# Climate Trends of Tropical Cyclone Intensity and Energy Extremes Revealed by Deep Learning

arXiv: https://arxiv.org/abs/2402.00362 · HTML: https://mlsanity.com/paper/2402.00362/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 84 references that are in the map.
- First posted on arXiv 1 Feb 2024.

## Builds on (most cited first, 4 of 4)

- [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016)
- [FourCastNet: A Global Data-driven High-resolution Weather Model using Adaptive Fourier Neural Operators](https://mlsanity.com/paper/2202.11214/) (2022)
- [Real-time Tropical Cyclone Intensity Estimation by Handling Temporally Heterogeneous Satellite Data](https://mlsanity.com/paper/2010.14977/) (2020)
- [CNN Profiler on Polar Coordinate Images for Tropical Cyclone Structure Analysis](https://mlsanity.com/paper/2010.15158/) (2020)

## Abstract (arXiv)

Anthropogenic influences have been linked to tropical cyclone (TC) poleward migration, TC extreme precipitation, and an increased proportion of major hurricanes [1, 2, 3, 4]. Understanding past TC trends and variability is critical for projecting future TC impacts on human society considering the changing climate [5]. However, past trends of TC structure/energy remain uncertain due to limited observations; subjective-analyzed and spatiotemporal-heterogeneous "best-track" datasets lead to reduced confidence in the assessed TC repose to climate change [6, 7]. Here, we use deep learning to reconstruct past "observations" and yield an objective global TC wind profile dataset during 1981 to 2020, facilitating a comprehensive examination of TC structure/energy. By training with uniquely labeled data integrating best tracks and numerical model analysis of 2004 to 2018 TCs, our model converts multichannel satellite imagery to a 0-750-km wind profile of axisymmetric surface winds. The model performance is verified to be sufficient for climate studies by comparing it to independent satellite-radar surface winds. Based on the new homogenized dataset, the major TC proportion has increased by ~13% in the past four decades. Moreover, the proportion of extremely high-energy TCs has increased by ~25%, along with an increasing trend (> one standard deviation of the 40-y variability) of the mean total energy of high-energy TCs. Although the warming ocean favors TC intensification, the TC track migration to higher latitudes and altered environments further affect TC structure/energy. This new deep learning method/dataset reveals novel trends regarding TC structure extremes and may help verify simulations/studies regarding TCs in the changing climate.