Reconstructing Pre-Satellite Tropical Cyclogenesis Climatology Using Deep Learning

TL;DR

This paper uses deep learning to reconstruct historical tropical cyclone activity in the western North Pacific, especially before satellite data, revealing significant undercounting in traditional records and improving understanding of cyclogenesis.

Contribution

The study introduces a deep learning method to reconstruct pre-satellite tropical cyclone climatology, enhancing historical data accuracy and understanding of tropical cyclogenesis processes.

Findings

DL effectively captures TCG characteristics during the post-satellite era.

Significant undercounting of TCG in pre-satellite data, especially Sept-Nov in central WNP.

DL reconstruction aligns better with environmental conditions conducive to TC formation.

Abstract

A reliable tropical cyclone (TC) climatology is the key to assessing historical and future changes in TC activities. While global TC records have been systematically maintained since the early 1940s, substantial uncertainties remain for the pre-satellite era during which TC observations relied mostly on scattered aircraft reconnaissance and sporadic ship reports. This study presents a deep learning (DL) approach to reconstruct historical TC activity in the western North Pacific (WNP) basin, with a main focus on the pre-satellite era. Using data feature enrichment tailored for tropical cyclogenesis (TCG), we demonstrate that DL can effectively capture the main characteristics and changes in TCG climatology during the post-satellite era. With additional cross-validations, the reconstruction of TCG climatology is then extended to a pre-satellite period (1940-1960) during which TC base-track datasets are most uncertain. Our DL reconstruction reveals a significant missing of TCG in the current best-track data between September and November during the pre-satellite era. Such a TCG undercount in the best track data occurs mainly around 10-15$^\circ$N in the central WNP, while coastal regions show better consistency with DL reconstruction. These findings not only highlight the potential of DL for improving historical assessments of TC activity, but also advance our understanding of TCG processes by identifying key environmental conditions conducive to TC formation. The DL approach presented herein can be applied to other ocean basins, climate proxies, or reanalysis datasets for future TC climate studies.