electricsheepafrica/africa-climada-tropical-cyclone-dataset

Tropical cyclone: Hazard Data for Disaster Risk Assessment (selected countries)

Publisher: ETH Zürich - Weather and Climate Risks · Source: HDX · License: cc-by · Updated: 2025-09-06

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Abstract

Tropical cyclone wind footprints (m/s) at 150 arcsec (approx 4 kilometers at equator) resolution. Available as global files and per country; available for historically observed records, and synthetically created, probabilistic events, from various modelling sources, for present and future climate scenarios.

Each row in this dataset represents first-level administrative unit observations. Data was last updated on HDX on 2025-09-06. Geographic scope: BGD, COL, PRK, SLV, ETH, FJI, GTM, HTI, and 13 others.

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

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Dataset Characteristics

Domain	Climate and environment
Unit of observation	First-level administrative unit observations
Rows (total)	388
Columns	9 (3 numeric, 6 categorical, 0 datetime)
Train split	310 rows
Test split	77 rows
Geographic scope	BGD, COL, PRK, SLV, ETH, FJI, GTM, HTI, and 13 others
Publisher	ETH Zürich - Weather and Climate Risks
HDX last updated	2025-09-06

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Variables

Geographic — country_name (Viet Nam, Indonesia, Colombia), admin1_name (La Paz, Sucre, Bolívar), latitude (range -25.9531–41.8354), longitude (range -91.9338–178.25).

Outcome / Measurement — value (range 0.0–84.0).

Identifier / Metadata — esa_source (HDX), esa_processed (2026-04-04).

Other — aggregation (max), indicator (tropical-cyclone, #indicator+name).

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Quick Start

from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/africa-climada-tropical-cyclone-dataset")
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
country_name	object	0.0%	Viet Nam, Indonesia, Colombia
admin1_name	object	0.0%	La Paz, Sucre, Bolívar
latitude	float64	0.3%	-25.9531 – 41.8354 (mean 9.9075)
longitude	float64	0.3%	-91.9338 – 178.25 (mean 37.1764)
aggregation	object	0.3%	max
indicator	object	0.0%	tropical-cyclone, #indicator+name
value	float64	0.3%	0.0 – 84.0 (mean 27.2584)
esa_source	object	0.0%	HDX
esa_processed	object	0.0%	2026-04-04

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Numeric Summary

Column	Min	Max	Mean	Median
latitude	-25.9531	41.8354	9.9075	11.7453
longitude	-91.9338	178.25	37.1764	49.3527
value	0.0	84.0	27.2584	28.0

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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. 3 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 ETH Zürich - Weather and Climate Risks and has not been independently validated by ESA.
• Automated cleaning cannot correct for misreported values, definitional inconsistencies, or sampling bias in the original collection.
• This dataset spans 21 countries; geographic and methodological inconsistencies across national boundaries may affect cross-country comparability.
• Refer to the original HDX dataset page for the publisher's own methodology notes and caveats.

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Citation

@dataset{hdx_africa_climada_tropical_cyclone_dataset,
  title     = {Tropical cyclone: Hazard Data for Disaster Risk Assessment (selected countries)},
  author    = {ETH Zürich - Weather and Climate Risks},
  year      = {2025},
  url       = {https://data.humdata.org/dataset/climada-tropical-cyclone-dataset},
  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.