Easy Hurricane Tracking with Tropycal | by Lee Vaughan | Nov, 2024


Quick Success Data Science

A great Python package for storm analysis

Lee Vaughan

Towards Data Science

Map of the North Atlantic produced with the Tropycal package showing colored tracks for all 2017 hurricanes.
North Atlantic hurricane tracks for the 2017 season colored by storm type (by author)

A friend approached me recently with an intriguing request: he wanted help selecting his Spring Break vacation destination in the Caribbean. His heart was set on aiding a region recently impacted by a hurricane, hoping his tourism dollars would contribute to their recovery efforts. Naturally, he wanted to steer clear of areas affected too recently, so we decided to look at hurricanes from the past eight years (2017–2024) and exclude sites impacted in the last two (2023–2024).

A map of the Caribbean Sea.
The Caribbean Sea (courtesy of Ian Macky; license)

Of course, an AI chatbot could’ve handled this in seconds, but I was not ready to go quietly into that good night and decided to perform the analysis myself using Python. Open-source hurricane data is readily available from multiple sources, including the:

  • National Hurricane Center (NHC) Data Archive (HURDAT)
  • International Best Track Archive for Climate Stewardship (IBTrACS)
  • U.S. Geological Survey (USGS) Hurricanes Data
  • NOAA Atlantic Oceanographic and Meteorological Laboratory



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