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dynamical.org

Free, open, cloud-optimized weather and climate datasets — NOAA GFS, GEFS, HRRR, and MRMS; ECMWF IFS and AIFS; DWD ICON-EU; ECCC HRDPS; NASA IMERG — as Icechunk/Zarr archives on AWS Open Data, described by a STAC catalog. Nothing here needs authentication.

For agents: Free cloud-optimized weather and climate datasets (NOAA GFS, GEFS, HRRR, ECMWF, NASA IMERG) as Zarr/Icechunk on AWS, described by a STAC catalog; no auth.

weather dataclimate datasetsnoaa gfszarrstac catalog

Their llms.txt

The beginning of the file, from our validated copy saved 2026-10-23T16:31:52.435Z. Cached copy · Source on dynamical.org

# dynamical.org

> Free, open, cloud-optimized weather and climate datasets — NOAA GFS, GEFS, HRRR, and MRMS; ECMWF IFS and AIFS; DWD ICON-EU; ECCC HRDPS; NASA IMERG — as Icechunk/Zarr archives on AWS Open Data, described by a STAC catalog. Nothing here needs authentication.

The STAC catalog at https://stac.dynamical.org/catalog.json is the source of truth. Each dataset is one STAC Collection with its title and description, license and attribution, spatial and temporal extent, `cube:dimensions`, `cube:variables` (every variable with units, long name, dimensions, and chunk shape), runnable `examples`, and `assets` — the Icechunk repository to open, as `icechunk` (s3://, anonymous) and `icechunk-https`. When this file and STAC disagree, STAC is right.

**Open a dataset.** Two equally supported ways. Both open an Icechunk 2 repository, so the direct route needs `icechunk>=2` (Python 3.12+); Icechunk also has JavaScript and Rust clients that open the same `icechunk-https` asset.

- The `dynamical-catalog` package (latest release): `import dynamical_catalog; ds = dynamical_catalog.open("noaa-gfs-forecast", chunks=None)`
- STAC and Icechunk directly, with any HTTP client for the STAC part:

```python
import icechunk
import pystac
import xarray as xr

catalog = pystac.Catalog.from_file("https://stac.dynamical.org/catalog.json")
asset = catalog.get_child("noaa-gfs-forecast").assets["icechunk-https"]
repo = icechunk.Repository.open(icechunk.http_storage(asset.href))
ds = xr.open_zarr(repo.readonly_session("main").store, chunks=None)
```

Resolve the asset href from STAC every time you open a dataset. Do not hard-code buckets, hrefs, or version numbers: they change when a dataset is reprocessed.

For a time series at one point without any data libraries, use the JSON API (beta, docs at https://dynamical.org/api/): `POST https://api.dynamical.org/v1/forecasts` with `{"queries": [{"dataProductId": "noaa-gfs-forecast", "location": {"latitude": 41.88, "longitude": -87.63}, "variables": ["temperature_2m"]}]}` returns the latest run; `POST /v1/analyses` takes `startTime` and `endTime` instead. Dataset ids double as `dataProductId`; `GET /v1/data-products` lists what the API serves.

`data.dynamical.org/**/latest.zarr` is retired after 2026-09-30. Never generate new code with those URLs; migration guide at https://dynamical.org/migration-2026/.

**Conventions.**

- Forecast datasets have dimensions `init_time` (start of the model run, UTC), `lead_time` (a timedelta from `init_time`; valid time = `init_time` + `lead_time`), and the grid; ensemble datasets add `ensemble_member`. The latest run is the last `init_time`; check that the variables and lead times you need are present there (gaps are NaN) before presenting it as complete.
- Global and European datasets are on `latitude`/`longitude` grids. HRRR and HRDPS keep their native projected grids with `x` and `y` dimensions; the collection's description and example notebook show how to select geographic points and
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1. POST https://agentwebmap.com/v1/sites/dynamical.org/claims and keep claim_id and claim_secret private.
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3. POST https://agentwebmap.com/v1/sites/dynamical.org/verify with {"claim_id": "...", "claim_secret": "..."}.
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