Built-in datasets¶
The Open Climate Service ships with built-in dataset templates covering precipitation, temperature, and population. Each template describes an upstream data source and the rules for downloading, transforming, and syncing it. They are available in every instance without any additional configuration.
To ingest a built-in dataset for your configured extent, see the API reference. To add datasets beyond these, see Adding custom datasets.
CHIRPS v3 — daily precipitation¶
| Property | Value |
|---|---|
| Dataset ID | chirps3_precipitation_daily |
| Variable | precip |
| Units | mm |
| Period | Daily |
| Spatial coverage | Global land, 50°S–50°N |
| Spatial resolution | ~5 km |
| Record start | 1981-01-01 |
| Source | CHIRPS v3 |
CHIRPS (Climate Hazards Group InfraRed Precipitation with Station data) v3 is a quasi-global daily precipitation dataset merging satellite thermal infrared imagery with station observations. It is widely used for drought monitoring, food security analysis, and WASH planning in low- and middle-income countries.
Sync behaviour — new data is ingested incrementally as it becomes available. CHIRPS has a nominal publication lag of around 3–7 days, so data through yesterday is not always present. The API uses a custom availability function that checks the actual latest available date from the CHIRPS server before each sync.
Transforms — none applied; data is stored as received in mm.
CHIRPS v3 — monthly precipitation¶
| Property | Value |
|---|---|
| Dataset ID | chirps3_precipitation_monthly |
| Variable | precip |
| Units | mm/d |
| Period | Monthly |
| Spatial coverage | Global land, 50°S–50°N |
| Spatial resolution | ~5 km |
| Record start | 1981-01 |
| Source | CHIRPS v3 |
The monthly CHIRPS product, published as one global raster per calendar month. Prefer it over aggregating the daily dataset when you only need monthly values: it is roughly 30× less data to ingest, and drought indices such as SPI and SPEI are conventionally computed monthly.
Sync behaviour — as for the daily dataset, the latest published month is discovered by probing the CHIRPS server rather than assuming a fixed lag. The monthly final product typically trails the calendar by a month or two.
Transforms — the source raster is a monthly total in mm; it is divided by the number of days in the month and stored as a mean daily rate (mm/d). Unlike the daily dataset, where mm and mm/day are the same number, this is a real conversion.
That choice keeps every monthly precipitation dataset on the same units, which matters because chirps3_precipitation_monthly_normal_1991_2020 is also mm/d and is the natural partner for a monthly anomaly — storing the raw total under the same label would make that comparison wrong by a factor of about 30. It is also the form xclim's drought indices expect.
ERA5-Land — temperature and precipitation¶
ERA5-Land provides temperature and precipitation at hourly, daily, and monthly resolution. Nine dataset templates are available covering both variables and all resolutions, with options for UTC or local-timezone daily aggregation.
See ERA5-Land datasets for the full reference, including dataset IDs, coverage, lag times, and guidance on choosing the right dataset for your use case.
WorldPop Global2 — total population (yearly)¶
| Property | Value |
|---|---|
| Dataset ID | worldpop_population_global2_100m |
| Variable | pop_total |
| Units | people |
| Period | Yearly |
| Spatial coverage | Global |
| Spatial resolution | ~100 m |
| Record start | 2015 |
| Record end | 2030 |
| Source | WorldPop Global2 |
WorldPop Global2 provides gridded population estimates and projections at 100 m resolution. Each raster year represents estimated residential population counts. Years up to and including the present are backward-modelled estimates; years beyond the present are forward projections.
Sync behaviour — population data is released year by year, not as a continuous stream. The API uses a release-kind sync that checks each calendar year separately. Future years (projections) are also requestable, since the underlying data covers through 2030.
Transforms — none applied; values are stored as received (population counts per pixel).
WorldPop Global2 — population by age and sex (yearly)¶
| Property | Value |
|---|---|
| Dataset ID | worldpop_agesex_global2_100m |
| Variable | population (over sex and age_group dimensions) |
| Units | people |
| Period | Yearly |
| Spatial coverage | Per-country (set extent.country_code) |
| Spatial resolution | ~100 m |
| Record start | 2015 |
| Record end | 2030 |
| Source | WorldPop Global2 age & sex structures |
Population disaggregated by sex and 5-year age band. Population is the quantity; sex and age are both disaggregation dimensions of it — so WorldPop's ~40 per-(sex, age) GeoTIFFs per country-year are combined into a single population variable over a sex dimension (female, male) and an ordinal age_group dimension (the lower bound of each band: 0, 1, 5, 10, … 90).
Temporal resampling¶
Any ingested dataset can be resampled to a coarser temporal resolution (e.g. hourly → daily, daily → monthly) using the standard openEO aggregate_temporal_period process in a process graph. See Processes for an example.