Samudra is a global ocean emulator described in "Samudra: An AI Global Ocean Emulator for Climate" and updated in "Samudra 2: Scaling Ocean Emulators across Resolutions". Samudra efficiently emulates the ocean component of a state-of-the-art climate model, accurately reproducing key ocean variables including sea surface height, horizontal velocities, temperature, and salinity, across their full depth.
Ground truth (left) vs. Samudra 2 prediction (right) for sea surface temperature in the tropical Pacific.
We are actively and openly developing this emulator to support new tasks and data sources with the goal of building a broadly useful foundation model for ocean and climate. Please see our docs for more or our contributing guide to join in!
To configure, train, validate, and visualize Samudra 2 in a browser on public data with a Colab GPU, open the free-tier Colab quickstart.
Given uv is installed:
# Launch a training run with the default Samudra configuration
uvx samudra train samudra_om4/train.yaml --data @data/om4_demo.yaml
# Run a long autoregressive rollout against ground-truth data, collecting metrics.
uvx samudra eval samudra_om4/eval.yaml --data @data/om4_demo.yaml
# Generate maps, time series, and probability density plots from eval outputs.
uvx samudra viz samudra_om4/viz.yaml --data @data/om4_demo.yaml
To learn more, check out the installation and quick start guides.
