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Discernability of the vertical vortex structure of pre-existing disturbances and their implication for tropical cyclone formation

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

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Abstract

The formation of a tropical cyclone (TC) is often rooted in a pre-existing disturbance, yet our understanding of their structural differences and evolution into TCs remains limited. To bridge the knowledge gap, we examine tropical disturbances and depressions in the western North Pacific during the period 2004–2021 from a best-track dataset. Here we show four discernible structures of pre-existing disturbances in terms of their vertical and radial extents: broad vortex dominated by lower-tropospheric vorticity (Cluster 1), narrow vortex with its vorticity maximum in the lower troposphere (Cluster 2), broad and deep vortex spanning most of the troposphere (Cluster 3), and narrow vortex dominated by upper-tropospheric vorticity (Cluster 4), by applying unsupervised machine learning techniques. Out of the 2014 samples analyzed, almost 80 % exhibit vorticity maximum in the lower troposphere, while the others peak aloft. While these different structures have varying implications for stratiform and convective precipitations, there is no clear preference for specific vortex structures in pre-existing disturbances for TC genesis in the next 6 h. On the other hand, the time it takes for TC genesis or the intensification rate is more closely related to the upper-level extent of relative vorticity rather than the local maximum magnitude or radial size of the vortices. Despite the uncertainty concerning the data during the earlier lifetime, the study introduces a systematic approach to categorizing the vortex structures of pre-existing disturbances which provides new insights into their role in TC formation. © 2025 The Authors
Original languageEnglish
Article number100804
JournalWeather and Climate Extremes
Volume50
Online published5 Sept 2025
DOIs
Publication statusPublished - Dec 2025

Funding

This research was supported by the Hong Kong Research Grants Council Early Career Scheme (9048309).

Research Keywords

  • Machine learning
  • Pre-existing disturbance
  • Tropical cyclogenesis
  • Tropical cyclone
  • Vortex structure

Publisher's Copyright Statement

  • This full text is made available under CC-BY 4.0. https://creativecommons.org/licenses/by/4.0/

RGC Funding Information

  • RGC-funded

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