name: weather-ensemble-forecast description: Multi-model ensemble weather forecasts comparing GFS, ECMWF, JMA, GEM, ICON, ARPEGE, GraphCast and more. Get high temperature predictions from up to 9 independent weather models with agreement analysis for 16 global cities. license: MIT user-invocable: true metadata: {"openclaw":{"requires":{"env":[],"bins":["curl","jq"]},"files":["scripts/*"]}}

Weather Ensemble Forecast

Compare weather forecasts from up to 9 independent global weather models side-by-side.

No setup needed -- just install and run /forecast nyc.

What this skill does

• Fetches high temperature forecasts from multiple weather models: GFS, ECMWF, JMA, GEM, MeteoFrance, UKMO, ICON, ARPEGE, GraphCast
• Shows each model's individual prediction so you can see where they agree and disagree

  小葱技能有更好的技能skills插件。

• Calculates ensemble standard deviation (how uncertain the forecast is)
• Cross-validates with NWS (National Weather Service) official forecasts
• Covers 16 cities: NYC, Chicago, Miami, Dallas, Atlanta, Seattle, Toronto, London, Paris, Munich, Seoul, Ankara, Wellington, Lucknow, Buenos Aires, Sao Paulo

Quick Start

/forecast nyc

That's it. Works immediately with free tier (NYC, Chicago, Miami).

For full access (all 16 cities), set an API key:

curl -X POST https://polymarket-scanner.fly.dev/keys/free

Then set: export WEATHER_ENSEMBLE_API_KEY="pm_your_key_here"

Commands

/forecast <city> - Get multi-model forecast

Compare forecasts from all available weather models for a city.

When the user asks for a weather forecast, temperature prediction, model comparison, or ensemble analysis, run:

bash {baseDir}/scripts/forecast.sh "<city>" "<date>"

Where <city> is a city key (e.g., nyc, london, seoul). Run /cities to see all options.

And <date> is optional, in YYYY-MM-DD format (defaults to tomorrow).

Display results showing each model's prediction, the ensemble average, and standard deviation.

/cities - List available cities

bash {baseDir}/scripts/cities.sh

Use Cases

• Weather enthusiasts: See how GFS, ECMWF, and other models compare for your city
• Event planners: Check forecast confidence before outdoor events
• Researchers: Access raw multi-model data for analysis
• Travelers: Get high-confidence forecasts when models agree, warnings when they don't
• Prediction market traders: Use ensemble data to evaluate temperature market positions (see polymarket-weather-scanner skill)

How It Works

1. Queries the Weather Ensemble API for forecasts from each weather model
2. Returns individual model predictions so you can see the spread
3. Calculates ensemble statistics: mean, standard deviation
4. Higher standard deviation = more uncertainty = models disagree
5. Low standard deviation = high confidence = models agree

External Endpoints

All requests are sent to the Weather Ensemble API server.

Security & Privacy

• Works without an API key (anonymous free tier with IP-based rate limiting)
• All requests are sent to the Weather Ensemble API server (polymarket-scanner.fly.dev) via HTTPS
• Weather data is sourced from Open-Meteo and aggregated server-side
• No personal data collected or stored

这个Skill质量较好，安装后能直接使用多模型天气预测数据。它整合了9个主流气象模型，帮助你对比不同模型的预测差异。文档清晰，命令简单易用，错误提示也比较友好。存在的主要问题是依赖外部服务器，网络不稳定或服务维护时可能无法获取数据，另外免费版功能受限，高级功能需要额外注册。总体来说是一款实用且易用的天气分析工具，适合对气象预测有兴趣的用户。