A specialized tool within the Openclaw Skills library that compares high temperature predictions from up to nine independent global weather models to provide high-accuracy forecasts.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install weather-ensemble-forecast
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install weather-ensemble-forecast using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is Weather Ensemble Forecast?
Weather Ensemble Forecast is a powerful addition to the Openclaw Skills ecosystem designed for users who need more than just a single forecast. By aggregating data from leading global models like GFS, ECMWF, JMA, and ICON, it allows users to see the variance between different meteorological predictions in one view. This skill provides a deeper level of insight by calculating ensemble standard deviation, helping you understand whether a forecast is highly certain or subject to model disagreement.
Built for developers and weather enthusiasts, this skill bridges the gap between raw meteorological data and actionable intelligence. Whether you are using it as a standalone tool or integrating it into a larger automated workflow, it represents the precision and utility expected from the highest quality Openclaw Skills. It ensures that you are never reliant on a single source of truth for critical weather-related decisions.
Weather Ensemble Forecast Use Cases
- Weather enthusiasts: Compare GFS, ECMWF, and other major models side-by-side for your specific city.
- Event planners: Evaluate forecast confidence levels before committing to outdoor venues or logistics.
- Researchers: Access structured multi-model data for climate or meteorological analysis via terminal.
- Travelers: Gain high-confidence forecasts when models align or get early warnings when they diverge.
- Prediction market traders: Utilize ensemble statistics to evaluate positions in temperature-based financial markets.
How Weather Ensemble Forecast Works
- The skill queries the dedicated Weather Ensemble API to retrieve current data for the requested city and date.
- It fetches individual high-temperature predictions from up to nine independent models, including GraphCast and ARPEGE.
- The system calculates ensemble statistics, specifically the mean temperature and the standard deviation across all models.
- It interprets the results where a low standard deviation indicates high model agreement, while a high one suggests significant uncertainty.
- The final comparison is presented to the user, allowing for quick cross-validation between global models and official local forecasts.
Weather Ensemble Forecast Setup
The Weather Ensemble Forecast skill is designed to be plug-and-play within your environment. Ensure you have curl and jq installed on your system.
To use the basic features for major cities like NYC or Chicago, simply run:
/forecast nyc
For full access to all 16 supported global cities, generate a free API key using the following command:
curl -X POST https://polymarket-scanner.fly.dev/keys/free
Then, configure your environment variable to enable the full suite of Openclaw Skills features:
export WEATHER_ENSEMBLE_API_KEY="pm_your_key_here"
Weather Ensemble Forecast Data Schema & Taxonomy
This skill interacts with the Weather Ensemble API and organizes data into a structured format for easy interpretation. The primary data points include individual model outputs and aggregate statistics presented in the output.
| Attribute | Description |
|---|---|
| Model Name | The specific source of the forecast (e.g., GFS, ECMWF, GEM, ICON) |
| Temperature | The predicted high temperature for the target date from that specific model |
| Ensemble Mean | The average temperature calculated across all reporting models |
| Std Dev | Measures model variance; lower numbers indicate higher forecast confidence |
| City Key | Normalized city identifier used for queries (e.g., nyc, lon, sel) |
Weather Ensemble Forecast Advanced Features
- Multi-model cross-validation including advanced AI-driven GraphCast model predictions.
- Automated uncertainty analysis via standard deviation calculations to gauge forecast reliability.
- Integration support for trading workflows, specifically designed to complement other Openclaw Skills used for market scanning.
- Support for 16 major global hubs across North America, Europe, Asia, and South America.
- Stateless API interactions ensuring privacy and high-speed data retrieval without local storage overhead.
SKILL.md
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