Accelerating Simulations of Tropical Cyclone Rapid Intensification Using Adaptive Mesh Refinement

  • Yassine Tissaoui
  • , Stephen R. Guimond
  • , Francis X. Giraldo
  • , Simone Marras

Research output: Contribution to journalArticlepeer-review

Abstract

Tropical cyclones (TCs) are powerful, natural phenomena that can severely impact populations and infra-structure. Enhancing our understanding of the mechanisms driving their intensification is crucial for mitigating these im-pacts. To this end, researchers are pushing the boundaries of TC simulation resolution down to scales of just a few meters. However, high-resolution simulations come with significant computational challenges, increasing both time and energy costs. Adaptive mesh refinement (AMR) is a technique widely used in computational fluid dynamics but has seen limited application in atmospheric simulations. This study explores the use of h-adaptive spectral elements to accelerate simulations of TC rapid intensification while allowing very high resolutions in certain parts of the domain. By applying AMR to a rapidly intensifying TC test case, we demonstrate that AMR can replicate the results of uniform grid simulations in terms of mean and local wind speed maxima while dramatically reducing computational costs. We show that AMR can speed up dry simulations of rapidly intensifying TCs forced by heating observations by a factor of 2–13 for the set of tested refinement criteria. Additionally, we show that TC intensity changes as resolution is increased and that AMR can deliver high-resolution simulations at the cost of coarser static simulations. Our findings indicate that AMR and spectral element methods are promising tools for enhancing TC simulations.

Original languageEnglish (US)
Pages (from-to)2711-2736
Number of pages26
JournalJournal of the Atmospheric Sciences
Volume82
Issue number12
DOIs
StatePublished - Dec 2025

All Science Journal Classification (ASJC) codes

  • Atmospheric Science

Keywords

  • Adaptive models
  • Hurricanes/typhoons
  • Idealized models
  • Nonhydrostatic models
  • Numerical weather prediction/forecasting

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