Example - Open-meteo Historical Weather API.txt

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Historical Weather API
Discover how weather has shaped our world from 1940 until now

Now, with the addition of the 9-kilometer ECMWF IFS model, the historical weather API provides access to a staggering 90 terabytes of meteorological data! Read the blog article.
Location and Time
Location:
 Coordinates
 List
52,52
Latitude
13,41
Longitude

Not set (GMT+0)
Timezone
2024-09-25
Start Date
2024-10-09
End Date
You can access past weather data dating back to 1940. However, there is a 5-day delay in the data. If you want information for the most recent days, you can use the forecast API and adjust the Past Days setting.

Quick:     

Hourly Weather Variables
Temperature (2 m)
Relative Humidity (2 m)
Dewpoint (2 m)
Apparent Temperature
Precipitation (rain + snow)
Rain
Snowfall
Snow depth
Weather code
Sealevel Pressure
Surface Pressure
Cloud cover Total
Cloud cover Low
Cloud cover Mid
Cloud cover High
Reference Evapotranspiration (ET₀)
Vapour Pressure Deficit
Wind Speed (10 m)
Wind Speed (100 m)
Wind Direction (10 m)
Wind Direction (100 m)
Wind Gusts (10 m)
Soil Temperature (0-7 cm)
Soil Temperature (7-28 cm)
Soil Temperature (28-100 cm)
Soil Temperature (100-255 cm)
Soil Moisture (0-7 cm)
Soil Moisture (7-28 cm)
Soil Moisture (28-100 cm)
Soil Moisture (100-255 cm)
Additional Variables And Options
Solar Radiation Variables
ERA5-Ensemble Spread Variables
Reanalysis models
Daily Weather Variables
Weather code
Maximum Temperature (2 m)
Minimum Temperature (2 m)
Mean Temperature (2 m)
Maximum Apparent Temperature (2 m)
Minimum Apparent Temperature (2 m)
Mean Apparent Temperature (2 m)
Sunrise
Sunset
Daylight Duration
Sunshine Duration
Precipitation Sum
Rain Sum
Snowfall Sum
Precipitation Hours
Maximum Wind Speed (10 m)
Maximum Wind Gusts (10 m)
Dominant Wind Direction (10 m)
Shortwave Radiation Sum
Reference Evapotranspiration (ET₀)
Settings

Celsius °C
Temperature Unit

Km/h
Wind Speed Unit

Millimeter
Precipitation Unit

ISO 8601 (e.g. 2022-12-31)
Timeformat
Usage License:
Non-Commercial
Commercial
Self-Hosted
Only for non-commercial use and less than 10.000 daily API calls. See Terms for more details.
API Response
Preview:
Chart And URL
Python
Typescript
Swift
Other
The sample code automatically applies all the parameters selected above. It includes caching and the conversion to Pandas DataFrames. The use of DataFrames is entirely optional. You can find further details and examples in the Python API client documentation.

Install
pip install openmeteo-requests
pip install requests-cache retry-requests numpy pandas
Usage
import openmeteo_requests

import requests_cache
import pandas as pd
from retry_requests import retry

# Setup the Open-Meteo API client with cache and retry on error
cache_session = requests_cache.CachedSession('.cache', expire_after = -1)
retry_session = retry(cache_session, retries = 5, backoff_factor = 0.2)
openmeteo = openmeteo_requests.Client(session = retry_session)

# Make sure all required weather variables are listed here
# The order of variables in hourly or daily is important to assign them correctly below
url = "https://archive-api.open-meteo.com/v1/archive"
params = {
	"latitude": 52.52,
	"longitude": 13.41,
	"start_date": "2024-09-25",
	"end_date": "2024-10-09",
	"hourly": ["temperature_2m", "precipitation", "rain", "snowfall", "snow_depth", "wind_speed_10m", "wind_speed_100m", "wind_direction_10m", "wind_direction_100m", "wind_gusts_10m"]
}
responses = openmeteo.weather_api(url, params=params)

# Process first location. Add a for-loop for multiple locations or weather models
response = responses[0]
print(f"Coordinates {response.Latitude()}°N {response.Longitude()}°E")
print(f"Elevation {response.Elevation()} m asl")
print(f"Timezone {response.Timezone()} {response.TimezoneAbbreviation()}")
print(f"Timezone difference to GMT+0 {response.UtcOffsetSeconds()} s")

# Process hourly data. The order of variables needs to be the same as requested.
hourly = response.Hourly()
hourly_temperature_2m = hourly.Variables(0).ValuesAsNumpy()
hourly_precipitation = hourly.Variables(1).ValuesAsNumpy()
hourly_rain = hourly.Variables(2).ValuesAsNumpy()
hourly_snowfall = hourly.Variables(3).ValuesAsNumpy()
hourly_snow_depth = hourly.Variables(4).ValuesAsNumpy()
hourly_wind_speed_10m = hourly.Variables(5).ValuesAsNumpy()
hourly_wind_speed_100m = hourly.Variables(6).ValuesAsNumpy()
hourly_wind_direction_10m = hourly.Variables(7).ValuesAsNumpy()
hourly_wind_direction_100m = hourly.Variables(8).ValuesAsNumpy()
hourly_wind_gusts_10m = hourly.Variables(9).ValuesAsNumpy()

