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Quick start

This is the fastest way to try Open Climate Service locally with the built-in datasets: clone the repository, configure an extent, start the service, then ingest and view data from the web interface.

Running a service for a country?

For an operational deployment — custom datasets, version pinning, configuration kept in its own repository — the instance guide is the recommended path. Most people running a real climate service should start there. This quick start is for evaluating the service or developing against the core repository.

Prerequisites

  • Python 3.12 or higher
  • uv for dependency management
  • Git
  • Make (make — available by default on macOS and most Linux distributions; on Windows use WSL or run the commands in the Makefile directly)

Step 1: Clone and install

This quick start runs from a clone of the core repository, which is best for evaluating the service or developing against it. To run an instance from the released PyPI package instead — in its own repository, without cloning this one — follow the instance guide.

git clone https://github.com/dhis2/open-climate-service.git
cd open-climate-service
make sync

Step 2: Configure the spatial extent

The repo includes climate-service.yaml.example with Sierra Leone as a starting point. Copy it to climate-service.yaml (which is gitignored, so your local extent stays out of version control) and replace the entry with your area of interest:

cp climate-service.yaml.example climate-service.yaml

id: rwanda-climate-service
name: Rwanda Climate Service

extent:
  name: Rwanda
  bbox: [28.8, -2.9, 30.9, -1.0]    # [xmin, ymin, xmax, ymax] in WGS84
  country_code: RWA                   # ISO 3166-1 alpha-3, required for WorldPop

data_dir: ./data

bbox is required; country_code is needed for WorldPop downloads. To find a bounding box, bboxfinder.com is a useful tool. For the full list of configuration fields, see the instance guide.

Step 3: Configure environment variables

cp .env.example .env

CLIMATE_SERVICE_CONFIG=./climate-service.yaml is already set, and the remaining defaults are enough to run the API and ingest CHIRPS3 and WorldPop data. For ERA5-Land downloads, see ERA5-Land datasets.

Step 4: Start the service

make run

The service starts on http://127.0.0.1:9000. Open it in a browser — the overview reports what the instance holds and links to the dataset templates you can ingest, the datasets you hold, and the map viewer.

Step 5: Ingest and view data

Open Dataset templates from the navigation and ingest your first dataset from its page. CHIRPS3 (daily precipitation) requires no API key and is a good one to start with — open it, enter a start date, and ingest (leave Publish checked). Progress streams live, and the dataset's page opens when it finishes.

Then open http://127.0.0.1:9000/map to view it on the map, stepping through time with the slider.

See Using the web interface for a full walkthrough of the management and map-viewer pages.

Prefer the API?

Everything the interface does is also available over HTTP. To ingest, sync, discover, and open data programmatically, see Accessing data and the API reference.

Next steps

  • Instance guide — set up a versioned instance repository for your country, with custom datasets and deliberate upgrades (the recommended path for operators).
  • Using the web interface — manage ingestion, sync, and the map viewer.
  • Accessing data — discover and open datasets from Python or any tool.
  • Adding custom datasets — add sources beyond the built-in templates.