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Interpret Hurricane Data contained in HURDAT2

Project description

hurdat2parser -- v2.2.3.1

Interpreting the Hurdat2 Tropical Cyclone Dataset using Python

Hurdat2 ( is a collection of records from individual Tropical Cyclones. The two primary Hurdat2 records are for the Atlantic (since 1850) and East/Central-Pacific Oceans (since 1949).

The purpose of this module is to provide a quick way to analyze the Hurdat2 datasets. It includes methods for retrieving, inspecting, ranking, or even exporting data for seasons, individual storms, or climatological eras.

PyPI Link:


Changes in this Version (

  • Hotfix for <TropicalCyclone>.track_map() that simply skips over tkinter-dependent lines upon error (will occur if user does not have tkinter installed).

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  • At the command prompt, run pip install hurdat2parser
    • When installing, packages pyshp, geojson, and matplotlib will be downloaded as dependencies (if necessary). From scratch, it's around 30MB total of dependency downloads.

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Importing the Module

Follow these commands for genesis!

>>> import hurdat2parser
#or if you're lazy like me...
>>> import hurdat2parser as hd2
  • If you want a local copy of a hurdat2file, got to to download. It's a text file. Hurdat2 files for the Atlantic Basin and East/Central Pacific Basins are available and compatible with the package.
    • As of v2.2 this isn't 100% necessary as the datasets can be automatic retrieval can be attempted (keep reading). This method does not keep a local copy though (as of this version).

To read-in a local file:
>>> atl = hurdat2parser.Hurdat2("path_to_hurdat2.txt")

And/Or to let the module download and ingest the data for you:

  • North Atlantic Basin
    >>> atl = hurdat2parser.Hurdat2(basin="atl")
    • Any of the following will be recognized: "al", "atl", or "atlantic"
  • NE/CEN Pacific Basin
    >>> pac = hurdat2parser.Hurdat2(basin="pac")
    • Any of the following will be recognized: "pac", "nepac", or "pacific"
  • Of note, the above commands do not keep a local copy for next time

To include a url to download, set the kwarg urlcheck to True. This generally will not be needed unless you want to use my (shameless plug) daily-updated current-season hurdat2 during the year (on my website; see Copyright section)
>>> atl = hurdat2parser.Hurdat2("http://crazygonuts.aboutweatherstuff/myhurdat2file.txt", urlcheck=True)

These various ways can be mixed/matched too.

  • For the Hurdat2 object, I highly recommend to use something shortened and readable for the parent object name (like atl or al for the Atlantic database and ep, cp, or pac for the East/Central Pacific)*

After a few seconds (a little longer if downloading) you'll be ready to dive into the data! Subsequent examples imply the use of the Atlantic Hurdat2 (atl).

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Example calls

There are several ways to invoke a call to a Season or TropicalCyclone object. These ways are detailed in the next section, indirectly in the Methods and Attribute tables, and in a large portion of method docstrings. This brief section will demonstrate the ways I navigate the dataset with this module when coding and testing.

  • Season objects (just the year)
    • atl[1995]
  • TropicalCyclone objects
    • by atcfid number
      • atl[2005,12] -- returns the object for hurricane katrina (AL200512)
    • by name
      • atl["danny"] -- shows a list of storms in the record given the name of Danny, allowing the user to select which one they desire
      • atl["_rita"], or atl["-rita"] -- beginning the name with an underscore or dash shows the most-recent storm named as such (this returns the most-notorious storms as well since storm names can be retired according to the destruction they cause)
  • TCRecordEntry calls
    • atl[1980,4,29] -- The entry at index 29 (30th total entry) for Hurricane Allen
    • or atl["_allen", 29]

