electricsheepafrica/africa-tun-ibtracs-tropical-storm-tracks

Tunisia: IBTrACS Storm Tracks

Publisher: HDX · Source: HDX · License: cc-by-igo · Updated: 2026-03-10

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Abstract

The International Best Track Archive for Climate Stewardship (IBTrACS) project is the most complete global collection of tropical cyclones available. It merges recent and historical tropical cyclone data from multiple agencies to create a unified, publicly available, best-track dataset that improves inter-agency comparisons.

Fields available:
SID: A unique storm identifier (SID) assigned by IBTrACS algorithm.
ISO_TIME: Time of the observation in ISO format (YYYY-MM-DD hh:mm:ss)
BASIN: Basin of the current storm position
SUBBASIN: Sub-basin of the current storm position
NATURE: Type of storm (a combination of the various types from the available sources)
NUMBER: Number of the storm for the year (restarts at 1 for each year
LAT: Mean position - latitude (a combination of the available positions)
LON: Mean position - longitude (a combination of the available positions)
WMO_WIND: Maximum sustained wind speed assigned by the responsible WMO agency
WMO_PRES: Minimum central pressure assigned by the responsible WMO agency.

Each row in this dataset represents geolocated point observations. Temporal coverage is indicated by the iso_time column(s). Geographic scope: TUN.

Curated into ML-ready Parquet format by Electric Sheep Africa.

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Dataset Characteristics

Domain	Climate and environment
Unit of observation	Geolocated point observations
Rows (total)	4,099
Columns	12 (3 numeric, 8 categorical, 1 datetime)
Train split	3,279 rows
Test split	819 rows
Geographic scope	TUN
Publisher	HDX
HDX last updated	2026-03-10

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Variables

Geographic — iso_time, lat (range 9.6–73.6), lon (range -86.9–9.1).

Outcome / Measurement — number (range 23.0–108.0).

Identifier / Metadata — sid (1957245N13339, 2018265N35315, 1987220N34283), esa_source (HDX), esa_processed (2026-04-05).

Other — basin (North Atlantic, ), subbasin (North Atlantic, Caribbean Sea, Gulf of Mexico), nature (Tropical, Extratropical, Subtropical), wmo_wind ( , 35, 60), wmo_pres ( , 1004, 996).

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Quick Start

from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/africa-tun-ibtracs-tropical-storm-tracks")
train = ds["train"].to_pandas()
test  = ds["test"].to_pandas()

print(train.shape)
train.head()

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Schema

Column	Type	Null %	Range / Sample Values
sid	object	0.0%	1957245N13339, 2018265N35315, 1987220N34283
number	float64	0.0%	23.0 – 108.0 (mean 68.0339)
basin	object	0.0%	North Atlantic,
subbasin	object	0.0%	North Atlantic, Caribbean Sea, Gulf of Mexico
iso_time	datetime64[ns]	0.0%
nature	object	0.0%	Tropical, Extratropical, Subtropical
lat	float64	0.0%	9.6 – 73.6 (mean 34.4567)
lon	float64	0.0%	-86.9 – 9.1 (mean -40.754)
wmo_wind	object	0.0%	, 35, 60
wmo_pres	object	0.0%	, 1004, 996
esa_source	object	0.0%	HDX
esa_processed	object	0.0%	2026-04-05

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Numeric Summary

Column	Min	Max	Mean	Median
number	23.0	108.0	68.0339	69.0
lat	9.6	73.6	34.4567	34.4
lon	-86.9	9.1	-40.754	-42.0

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Curation

Raw data was downloaded from HDX via the CKAN API and converted to Parquet. Column names were lowercased and standardised to snake_case. Common missing-value markers (N/A, null, none, -, unknown, no data, #N/A) were unified to NaN. 4 column(s) were cast from string to numeric or datetime based on parse-success rate (>85% threshold). The dataset was split 80/20 into train and test partitions using a fixed random seed (42) and saved as Snappy-compressed Parquet.

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Limitations

• Data originates from HDX and has not been independently validated by ESA.
• Automated cleaning cannot correct for misreported values, definitional inconsistencies, or sampling bias in the original collection.
• Refer to the original HDX dataset page for the publisher's own methodology notes and caveats.

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Citation

@dataset{hdx_africa_tun_ibtracs_tropical_storm_tracks,
  title     = {Tunisia: IBTrACS Storm Tracks},
  author    = {HDX},
  year      = {2026},
  url       = {https://data.humdata.org/dataset/tun-ibtracs-tropical-storm-tracks},
  note      = {Repackaged for machine learning by Electric Sheep Africa (https://huggingface.co/electricsheepafrica)}
}

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Electric Sheep Africa — Africa's ML dataset infrastructure. Lagos, Nigeria.