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 OM 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+.

print(mgr.get_data())

Installation (pypi)

working on it

Installation (manually)

git clone https://github.com/m0rp43us/openmeteopy
cd openmeteopy-main/
pip3 install .

APIs

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 : (I know it kindda sucks with integers as inputs,will change it soon)

You can get your output in json,dataframe or save it as csv or excel file as follows : get_data(output = 0,file = 0,filepath = None):

Ooutput	get_data Method values
json (server response)	get_data()
json (keys are dates,value are correspongding values)	get_data(1)
json (keys are dates,value are correspongding values) saved into a json file	get_data(1,1)
csv (keys are dates,value are correspongding values) saved into a csv file	get_data(1,1,'path')
excel (keys are dates,value are correspongding values) saved into a excel file	get_data(1,2,'path')
dataframe in excel (keys are dates,value are correspongding values) saved into a excel file	get_data(1,3,'path')

Upcoming Changes,updates,things to do :

• Add the other 2 variables to ECWMF pressure level parameters
• Change get_data() method to a more developper friendly method
• Add Documentation for dataframit() and jsonify()
• remove pressure level variables from JMA (Remove inheritence from FranceMeteo)
• Add Support of Date Types (input and output)
• Add versionning and package to pypi
• Clean and add corresponding comments
• Maybe Refactor OWmanager Class and add inheritence for a more Dev friendly Methods
• Readthedocs