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Hurdat2 Object Structure

  • The structure of a Hurdat2 object is hierarchal. It has two primary dictionaries:
    • <Hurdat2>.tc - A dictionary containing all TropicalCyclone objects compiled from the record.
      • Individual storms can be accessed using their ATCF ID, a unique 8-character id for each tropical cyclone OR a name of a storm.
        • atl["AL122005"] - Call for Hurricane Katrina's Data using its ATCFID (it being the 12th storm from the 2005 Atlantic Season).
        • atl["Name"] - Use this if you know the name, but the year is unknown. A partial search is okay, but may receive less accurate results. It uses the difflib package to calculate close matches and outputs a list (if applicable) that you then can select from.
        • atl["_Name"] or atl["-Name"] - including an underscore or dash at the beginning of the name will return the NEWEST storm by a name that matches. Use this for the most-notorious storms, as retired names would be the newest.
    • <Hurdat2>.season - A dictionary holding Season objects (yearly data).
      • A Season's data is accessed via a simple year key of type int.
        • atl[1995] retrieves the object associated with the 1995 hurricane season.
  • Season objects also have a dictionary (<Season>.tc) that holds TropicalCyclone objects, specific to the year. Some different ways to call these objects:
    • TC Number: atl[1988,8] or atl[1988][8] - Use the storm number (type int) from the season. This is defined by using the ATCF ID.
    • Storm starting letter (or name): atl[1995, "o"] or atl[1995, "opal"] or atl[1995]["Opal"] - This method works for most modern tropical storms, as they have been issued names. So in these cases, you wouldn't need to know the atcfid number. Also of note, because tropical depressions aren't issued names, the id numbers do not always correlate with the co-inciding letter of the alphabet. For example, Hurricane Andrew's ATCFID is AL041992.
  • TropicalCyclone objects contain a list of TCRecordEntry objects in time-ascending order. To access these:
    • atl[2005, 12, 21] would grab the TCRecordEntry for Hurricane Katrina at index 21.

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Methods and Attributes

Now that you know how to call a particular object of interest, you can now access its data, using methods and attributes. The docstrings (accessed via help()) are quite detailed and methods that take arguments have example calls listed. But here is a quick reference to what is available, along with some shortcuts and example calls to assist navigating while in this document.

Hurdat2 Methods and Attributes

Attributes Description Examples
rank_seasons(...) Print a report that lists a ranking of seasons based on an inquired attribute. Other details are included in the report as well, but they'll be ordered according to the attribute passed. Though not all attributes are represented in the printed report, the ordering will be correct based on the inquired attribute. It also takes positional keyword arguments for year1=None and year2=None, allowing optional ranking to limit the scope of the report to a range of years (this also applies to subsequent ranking methods). atl.rank_seasons(5, "track_distance_TS", 1971, 2000, True)
rank_seasons_thru(...) Print a ranking of seasons where you can use part of the season; a way to rank attributes up to or between certain dates. Special keyword arguments, start (internal default of (1,1)) and thru (default of (12, 31)), tuples representing a month and day, (ex. (9,6) for September 6) enable ranking of partial seasons.
*As a side note, exclusion of these keywords just make this a wrapper for the above rank_seasons method.
atl.rank_seasons_thru(10, "TSreach", 1967, thru=(9,30))
rank_climo(...) Yet another useful ranking method. This allows one to rank attributes assessed over several to many years (climatological periods) to one another. Optional keyword argument climatology (default is 30 years) controls the span of time that data is collected and increment (default is 5 years) dictates the temporal distance between periods atl.rank_climo(20,"track_distance_TC", climatology=10, increment=1)
rank_storms(...) Print a report that compares storms and their attributes to each other. Similar to the above methods, other data is included in the report. See the docstring for info on coordextent kw usage atl.rank_storms(20,"HDP",1967)
multi_season_info(...) Prints a report a gathered statistics based on a range of years. This is similar to the info() methods referenced in the next section. This could be thought of as an info method for a climatological period atl.multi_season_info(1991, 2000)
atl[2010, 2019]
storm_name_search(...) Search through the entire Hurdat2 record for storms issued a name. If the positional keyword info is True, the matching storm's info method will print. atl.storm_name_search("Hugo")
output_climo(...) outputs a .csv file using the csv package of 1-year incremented climatological periods. This csv file can then be opened in a spreadsheet program. To compare or rank, the spreadsheet GUI layout is much easier to use especially due to instant sorting and filtering. This accepts a positional keyword argument (climatology=30). atl.output_climo(15)
output_seasons_csv(...) Similar to the above, but for seasons. It takes no arguments. In other words, it would be redundant to run it multiple times, because as the hurdat2parser package doesn't natively allow modification of the data, it would just output the same data over and over. The only exception would be when the Hurdat2 database is updated by the NHC. With this in mind, it will automatically write-over the csv if it already exists (file-name is auto-generated within the method). atl.output_seasons_csv()
output_storms_csv(...) Similar to above, but for individual storms. atl.output_storms_csv()
climograph() displays a graph of the climatological tendency of a variable over multiple seasons (see docstring for a how-to) atl.climograph("ACE", 10, 1, year1=1967)
coord_contains(...) Takes 3 tupled lat-lon coordinates. The purpose is to inquire whether the first arg (the test coordinate) is contained within a bounding box formed by the other two coordinates passed (returns a bool. This is generally just accessed via the ranking methods atl.coord_contains(
    [31.5, 86.0],
    [29.0, 84.0]
) -> True
BASIN_DICT A dictionary containing abbreviations (keys) and definitions (values) of various basins around the world that hurricane data is kept. This is referenced via the basin() method. The data used to form this object came from IBTrACS atl.BASIN_DICT
basin(season=None) Interprets and returns a readable string identifying the TC basin based on the storms within the record. This allows support of dynamically-created singular OR multiple Hurdat2 datasets OR individual seasons (if a Season object is passed to the method). The data used to form this property came from IBTrACS `atl.basin() -> "North Atlantic Basin"
record_range Returns a tuple of the beginning year and end year of the Hurdat2 record atl.record_range -> (1851, 2020)

