The Hybrid Coordinate Ocean Model (HYCOM) is an open-source data-assimilative computer model used to simulate and predict ocean circulation, temperature, salinity, and water velocity. These are Icechunks from the data on AWS Open Data.
🌐 View data in browser · 💻 Data access (code) · 📦 Data access (HYCOM.org)
HYCOM ocean model output, published as
Icechunk stores on
Source Cooperative so that a whole archive
opens in about a second as one lazy xarray datacube.
This is the first of several planned HYCOM icechunks.
The stores are virtual: each holds only Zarr metadata and byte-range references to the NetCDF files on AWS Open Data.
⚠️ The data will not draw unless you disable CORS
The data arrays are read from the provider's bucket, and the GOFS 3.1 reanalysis bucket on AWS has no CORS configuration (checked 2026-09-19), so your browser will refuse to hand those bytes to the page. To look at the data anyway, you can install a CORS-disabling browser extension (search your browser's extension store for "CORS unblock" or "Allow CORS"). This affects browsers only; loading data via code works normally.
Required: Python ≥ 3.12, icechunk ≥ 2.2. No credentials are needed. On older Python,
pip install icechunk quietly installs icechunk 1.x, which will not work.
One directory per dataset in the
GitHub repository, each with its own README —
coverage, variables, gaps, known problems, what to expect from reads — plus its build
notebooks, requirements.txt and source manifest. On Source Cooperative the same files are
under docs/<dataset>/.
Each dataset is built in the same order: a written plan, a smoke test into a temporary local
repository, the same test written to ocean-icechunks/test-repo/hycom/ with a viewer and a
short README, and only then the published store, which is validated anonymously from its
public URL against an independent reader.
This work is released under Apache-2.0. You are free to use, copy, modify, and redistribute it, including commercially. If you use it in published work, in a presentation, or in another repository, please give attribution:
Holmes, E.E. and Signell, R. (2026). HYCOM as virtual Icechunk stores. ocean-icechunks/hycom. https://github.com/ocean-icechunks/hycom
Rich Signell is named because this work rests on his: he showed that the uncompressed NetCDF-3 files of this archive can be given virtual chunks, one per depth level, without rewriting them. None of his code is used here, but the approach is his. Please cite it too:
Signell, R. (2024). Using Kerchunk with uncompressed NetCDF 64-bit offset files: Cloud-optimized access to HYCOM Ocean Model output on AWS Open Data. Pangeo (Medium). https://medium.com/pangeo/using-kerchunk-with-uncompressed-netcdf-64-bit-offset-files-cloud-optimized-access-to-hycom-ocean-9008ba6d0d67
Signell, R. (2024). hycom-kerchunk. https://github.com/rsignell/hycom-kerchunk
The icechunk stores do not contain the HYCOM source data — only references to the provider's files. Each dataset's README gives its terms and the acknowledgement its provider asks for. For HYCOM data, hycom.org recommends:
Funding for the development of HYCOM has been provided by the National Ocean Partnership Program and the Office of Naval Research. Data assimilative products using HYCOM are funded by the U.S. Navy. Computer time was made available by the DoD High Performance Computing Modernization Program. The output is publicly available at https://hycom.org.
hycom/viewer/, is
built from a fork, eeholmes/gridlook.