electricsheepafrica/africa-mar-rainfall-subnational

Morocco: Rainfall Indicators at Subnational Level

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

---

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: MAR.

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

---

Dataset Characteristics

Domain	Climate and environment
Unit of observation	Time-series observations
Rows (total)	112,470
Columns	17 (12 numeric, 4 categorical, 1 datetime)
Train split	89,976 rows
Test split	22,494 rows
Geographic scope	MAR
Publisher	WFP - World Food Programme
HDX last updated	2026-04-03

---

Variables

Geographic — n_pixels (range 2.0–2704.0).

Temporal — date.

Identifier / Metadata — adm_id (range 2107.0–999616.0), pcode (MA002, MA003, MA004), esa_source (HDX), esa_processed (2026-04-06).

Other — adm_level (range 1.0–2.0), rfh (range 0.0–265.0748), rfh_avg (range 0.0–63.4745), r1h (range 0.0–617.1176), r1h_avg (range 0.0–156.7315) and 6 others.

---

Quick Start

from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/africa-mar-rainfall-subnational")
train = ds["train"].to_pandas()
test  = ds["test"].to_pandas()

print(train.shape)
train.head()

---

Schema

Column	Type	Null %	Range / Sample Values
date	datetime64[ns]	0.0%
adm_level	int64	0.0%	1.0 – 2.0 (mean 1.7826)
adm_id	int64	0.0%	2107.0 – 999616.0 (mean 408742.5797)
pcode	object	0.0%	MA002, MA003, MA004
n_pixels	float64	0.0%	2.0 – 2704.0 (mean 458.5652)
rfh	float64	0.0%	0.0 – 265.0748 (mean 10.2848)
rfh_avg	float64	0.0%	0.0 – 63.4745 (mean 10.8049)
r1h	float64	0.1%	0.0 – 617.1176 (mean 30.8701)
r1h_avg	float64	0.1%	0.0 – 156.7315 (mean 32.3882)
r3h	float64	0.5%	0.0 – 1067.1307 (mean 92.4746)
r3h_avg	float64	0.5%	0.1484 – 391.8053 (mean 97.0219)
rfq	float64	0.0%	11.0839 – 694.696 (mean 97.7944)
r1q	float64	0.1%	8.8707 – 505.7507 (mean 96.9603)
r3q	float64	0.5%	17.4083 – 370.8018 (mean 96.1768)
version	object	0.0%	final, prelim, forecast
esa_source	object	0.0%	HDX
esa_processed	object	0.0%	2026-04-06

---

Numeric Summary

Column	Min	Max	Mean	Median
adm_level	1.0	2.0	1.7826	2.0
adm_id	2107.0	999616.0	408742.5797	147350.0
n_pixels	2.0	2704.0	458.5652	248.0
rfh	0.0	265.0748	10.2848	5.1034
rfh_avg	0.0	63.4745	10.8049	9.0767
r1h	0.0	617.1176	30.8701	19.4706
r1h_avg	0.0	156.7315	32.3882	27.771
r3h	0.0	1067.1307	92.4746	70.9076
r3h_avg	0.1484	391.8053	97.0219	83.0039
rfq	11.0839	694.696	97.7944	94.2494
r1q	8.8707	505.7507	96.9603	93.362
r3q	17.4083	370.8018	96.1768	90.9171

---

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.

---

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.

---

Citation

@dataset{hdx_africa_mar_rainfall_subnational,
  title     = {Morocco: Rainfall Indicators at Subnational Level},
  author    = {WFP - World Food Programme},
  year      = {2026},
  url       = {https://data.humdata.org/dataset/mar-rainfall-subnational},
  note      = {Repackaged for machine learning by Electric Sheep Africa (https://huggingface.co/electricsheepafrica)}
}

---

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