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Shared Methods and Attributes for Season and TropicalCyclone

Attributes Description Examples
output_shp() Generate a GIS-compatible Shapefile using the shapefile package atl[1988]["Gilbert"].output_shp()
output_geojson() Generate a GIS-compatible and text-readable .geojson file via the geojson package; can also be used in GIS applications atl[2000].output_geojson()
summary() Prints detailed statistics for the season or life of the TC atl[2019]["dorian"].summary()
*SEE DISCLAIMER FOR LANDFALL INFO* Sum of all landfalls (remember, a storm can make multiple landfalls). <Season>.landfalls is also an unfiltered aggregate of its storms landfalls. At the seasonal level, you'd probably be more interested in the attribute landfall_TC (see following section) atl[1960]["doNnA"].landfalls
-can be TC, TD, TS, HU, or MHU
Season: qty of TC's that made landfall as the inquired status; TropicalCyclone: bool indicating if the storm made landfall while the inquired status
-CAUTION: These attrs are exclusive of landfalls made while of stronger designation-
atl[1996].landfall_TS -> 2
atl[2011]["I"].landfall_HU -> True
-can be TC, TD, TS, HU, or MHU
Season: qty of TC's that reached at least the inquired status; TropicalCyclone: bool indicating if the storm ever was designated as the inquired status atl[2010].HUreach -> 12
atl[1991]["danny"].HUreach -> False
ACE, HDP, or MHDP Returns the specified Energy Index reading associated with the season or storm. Note that the MHDP is one you likely won't see elsewhere. It's formula is the same as ACE and HDP but for major-hurricanes only atl[1966].HDP
track_distance The calculated distance (in nmi) for the entire track of the storm (or aggregate of the season), regardless of status atl[1954]["hazel"].track_distance
-can be TC, TD, TS, HU, or MHU
The distance traversed while storm(s) was(were) at least the status designation atl[1961].track_distance_MHU
For Season objects, returns the quantity of storms that reached category 4+ status or category 5 status (respectively)
For TropicalCyclone objects, returns a bool, indicating if the TC ever achieved cat 4+ or 5 status (respectively)
atl[2020].cat45reach -> 5
atl[2020].cat5reach -> 0
atl[2020]["Iota"].cat5reach -> False
hurdat2() This method prints a hurdat2-formatted output of the storms's data, emulating the actual hurdat2 record from which it (the Season or Tropical Cyclone) was created atl[2005].hurdat2()
duration Returns the length of the Season or life of the cyclone (Of note, the TropicalCyclone property disregards storm status) atl[2022].duration
tc_entries Returns a list of TCRecordEntry's from where a storm was designated as a tropical cyclone atl["_katrina"].tc_entries
start_date The beginning moment of the Season or storm atl[1938].start_date
start_ordinal The quantified day of the year of the beginning moment of the Season or storm atl[1938].start_ordinal
end_date The ending date of the Season or storm atl[1954, 16].end_date
end_ordinal The quantified day of the year of the ending date of the Season or storm atl[1954].end_ordinal

