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

---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- en
license: cc-by-4.0
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- other
task_ids: []
tags:
- africa
- humanitarian
- hdx
- electric-sheep-africa
- cyclones-hurricanes-typhoons
- hxl
- ssd
pretty_name: "South Sudan: IBTrACS Storm Tracks"
dataset_info:
splits:
- name: train
num_examples: 3815
- name: test
num_examples: 953
---

# South Sudan: IBTrACS Storm Tracks

**Publisher:** HDX · **Source:** [HDX](https://data.humdata.org/dataset/ssd-ibtracs-tropical-storm-tracks) · **License:** `cc-by-igo` · **Updated:** 2025-05-23

---

## 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](https://huggingface.co/electricsheepafrica).*

---

## 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 |

---

## 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).

---

## Quick Start

```python
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()
```

---

## 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 |

---

## 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 |

---

## 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.

---

## 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](https://data.humdata.org/dataset/ssd-ibtracs-tropical-storm-tracks) for the publisher's own methodology notes and caveats.

---

## Citation

```bibtex
@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)}
}
```

---

*[Electric Sheep Africa](https://huggingface.co/electricsheepafrica) — Africa's ML dataset infrastructure. Lagos, Nigeria.*