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

South Sudan: IBTrACS Storm Tracks

Publisher: HDX · Source: HDX · License: cc-by-igo · Updated: 2025-05-23

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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: SSD.

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)	4,769
Columns	12 (3 numeric, 8 categorical, 1 datetime)
Train split	3,815 rows
Test split	953 rows
Geographic scope	SSD
Publisher	HDX
HDX last updated	2025-05-23

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Variables

Geographic — iso_time, lat (range -26.1–20.5), lon (range 34.3–93.0).

Outcome / Measurement — number (range 2.0–142.0).

Identifier / Metadata — sid (1996288N09092, 2016102S12074, 1992331S11082), esa_source (HDX), esa_processed (2026-04-08).

Other — basin (North India, South Indian, ), subbasin (Missing, Arabian Sea, Bay of Bengal), nature (Tropical, Not reported, Disturbance), wmo_wind ( , 25, 30), wmo_pres ( , 1000, 1002).

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Quick Start

from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/africa-ssd-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%	1996288N09092, 2016102S12074, 1992331S11082
number	float64	0.0%	2.0 – 142.0 (mean 72.4566)
basin	object	0.0%	North India, South Indian,
subbasin	object	0.0%	Missing, Arabian Sea, Bay of Bengal
iso_time	datetime64[ns]	0.0%
nature	object	0.0%	Tropical, Not reported, Disturbance
lat	float64	0.0%	-26.1 – 20.5 (mean 0.497)
lon	float64	0.0%	34.3 – 93.0 (mean 57.7845)
wmo_wind	object	0.0%	, 25, 30
wmo_pres	object	0.0%	, 1000, 1002
esa_source	object	0.0%	HDX
esa_processed	object	0.0%	2026-04-08

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Numeric Summary

Column	Min	Max	Mean	Median
number	2.0	142.0	72.4566	89.0
lat	-26.1	20.5	0.497	0.9
lon	34.3	93.0	57.7845	56.3

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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_ssd_ibtracs_tropical_storm_tracks,
  title     = {South Sudan: IBTrACS Storm Tracks},
  author    = {HDX},
  year      = {2025},
  url       = {https://data.humdata.org/dataset/ssd-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.