hurdat2py 0.3.3

A custom HURDAT2 data parser.

hurdat2py

hurdat2py is a research-focused Python interface for the NOAA HURDAT2 Dataset. It automates data retrieval and parsing, giving you immediate access to clean, analysis-ready data.

• Note: Currently only the North Atlantic Basin is supported. See Roadmap for more info.

Table of Contents

1. Installation
2. Quick Start
3. API Reference
   • The Database (Hurdat2)
   • The Storm Object
   • The Season Object
4. Roadmap
5. Changelog
6. Attribution

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Installation

pip install hurdat2py

Quick Start

1. Initialize the Database

• You can specify a path to a local download of the Hurdat2 dataset.

import hurdat2py

hd2 = hurdat2py.Hurdat2("path_to_file.txt")

• Or if no path is specified, it will automatically download the latest data from https://www.nhc.noaa.gov/data/hurdat/ (~7MB) and save it locally.
  • Note: The cached file will be overwritten after 30 days to ensure the latest file is available.

import hurdat2py

hd2 = hurdat2py.Hurdat2()

2. Create Storm or Season objects

• Allows for easy access to storm or season data.

# Storm obect:
storm = hd2['bob', 1991]
# or
storm = hd2['al031991']

# Season object:
season = hd2[1991]

• See The Storm Object or The Season Object for more info.

API Reference

1. The Database (Hurdat2)

The main entry point. Handles downloading, caching, and parsing the raw text data.

Method	Description	Example
hurdat2py.Hurdat2()	Initializes the database. Downloads latest data if local cache is missing/expired.	hd2 = hurdat2py.Hurdat2()
db['name', year]	Get Storm (by Name)*. Returns a Storm object. Case-insensitive. *This method will not work for storms named UNNAMED, especially before 1950.	storm = hd2['bob', 1991]
db[atcfid]	Get Storm (by ID). Returns a Storm object using ATCFID.	storm = hd2['al031991']
db[year]	Get Season. Returns a Season object for the specified year.	season = hd2[1991]
rank_seasons_by_ace()	Returns a sorted list of seasons by Accumulated Cyclone Energy.	top5 = hd2.rank_seasons_by_ace()[:5]

2. The Storm Object

Represents a single tropical cyclone.

Method/Attribute	Description	Example
name	Operational name (e.g., "Bob") (str).	print(storm.name)
atcfid	ATCF ID (e.g., "AL031991") (str).	print(storm.atcfid)
year	The year the storm formed (int).	print(storm.year)
ace	Accumulated Cyclone Energy (10^-4 kn^2) (float).	print(storm.ace)
peak_wind	Maximum sustained wind speed (knots) (int).	print(storm.peak_wind)
peak_status	Highest tropical classification the storm achieved (MH, HU, TS, SS, TD, SD) (str).	print(storm.peak_status)
min_pressure	Minimum central pressure (mb) (int).	print(storm.min_pressure)
landfalls	Number of landfalls recorded* (int). *Note: not every landfall is recorded in the Hurdat2 dataset.	print(storm.landfalls)
lats/lons	Raw list of latitude/longitude points.	ax.plot(storm.lons, storm.lats)
duration_total duration_tc duration_ts duration_hurricane duration_major	Duration of the system in hours, while at specified status (float)	print(storm.duration_tc)
distance_total distance_tc distance_ts distance_hurricane distance_major	Distance* the system travelled in Nautical Miles, while at specified status (float) *Note: Distances calculated using the Haversine Formula.	print(storm.distance_major)
info()	Prints quick overview of the storm.	storm.info()
stats()	Prints statistics and detailed information about the storm.	storm.stats()
plot()	Plots storm track, colored by Saffir-Simpson intensity. Plot aesthetic is inspired by the tropycal package.	storm.plot()
plot_intensity()	Plots the storm's windspeed over time, colored by Saffir-Simpson intensity. Kwargs: zoom (bool) Crops into the intensity curve. landfalls (bool) Plots landfall times on intensity curve.	storm.plot_intensity(zoom=True, landfalls=False)
to_dataframe()	Exports track data (date, time, lat, lon, wind, pressure) to a Pandas DataFrame.	df = storm.to_dataframe()

3. The Season Object

Represents a full year of activity.

Method/Attribute	Description	Example
year	Returns the year of the season (int)	print(season.year)
storms	List of Storm objects within the season.	print(season.storms)
total_storms	Number of storms in the season. Effectively len(season.storms)	print(season_total_storms)
tropical_storms	Number of tropical storms in the season.	print(season.tropical_storms)
hurricanes	Number of hurricanes in the season.	print(season.hurricanes)
major_hurricanes	Number of major hurricanes in the season.	print(season.major_hurricanes)
ace	Total Accumulated Cyclone Energy for the season (float).	print(season.ace)
stats()	Prints statistics and detailed information about the season.	season.stats()
plot()	Plots all storm tracks for the season, colored by Saffir-Simpson intensity. Plot aesthetic is inspired by the tropycal package. Kwargs: labels (bool) Adds labels for each storm track.	season.plot(labels=True)
to_dataframe()	Exports track data (date, time, lat, lon, wind, pressure) for all storms in the season to a Pandas DataFrame.	df = season.to_dataframe()

Roadmap

Future planned updates:

• Northeast Pacific Support: Add support for the NEPAC Hurdat2 dataset.
  • Expected: Spring 2026
• Statistics Improvements: Implement new statistics functionality for Storm and Season objects.
  • Expected: Summer 2026
• Improved Plotting Functionality: Add/improve plotting functions.
  • Expected: Summer 2026
• Wind Radius Implementation: Add support for and methods utilizing wind radius data included with modern records in the Hurdat2 dataset.
  • Expected: 2027

Changelog

v0.3.3 (2026-01-15)

• Documentation: Major overhaul of README to include API reference tables and additional information.

v0.3.2 (2026-01-13)

• Documentation: Updates to README.

v0.3.1 (2026-01-13)

• Launch: Initial release on PyPI.
• Structure: Package structure overhaul for organization.

Attribution & Data Sources

Data

• This package processes data from the National Hurricane Center (NHC) HURDAT2 Database.
  • Landsea, C. W. and J. L. Franklin, 2013: Atlantic Hurricane Database Uncertainty and Presentation of a New Database Format. Mon. Wea. Rev., 141, 3576-3592.

Acknowledgements

• Inspiration: This work was inspired by the great hurdat2parser package. There are many technical capabilities of hurdat2parser that we do not seek to replicate. We recommend you choose whichever package best suites your needs.

• Plotting Style: The map visualization aesthetic in this package was inspired by the excellent tropycal package. While hurdat2py is a standalone implementation, we aimed to match their clear, publication-ready visual style.

License

MIT License. See LICENSE file for details.

Disclaimer

This package is maintained for personal research and is not an official NOAA product.

Copyright

hurdat2py
Copyright © 2026 Andy McKeen
License: MIT