OpenmeteoPy
Download Meteorological Data from OPEN-METEO API (https://open-meteo.com/en)
Open-Meteo collaborates with National Weather Services providing Open Data with 11 to 2 km resolution. Our high performance APIs select the best weather model for your location and provide data as a simple JSON API.
APIs are free without any API key for open-source developers and non-commercial use. You can embed them directly into your app.
What is it?
openmeteopy is a client Python wrapper library for Open-Meteo web API. It allows quick and easy consumption of Open-Meteo data from Python applications via a simple object model and in a human-friendly fashion.
You can use all Openmeteo's available APIs,which are 14 (explained in the table below).
openmeteopy runs on Python 3.6+.
Full documentation is reported here: DOCUMENTATION.
Installation (pypi)
working on itInstallation (source)
pip install git+https://github.com/m0rp43us/openmeteopyAPIs
| API | Description | Documentation | Options Class | Hourly Class | Daily Class | 15 Minutes Class |
|---|---|---|---|---|---|---|
| Weather forecast | Select your location, weather variables and start using the API. | Weather Forecast | ForecastOptions() | HourlyForecast()|DailyForecast() |
- | ||
| Historical Weather | Discover how weather has shaped our world from 1940 until now | Historical Weather | HistoricalOptions() | HourlyHistorical()|DailyHistorical() |
- | ||
| ECMWF Weather Forecast | Global High Frequency Forecasts at 0.4° resolution | ECMWF Weather Forecast | EcmwfOptions() | HourlyEcmwf() |
- | - | |
| GFS & HRRR Forecast | Global GFS model combined with hourly HRRR updates at 3-km resolution | GFS & HRRR Forecast | GfsOptions() | HourlyGfs()|DailyGfs() |
- | ||
| MeteoFrance | Global ARPEGE model combined with high resolution AROME model | MeteoFrance | MeteoFranceOptions() | HourlyMeteoFrance()|DailyMeteoFrance() |
- | ||
| DWD ICON | Open data weather forecasts from the German weather service DWD | DWD ICON | DwdOptions() | HourlyDwd()|DailyDwd()|FifteenMinutesDwd() |
|||
| JMA | 5-km high resolution forecasts for Japan, Korea, parts of China and Russia | JMA | JmaOptions() | HourlyJma()|DailyJma() |
- | ||
| MET Norway | Hourly updates & 1 km forecasts for Scandinavia | MET Norway | MetnoOptions() | HourlyMetno() |
- | - | |
| GEM | 2.5 km high resolution forecasts for North America | GEM | GemOptions() | HourlyGem()|DailyGem() |
- | ||
| Marine Weather | Hourly wave forecasts at 5 km resolution | Marine Weather | MarineOptions() | HourlyMarine()|DailyMarine() |
- | ||
| Air Quality | Pollutants and pollen forecast in 11 km resolution | Air Quality | AirQualityOptions() | HourlyAirQuality() |
- | - | |
| Geocoding | Search locations in any language globally | Geocoding | GeocodingOptions() |
- | - | - |
| Elevation | 90 meter resolution digital elevation model | Elevation | ElevationOptions() |
- | - | - |
| Global Flood | Simulated river discharge at 5 km resolution from 1984 up to 7 months forecast | Global Flood | FloodOptions() | - |DailyForecast() |
- |
Output formats:
You can get your output in python dictionary, json string, pandas DataFrame, numpy array. Additionally, you can save APIs response to files in csv, excel or json format.
| Output | Library function |
|---|---|
| dict (full server response) | client._fetch() |
| dict (cleaned version) | client.get_dict() |
| json string (cleaned version) | client.get_json_str() |
| numpy array | client.get_numpy() |
| pandas DataFrame (keys are dates,value are correspongding values) | client.get_pandas() |
| save to csv (keys are dates,value are correspongding values) | save_csv(<filepath>) |
| save to excel (keys are dates,value are correspongding values) | save_excel(<filepath>) |
| save to json | save_json(<filepath>) |
Upcoming Changes,updates,things to do :
- Add the other 2 variables to ECWMF pressure level parameters
- remove pressure level variables from JMA (Remove inheritence from FranceMeteo)
- Add Support of Date Types (input and output)
- Add versionning and package to pypi