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Season Only Methods and Attributes

Attribute Description Example
output_shp_segmented() Generates a shapefile including each segment from each tropical-cyclone from a given season; each individual segment from each individual storm will be represented atl[1932].output_shp_segmented()
output_geojson_segmented() Generates a geojson including each segment from each tropical-cyclone from a given season; each individual segment from each individual storm will be representeds atl[2003].output_geojson_segmented()
tracks Returns the quantity of tropical cyclones from a season atl[1995].tracks
-can be TD, TS, or HU
Returns the quantity of TC's from a season that made were given the inquired designation, but never strengthened beyond it. HUonly implies Category 1 and 2 storms only. To inquire about MHU, use the MHUreach attr atl[1950].TDonly
stats() prints a report filled with statistics and ranks for the season, optionally compared with a subset of seasons and/or partial seasons. Though it technically exists as a TropicalCyclone method, its iteration is a mirror-method of .info() atl[2020].stats()

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TropicalCyclone Only Methods and Attributes

Attribute Description Example
gps Returns a list of tupled latitude and Longitude coordinates atl[1998]["MITCH"].gps
maxwind Returns the highest maximum sustained wind-speed observed in the tropical cyclone atl["_katrina"].maxwind
minmslp Returns the lowest recorded pressure of the TC atl[1955][10].minmslp
Returns the MSLP (wind) occurring at the time of peak-winds (minimum MSLP) respectively. These attributes were included because peak winds and minimum MSLP do not necessarily occur at the same time. atl[2015]["joaquin"].maxwind_mslp -> 934
atl[2015]["joaquin"].minmslp_wind -> 120
* This example was given as Joaquin's peak winds (135) were not observed at the time of Joaquin's minimum MSLP (931)
-can be ACE, HDP, or MHDP
Returns the inquired energy index divided by the track distance covered while the energy index was being contributed to (ACEtrack_distance_TS or HDPtrack_distance_HU as examples). WARNING! These attributes do not have any kind of threshold controls, so storms which were short-lived can have high values atl[1964][10].HDP_per_nmi
<EI>_perc_ACE-can be HDP or MHDP
Simply the storm's HDP or MHDP divided by ACE or the MHDP divided by the HDP. Look at this as the percentage (as a float between 0 and 1) of the storm's ACE or HDP that was contributed to while a Hurricane or Major Hurricane, respectively atl[2017]["IRMA"].HDP_perc_ACE
-<EI1> can be TS, HU, or MHU
-<EI2> can be TC, TS, HU, or MHU
Returns the the storm's <EI2> track_distance divided by the track distance while of status <EI1>. <EI1> must be of higher hierarchal status than <EI2> atl[2005][25].track_HU_perc_TC
track_map() Generates a map of the storm's track using matplotlib. Check the docstring for ways to modify the output to your liking. atl["_katrina"].track_map()
Return the percentage (in decimal form) of the contribution of the storm's ACE, HDP, or MHDP value to the season's value `atl["_matthew"].perc_MHDP
info() Prints basic stats for the tropical cyclone atl[2005][12].info()
ACE_no_landfall Returns the ACE of the storm prior to any landfall made `atl["_ivan"].ACE_no_landfall
*<SUFFIX> can be TC, TS, HU, MHU
Returns the aggregated length in days that the cyclone spent as on of the given statuses. These do account for storm status loss and possible reaquisition atl["_irma"].duration_MHU