hourly_data = {"date": pd.date_range(
	start = pd.to_datetime(hourly.Time(), unit = "s", utc = True),
	end = pd.to_datetime(hourly.TimeEnd(), unit = "s", utc = True),
	freq = pd.Timedelta(seconds = hourly.Interval()),
	inclusive = "left"
)}
hourly_data["temperature_2m"] = hourly_temperature_2m
hourly_data["precipitation"] = hourly_precipitation
hourly_data["rain"] = hourly_rain
hourly_data["snowfall"] = hourly_snowfall
hourly_data["snow_depth"] = hourly_snow_depth
hourly_data["wind_speed_10m"] = hourly_wind_speed_10m
hourly_data["wind_speed_100m"] = hourly_wind_speed_100m
hourly_data["wind_direction_10m"] = hourly_wind_direction_10m
hourly_data["wind_direction_100m"] = hourly_wind_direction_100m
hourly_data["wind_gusts_10m"] = hourly_wind_gusts_10m

hourly_dataframe = pd.DataFrame(data = hourly_data)
print(hourly_dataframe)


Data Sources
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 models for historical weather data use a spatial resolution of 9 km to resolve fine details close to coasts or complex mountain terrain. In general, a higher spatial resolution means that the data is more detailed and represents the weather conditions more accurately at smaller scales.

The ECMWF IFS dataset has been meticulously assembled by Open-Meteo using simulation runs at 0z and 12z, employing the most up-to-date version of IFS. This dataset offers the utmost resolution and precision in depicting historical weather conditions.

However, when studying climate change over decades, it is advisable to exclusively utilize ERA5 or ERA5-Land. This choice ensures data consistency and prevents unintentional alterations that could arise from the adoption of different weather model upgrades.

You can access data dating back to 1940 with a delay of 2 days. If you're looking for weather information from the previous day, our Forecast API offers the &past_days= feature for your convenience.

You can find the update timings in the model updates documentation.
Data Set	Region	Spatial Resolution	Temporal Resolution	Data Availability	Update frequency
ECMWF IFS	Global	9 km	Hourly	2017 to present	Daily with 2 days delay
ERA5	Global	0.25° (~25 km)	Hourly	1940 to present	Daily with 5 days delay
ERA5-Land	Global	0.1° (~11 km)	Hourly	1950 to present	Daily with 5 days delay
ERA5-Ensemble	Global	0.5° (~55 km)	3-Hourly	1940 to present	Daily with 5 days delay
CERRA	Europe	5 km	Hourly	1985 to June 2021	-
ECMWF IFS Assimilation Long-Window	Global	9 km	6-Hourly	2024 to present	Daily with 2 days delay
Different reanalysis models may include different sets of weather variables in their datasets. For example, the ERA5 model includes all weather variables, while the ERA5-Land model only includes surface variables such as temperature, humidity, soil temperature, and soil moisture. The CERRA model includes most weather variables, but does not include soil temperature and moisture. It is important to be aware of the specific variables that are included in a particular reanalysis model in order to understand the limitations and potential biases of the data.

API Documentation
The API endpoint /v1/archive allows users to retrieve historical weather data for a specific location and time period. To use this endpoint, you can specify a geographical coordinate, a time interval, and a list of weather variables that they are interested in. The endpoint will then return the requested data in a format that can be easily accessed and used by applications or other software. This endpoint can be very useful for researchers and other users who need to access detailed historical weather data for specific locations and time periods.

All URL parameters are listed below:

Parameter	Format	Required	Default	Description
latitude
longitude	Floating point	Yes		Geographical WGS84 coordinates of the location. Multiple coordinates can be comma separated. E.g. &latitude=52.52,48.85&longitude=13.41,2.35. To return data for multiple locations the JSON output changes to a list of structures. CSV and XLSX formats add a column location_id.
elevation	Floating point	No		The elevation used for statistical downscaling. Per default, a 90 meter digital elevation model is used. You can manually set the elevation to correctly match mountain peaks. If &elevation=nan is specified, downscaling will be disabled and the API uses the average grid-cell height. For multiple locations, elevation can also be comma separated.
start_date
end_date	String (yyyy-mm-dd)	Yes		The time interval to get weather data. A day must be specified as an ISO8601 date (e.g. 2022-12-31).
hourly	String array	No		A list of weather variables which should be returned. Values can be comma separated, or multiple &hourly= parameter in the URL can be used.
daily	String array	No		A list of daily weather variable aggregations which should be returned. Values can be comma separated, or multiple &daily= parameter in the URL can be used. If daily weather variables are specified, parameter timezone is required.
temperature_unit	String	No	celsius	If fahrenheit is set, all temperature values are converted to Fahrenheit.
wind_speed_unit	String	No	kmh	Other wind speed speed units: ms, mph and kn
precipitation_unit	String	No	mm	Other precipitation amount units: inch
timeformat	String	No	iso8601	If format unixtime is selected, all time values are returned in UNIX epoch time in seconds. Please note that all time is then in GMT+0! For daily values with unix timestamp, please apply utc_offset_seconds again to get the correct date.
timezone	String	No	GMT	If timezone is set, all timestamps are returned as local-time and data is returned starting at 00:00 local-time. Any time zone name from the time zone database is supported If auto is set as a time zone, the coordinates will be automatically resolved to the local time zone. For multiple coordinates, a comma separated list of timezones can be specified.
cell_selection	String	No	land	Set a preference how grid-cells are selected. The default land finds a suitable grid-cell on land with similar elevation to the requested coordinates using a 90-meter digital elevation model. sea prefers grid-cells on sea. nearest selects the nearest possible grid-cell.
apikey	String	No		Only required to commercial use to access reserved API resources for customers. The server URL requires the prefix customer-. See pricing for more information.
Additional optional URL parameters will be added. For API stability, no required parameters will be added in the future!

