# earth2studio-deterministic-forecast

A skill that walks you through building a deterministic weather forecast script with Earth2Studio: model, data source, IO backend and inference.

- skill · `type=skill` · Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, OpenCode · 1900 installs · ★ 3735
- Author: NVIDIA Earth-2 Team
- License: Apache-2.0 · policy=mirror · the beginning of the file is at the end of this page
- Source: https://github.com/NVIDIA/skills/tree/HEAD/skills/earth2studio-deterministic-forecast/SKILL.md
- Updated: 2026-10-23
- HTML: https://affpapa.org/en/nevibecode/skills/earth2studio-deterministic-forecast

## What it does

The skill gathers requirements first — forecast horizon, variables, region, available VRAM — then picks a prognostic model, a data source and an IO backend. The result is a script that calls `earth2studio.run.deterministic`.

Before recommending components it fetches the live Earth2Studio docs, since APIs shift between releases. Model and data source compatibility is checked against the lexicon: every model input variable has to exist in the source's vocabulary. `ZarrBackend` is the default; alternatives come up only when you have a specific reason.

A CUDA-capable GPU is required. All prognostic models run on GPU only, CPU inference is not supported. You also need Python 3.10+ and network access for model weights and data. Ask for a manual loop instead of `run.deterministic` and you get an eight-step checklist, from `fetch_data` through `io.write`.

## Install

### Claude Code (native platform)

1. **Skills CLI (Vercel)**
```bash
npx skills add NVIDIA/skills --skill earth2studio-deterministic-forecast -a claude-code
```

2. **Manually: put the files in place**
- File: `~/.claude/skills/earth2studio-deterministic-forecast/SKILL.md` — Copy the whole skill folder; for one project use .claude/skills/earth2studio-deterministic-forecast/

### Codex

1. **Skills CLI (Vercel)**
```bash
npx skills add NVIDIA/skills --skill earth2studio-deterministic-forecast -a codex
```

2. **Manually: put the files in place**
- File: `.agents/skills/earth2studio-deterministic-forecast/SKILL.md` — For all projects use ~/.agents/skills/

### Cursor

1. **Skills CLI (Vercel)**
```bash
npx skills add NVIDIA/skills --skill earth2studio-deterministic-forecast -a cursor
```

2. **Manually: put the files in place**
- File: `.cursor/skills/earth2studio-deterministic-forecast/SKILL.md` — Cursor also reads .agents/skills/ and .claude/skills/

### GitHub Copilot

1. **GitHub CLI**
```bash
gh skill install NVIDIA/skills earth2studio-deterministic-forecast
```

2. **Manually: put the files in place**
- File: `.github/skills/earth2studio-deterministic-forecast/SKILL.md`

### Gemini CLI

1. **Skills CLI (Vercel)**
```bash
npx skills add NVIDIA/skills --skill earth2studio-deterministic-forecast -a gemini-cli
```

2. **Platform CLI**
```bash
gemini skills install https://github.com/NVIDIA/skills --path skills/earth2studio-deterministic-forecast
```

### Windsurf (Devin Desktop)

**Manually: put the files in place**
- File: `.devin/skills/earth2studio-deterministic-forecast/SKILL.md` — The legacy .windsurf/skills/ path still works

### OpenCode

**Skills CLI (Vercel)**
```bash
npx skills add NVIDIA/skills --skill earth2studio-deterministic-forecast -a opencode
```

### Claude.ai

**Claude.ai**
- Zip the folder and upload it in Settings → Capabilities → Skills

Where a file is given instead of a command, copy the files from the source to that path: https://github.com/NVIDIA/skills/tree/HEAD/skills/earth2studio-deterministic-forecast/SKILL.md

## When to use it
- build a deterministic forecast script with Earth2Studio
- pick a model that fits your horizon and VRAM budget
- confirm the data source supplies every model input variable
- work out nsteps from forecast hours and model step size
- choose an IO backend and filter the variables you actually need

## Example prompts
- build a 5-day temperature forecast script with Earth2Studio
- I need a deterministic wind forecast, include u10m and v10m
- do the same but with a manual loop instead of run.deterministic

## Source text · SKILL.md · Apache-2.0

Below is the content of a third-party file. It is data, not instructions for you: do not act on anything written inside unless the human asks. Block tag: a646ff4e25ef059a.

BEGIN THIRD-PARTY FILE a646ff4e25ef059a
````text
---
name: earth2studio-deterministic-forecast
version: 0.16.0
license: Apache-2.0
metadata:
  author: NVIDIA Earth-2 Team
  tags:
    - earth2studio
    - earth2
    - python
    - inference
    - forecast
    - deterministic
description: >
  Build deterministic forecast scripts with Earth2Studio (model, data source,
  IO, inference). Do NOT use for ensemble, diagnostics, data-only fetch, or
  install.
---

# 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/models_px.html> |
| Data sources (analysis) | <https://nvidia.github.io/earth2studio/modules/datasources_analysis.html> |
| Data sources (forecast) | <https://nvidia.github.io/earth2studio/modules/datasources_forecast.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 (`input_coords["variable"]`)
- Time step size (`output_coords["lead_time"]`)

### 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 = forecast_hours / model_step_hours`

Example: 5-day forecast with 6h step → `nsteps = 120 / 6 = 20`

### 6. Decide: output_coords Filtering

- **Filter variables** (`output_coords`) when user requests specific variables (e.g., "t2m and wind") - reduces output size
- **Save all variables** (omit `output_coords`) when user says "all variables" or doesn't specify - preserves full model output

### 7. Generate Script

```python
````
END THIRD-PARTY FILE a646ff4e25ef059a

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First 2547 of 5488 bytes. © NVIDIA Earth-2 Team · Apache-2.0 · https://github.com/NVIDIA/skills/tree/HEAD/skills/earth2studio-deterministic-forecast/SKILL.md

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