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TCRecordEntry Attributes

  • Only the most-common attributes will be referenced in this document. A comprehensive list is available via help
Attribute Description
entrytime A datetime.datetime object of the entry's timestamp
status The designated 2-letter abbreviation representing the storm's status
A tuple of the latitude/longitude coordinates; location_rev is reversed in order, a longitude/latitude tuple; seemingly favored for GIS use.
wind The maximum-sustained winds at the time of entry-recording
mslp The Mean Sea-Level Pressure (in hPa or mb) at the time of entry-recording
hurdat2() Returns a reassembled Hurdat2-formatted string, identical to the one parsed to create the entry
maxwind_radius The Radius of Maximum Winds. Currently, it is only available for storms since 2021, and is assumed that in subsequent releases, this variable will continue to be updated. It is unknown if this data for previous seasons will (or can) be back-filled.
Suffix can be TS, TS50, or HU
The statistical mean of the reported wind-extents (quadrants). Wind extents are only available for storms since 2004
Suffix can be TS, TS50, or HU
The calculated area covered by the extent of winds of specified strengths (of note, wind extents are only available for storms since 2004)
Suffix can be TC, TS, HU, or MHU
returns the distance tracked by the system from its genesist to the point of the entry. The variables with suffixes do account for status in their calculations
Returns the entry prior-to or after the TCRecordEntry that it is called from
direction() Returns the heading in degrees that the storm is going. A readable cardinal direction can be returned if True is passed to the method
speed The average speed between the previous entry and this one in nm/h
Of Note, remember that the Hurdat2 spatial resolution is 0.1 degrees latitude/longitude. So some appreciable error may be present

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Landfall Data

An important point to keep in mind when viewing data and ranking, Hurdat2's landfall data is incomplete. It is progressively being updated in conjunction with the yearly release of the dataset. Please see the documentation Hurdat2 Format Guide for more detail on currently-available landfall data temporal scope.

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Access to Future Methods

In, I have some stuff that I've started to develop but not ready/sure to release. The methods are functional though. The quickest way of accessing it is:

>>> import hurdat2parser._future as future

Valid classes reflect those of the main hurdat2parser object, namely future.Hurdat2, future.Season, future.TropicalCyclone, or future.TCRecordEntry. They'll be empty if I don't have anything I'm currently developing for that respective class/object. You can see what methods I have available by running dir() on the class.

When calling, you will need to manually pass a valid object (a substitute for self). For example:

>>> future.Season.track_map(atl[1984])

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  • Include more Season.duration-related properties based on status
  • Speed-up <Season>.stats() report; because it employs rank_seasons_thru, and since that is quite snappy, I think the stats() method should be quick too. I'll investigate.
  • Develop matplotlib-based graphs located in _future
  • Comparison methods to directly compare one season to another or specific storms to another.
  • Revamp hurdat2() method of TropicalCyclone and Season objects to optionally return a string instead of merely printing to console
  • Formulate a stats() method for TropicalCyclones (to eventually replace their info method); including ranking/comparing to storms across the record or a subset of seasons (right now, this method is just a wrapper for the info() method.
  • Track distance percentage of season total for TropicalCyclones
  • Maybe simpler rank methods so values sought for ranking will guarantee to show
  • add maps files for Pacific-centric storms
  • investigate adding a minmslp_entry or maxwind_entry property for TropicalCyclone objects. It is acknowledged though that multiple entries can have the same minimum mslp or max wind.
  • a genesis property, returning the first entry from a TropicalCyclone earning the TC designation. Not sure the feasability of this. I'll think about it.
  • Include legends for <TropicalCyclone>.track_map()
  • Account for absent season starts when using stats thru certain time

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  • HURDAT Reference: 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.
  • Haversine Formula (via Wikipedia)
  • Bell, et. al. Climate Assessment for 1999. Bulletin of the American Meteorological Society. Vol 81, No. 6. June 2000. S19.
  • G. Bell, M. Chelliah. Journal of Climate. Vol. 19, Issue 4. pg 593. 15 February 2006.
  • Hurdat2 Format Guide (PDF)
  • Natural Earth GIS Data (data extracted there-from to display maps)

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Author: Kyle S. Gentry
Copyright © 2019-2023, Kyle Gentry (
License: MIT
Author's Webpages:

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