Open-Meteo
L o a d i n g
Open-Meteo operates an open-source weather API with free access for non-commercial use.
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- The ensemble models are a type of weather forecasting technique that use multiple members or versions of a model to produce a range of possible outcomes for a given forecast. Each member is initialized with slightly different initial conditions and/or model parameters to account for uncertainties and variations in the atmosphere, resulting in a set of perturbed forecasts. By combining the perturbed forecasts, the ensemble model generates a probability distribution of possible outcomes, indicating not only the most likely forecast but also the range of possible outcomes and their likelihoods. This probabilistic approach provides more comprehensive and accurate forecast guidance, especially for high-impact weather events where uncertainties are high. Different national weather services calculate ensemble models, each with varying resolutions of weather variables and forecast time-range. For instance, the German weather service DWD's ICON model provides exceptionally high resolution for Europe but only forecasts up to 7 days. Meanwhile, the GFS model can forecast up to 35 days, albeit at a lower resolution of 50 km. The appropriate ensemble model to use would depend on the forecast horizon and region of interest.1Licence not specifiedalmost 2 years ago
- This API uses global NOAA GFS weather forecast and combines them with high-resolution HRRR forecasts. HRRR is a rapid-refresh model and updates every hour. High-resolution data are only available for the United States. For other locations, only GFS is used. For GFS, values are interpolated from 3-hourly to 1-hourly after 120 hours.1Licence not specifiedalmost 2 years ago
- Open-Meteo Air Quality API forecast is based on the 11-kilometer CAMS European air quality forecast and the 40-kilometer CAMS global atmospheric composition forecasts. The European and global domains are not coupled and may show different forecasts.1Licence not specifiedalmost 2 years ago
- The Historical Weather API is based on reanalysis datasets and uses a combination of weather station, aircraft, buoy, radar, and satellite observations to create a comprehensive record of past weather conditions. These datasets are able to fill in gaps by using mathematical models to estimate the values of various weather variables. As a result, reanalysis datasets are able to provide detailed historical weather information for locations that may not have had weather stations nearby, such as rural areas or the open ocean. The spatial resolution of a weather reanalysis dataset refers to the size of the grid cells used to represent the data. In the case of the reanalysis dataset described, the cells covering land have a resolution of 11 km, while those covering the ocean have a resolution of 25 km. For the region of Europe, the resolution is increased to 5 km, although this higher resolution is only available up until June 2021, with real-time updates expected to be available sometime in 2023. In general, a higher spatial resolution means that the data is more detailed and represents the weather conditions more accurately at smaller scales. The historic API takes the same parameters and gives the same response as the forecast API, but adds start_date and end_date1Licence not specifiedalmost 2 years ago
- Open-Meteo weather forecast APIs use weather models from multiple national weather providers. For each location worldwide, the best models will be combined to provide the best possible forecast. Weather models cover different geographic areas at different resolutions and provide different weather variables. Depending on the model, data have been interpolated to hourly values or not all weather variables are available. The API endpoint /v1/forecast accepts a geographical coordinate, a list of weather variables and responds with a JSON hourly weather forecast for 7 days. Time always starts at 0:00 today and contains 168 hours.1Licence not specifiedalmost 2 years ago
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