Agent skill · software engineering · nvidia
earth2studio-deterministic-forecast
Build deterministic forecast scripts with Earth2Studio (model, data source, IO, inference). Do NOT use for ensemble, diagnostics, data-only fetch, or install.
Why this skill is useful
Provides a detailed workflow and executable script for building deterministic weather forecast models using Earth2Studio, which is not commonly found in public documentation.
What it needs
Requires earth2studio, python installed locally. About 3k tokens when loaded. Last updated 2026-08-07. 2,816 stars on the source repository.
What this skill does
Earth2Studio Deterministic Forecast Skill Guide users through building deterministic (single-member) weather forecast inference scripts using earth2studio.run.deterministic. Prerequisites Earth2Studio installed with CUDA-capable GPU Python 3.10+, network access for model weights and data Live Doc References Fetch relevant docs to verify current APIs before recommending components: Component URL ----------- ----- Prognostic models <https://nvidia.github.io/earth2studio/modules/modelspx.html> Data sources (analysis) <https://nvidia.github.io/earth2studio/modules/datasourcesanalysis.html> Data sources (forecast) <https://nvidia.github.io/earth2studio/modules/datasourcesforecast.html> IO backends <https://nvidia.github.io/earth2studio/modules/io.html> run.deterministic <https://github.com/NVIDIA/earth2studio/blob/main/earth2studio/run.py> Workflow 1. Gather Requirements (skip what's already provided) Time horizon (hours/days/weeks) Variables of interest (t2m, wind, geopotential, etc.) Region (global or specific like CONUS) GPU/VRAM available 2. Select Model Fetch prognostic models page. Filter by time horizon, region, VRAM. Note model's: Input variables (inputcoords["variable"]) Time step size (outputcoords["leadtime"]) 3. Select Data Source Data source must provide all model input variables. Verify via lexicon at earth2studio/lexicon/<source>.py. Common pairings: Global models → GFS/ARCO/IFS; Regional → HRRR. 4. Select IO Backend Default: ZarrBackend. Use NetCDF4Backend for legacy tools, XarrayBackend for in-memory/small runs. 5. Calculate nsteps nsteps = forecasthours / modelstephours Example: 5-day forecast with 6h step → nsteps = 120 / 6 = 20 6. Decide: outputcoords Filtering Filter variables (outputcoords) when user requests specific variables (e.g., "t2m and wind") - reduces output size Save all variables (omit outputcoords) when user says "all variables" or doesn't specify - preserves full model output 7. Generate Script 8. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills nvidia/earth2studio-deterministic-forecast