity guidance. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_19854 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Multi-Resolution Analysis of the Convective Structure of Tropical Cyclones for Short-Term Intensity Guidance Cucuzzella, Elizabeth McNeely, Tria Wood, Kimberly Lee, Ann B. Image and Video Processing Machine Learning Accurate tropical cyclone (TC) short-term intensity forecasting with a 24-hour lead time is essential for disaster mitigation in the Atlantic TC basin. Since most TCs evolve far from land-based observing networks, satellite imagery is critical to monitoring these storms; however, these complex and high-resolution spatial structures can be challenging to qualitatively interpret in real time by forecasters. Here we propose a concise, interpretable, and descriptive approach to quantify fine TC structures with a multi-resolution analysis (MRA) by the discrete wavelet transform, enabling data analysts to identify physically meaningful structural features that strongly correlate with rapid intensity change. Furthermore, deep-learning techniques can build on this MRA for short-term intensity guidance. |
| title | Multi-Resolution Analysis of the Convective Structure of Tropical Cyclones for Short-Term Intensity Guidance |
| topic | Image and Video Processing Machine Learning |
| url | https://arxiv.org/abs/2510.19854 |