Open-Meteo started as an exercise to process weather model data from the German weather service with up to 2 km resolution. Their forecasts are great, but hard to use for non-data-scientists who regularly work with NetCDF and GRIB-files. Using this data in simple apps, websites, your home-automation software, or robot lawn mower is complex.
The Open-Meteo API makes using this data easier. APIs accept standard WGS84 coordinates and return a weather forecast for 7 days in hourly resolution.
The forecast quality is surprisingly good. Open-Meteo includes global and regional weather models. Global models use 11 km resolution with up to 180 hours of forecast. Local models vary between 2 and 7 km resolution and 48 to 72 hours. Updates every 3 hours. The best model is automatically selected and combined to produce a single 7-day hourly forecast. Currently the best forecast model coverage is in Europe. Models for North America will be integrated next.
Under the hood, all data is stored in binary files using Float16 and updated in-place after a new weather model arrives. The API is very efficient. Returning weather forecasts takes usually less than 5 milliseconds. Internet latency is usually much higher.
All data is offered for non-commercial use. With speedy APIs, all data can be served by just a couple of virtual machines for less than a coffee a day.
What’s next? Some important features are still missing like daily aggregations, additional weather models, ocean, and air quality forecasts. Additionally, I would like to deploy some servers in North America and Asia to improve latency.
The project went live 2 weeks ago and is slowly being used. I would be grateful for feedback, suggestions, ideas, and questions.
All documentation can be found at https://open-meteo.com/en/docs