electricsheepafrica/africa-sle-rainfall-subnational

Sierra Leone: Rainfall Indicators at Subnational Level

Publisher: WFP - World Food Programme · Source: HDX · License: cc-by · Updated: 2026-04-03

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Abstract

This dataset contains dekadal rainfall indicators, computed from Climate Hazards Group InfraRed Precipitation satellite imagery with insitu Station data (CHIRPS) version 2 and the CHIRPS-GEFS short term rainfall forecasts, aggregated by subnational administrative units.

Included indicators are (for each dekad):

• 10 day rainfall [mm] (rfh)
• rainfall 1-month rolling aggregation [mm] (r1h)
• rainfall 3-month rolling aggregation [mm] (r3h)
• rainfall long term average [mm] (rfh_avg)
• rainfall 1-month rolling aggregation long term average [mm] (r1h_avg)
• rainfall 3-month rolling aggregation long term average [mm] (r3h_avg)
• rainfall anomaly [%] (rfq)
• rainfall 1-month anomaly [%] (r1q)
• rainfall 3-month anomaly [%] (r3q)

The administrative units used for aggregation are based on WFP data and contain a Pcode reference attributed to each unit. The number of input pixels used to create the aggregates, is provided in the n_pixels column. Finally, the type column indicates if the value is based on a forecast, a preliminary or a final product.

Forecasts are issued on the 6th, 16th, and 26th of each month for the upcoming 10-day period (dekad), then updated with improved versions on the 1st, 11th, and 21st. Preliminary observations replace the previous dekad’s forecast on the 3rd, 13th, and 23rd, and are later replaced by final observations—published mid-month (13th or 23rd)—covering all three dekads of the prior month. Please find a summary below:

Publication Day: Forecast type, Covers (Dekad)

• 1st: Updated forecast, 1–10 of the same month
• 6th: Initial forecast, 11–20 of the same month
• 11th: Updated forecast, 1–10 of the same month
• 16th: Initial forecast, 21–end of the same month
• 21st: Updated forecast, 11–20 of the same month
• 26th: Initial forecast, 1–10 of the following month

For more on CHIRPS-GEFS forecasts, see: https://www.chc.ucsb.edu/data/chirps-gefs

For further details, please see the methodology section.

Each row in this dataset represents time-series observations. Temporal coverage is indicated by the date column(s). Geographic scope: SLE.

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

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Dataset Characteristics

Domain	Climate and environment
Unit of observation	Time-series observations
Rows (total)	34,230
Columns	17 (12 numeric, 4 categorical, 1 datetime)
Train split	27,384 rows
Test split	6,846 rows
Geographic scope	SLE
Publisher	WFP - World Food Programme
HDX last updated	2026-04-03

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Variables

Geographic — n_pixels (range 3.0–741.0).

Temporal — date.

Identifier / Metadata — adm_id (range 900443.0–1006463.0), pcode (SL01, SL0502, SL0401), esa_source (HDX), esa_processed (2026-04-08).

Other — adm_level (range 1.0–2.0), rfh (range 0.3936–787.6667), rfh_avg (range 0.669–335.6889), r1h (range 1.6968–1701.6667), r1h_avg (range 2.3823–914.5444) and 6 others.

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Quick Start

from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/africa-sle-rainfall-subnational")
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
date	datetime64[ns]	0.0%
adm_level	int64	0.0%	1.0 – 2.0 (mean 1.7619)
adm_id	int64	0.0%	900443.0 – 1006463.0 (mean 981214.9048)
pcode	object	0.0%	SL01, SL0502, SL0401
n_pixels	float64	0.0%	3.0 – 741.0 (mean 226.4762)
rfh	float64	0.0%	0.3936 – 787.6667 (mean 75.557)
rfh_avg	float64	0.0%	0.669 – 335.6889 (mean 75.3881)
r1h	float64	0.1%	1.6968 – 1701.6667 (mean 226.9214)
r1h_avg	float64	0.1%	2.3823 – 914.5444 (mean 226.398)
r3h	float64	0.5%	7.6961 – 3646.0 (mean 683.0364)
r3h_avg	float64	0.5%	11.4716 – 2389.1 (mean 681.0995)
rfq	float64	0.0%	17.1071 – 640.8437 (mean 99.5748)
r1q	float64	0.1%	22.9156 – 382.4611 (mean 99.1716)
r3q	float64	0.5%	27.3344 – 333.653 (mean 99.4682)
version	object	0.0%	final, prelim, forecast
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
adm_level	1.0	2.0	1.7619	2.0
adm_id	900443.0	1006463.0	981214.9048	1006453.0
n_pixels	3.0	741.0	226.4762	181.0
rfh	0.3936	787.6667	75.557	51.5701
rfh_avg	0.669	335.6889	75.3881	50.8438
r1h	1.6968	1701.6667	226.9214	164.3903
r1h_avg	2.3823	914.5444	226.398	162.074
r3h	7.6961	3646.0	683.0364	536.6228
r3h_avg	11.4716	2389.1	681.0995	537.6967
rfq	17.1071	640.8437	99.5748	95.8535
r1q	22.9156	382.4611	99.1716	96.5084
r3q	27.3344	333.653	99.4682	98.3965

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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. 1 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 WFP - World Food Programme 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_sle_rainfall_subnational,
  title     = {Sierra Leone: Rainfall Indicators at Subnational Level},
  author    = {WFP - World Food Programme},
  year      = {2026},
  url       = {https://data.humdata.org/dataset/sle-rainfall-subnational},
  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.