Hourly Parameter Definition
The parameter &hourly= accepts the following values. Most weather variables are given as an instantaneous value for the indicated hour. Some variables like precipitation are calculated from the preceding hour as and average or sum.

Variable	Valid time	Unit	Description
temperature_2m	Instant	°C (°F)	Air temperature at 2 meters above ground
relative_humidity_2m	Instant	%	Relative humidity at 2 meters above ground
dew_point_2m	Instant	°C (°F)	Dew point temperature at 2 meters above ground
apparent_temperature	Instant	°C (°F)	Apparent temperature is the perceived feels-like temperature combining wind chill factor, relative humidity and solar radiation
pressure_msl
surface_pressure	Instant	hPa	Atmospheric air pressure reduced to mean sea level (msl) or pressure at surface. Typically pressure on mean sea level is used in meteorology. Surface pressure gets lower with increasing elevation.
precipitation	Preceding hour sum	mm (inch)	Total precipitation (rain, showers, snow) sum of the preceding hour. Data is stored with a 0.1 mm precision. If precipitation data is summed up to monthly sums, there might be small inconsistencies with the total precipitation amount.
rain	Preceding hour sum	mm (inch)	Only liquid precipitation of the preceding hour including local showers and rain from large scale systems.
snowfall	Preceding hour sum	cm (inch)	Snowfall amount of the preceding hour in centimeters. For the water equivalent in millimeter, divide by 7. E.g. 7 cm snow = 10 mm precipitation water equivalent
cloud_cover	Instant	%	Total cloud cover as an area fraction
cloud_cover_low	Instant	%	Low level clouds and fog up to 2 km altitude
cloud_cover_mid	Instant	%	Mid level clouds from 2 to 6 km altitude
cloud_cover_high	Instant	%	High level clouds from 6 km altitude
shortwave_radiation	Preceding hour mean	W/m²	Shortwave solar radiation as average of the preceding hour. This is equal to the total global horizontal irradiation
direct_radiation
direct_normal_irradiance	Preceding hour mean	W/m²	Direct solar radiation as average of the preceding hour on the horizontal plane and the normal plane (perpendicular to the sun)
diffuse_radiation	Preceding hour mean	W/m²	Diffuse solar radiation as average of the preceding hour
global_tilted_irradiance	Preceding hour mean	W/m²	Total radiation received on a tilted pane as average of the preceding hour. The calculation is assuming a fixed albedo of 20% and in isotropic sky. Please specify tilt and azimuth parameter. Tilt ranges from 0° to 90° and is typically around 45°. Azimuth should be close to 0° (0° south, -90° east, 90° west). If azimuth is set to "nan", the calculation assumes a horizontal tracker. If tilt is set to "nan", it is assumed that the panel has a vertical tracker. If both are set to "nan", a bi-axial tracker is assumed.
sunshine_duration	Preceding hour sum	Seconds	Number of seconds of sunshine of the preceding hour per hour calculated by direct normalized irradiance exceeding 120 W/m², following the WMO definition.
wind_speed_10m
wind_speed_100m	Instant	km/h (mph, m/s, knots)	Wind speed at 10 or 100 meters above ground. Wind speed on 10 meters is the standard level.
wind_direction_10m
wind_direction_100m	Instant	°	Wind direction at 10 or 100 meters above ground
wind_gusts_10m	Instant	km/h (mph, m/s, knots)	Gusts at 10 meters above ground of the indicated hour. Wind gusts in CERRA are defined as the maximum wind gusts of the preceding hour. Please consult the ECMWF IFS documentation for more information on how wind gusts are parameterized in weather models.
et0_fao_evapotranspiration	Preceding hour sum	mm (inch)	ET₀ Reference Evapotranspiration of a well watered grass field. Based on FAO-56 Penman-Monteith equations ET₀ is calculated from temperature, wind speed, humidity and solar radiation. Unlimited soil water is assumed. ET₀ is commonly used to estimate the required irrigation for plants.
weather_code	Instant	WMO code	Weather condition as a numeric code. Follow WMO weather interpretation codes. See table below for details. Weather code is calculated from cloud cover analysis, precipitation and snowfall. As barely no information about atmospheric stability is available, estimation about thunderstorms is not possible.
snow_depth	Instant	meters	Snow depth on the ground. Snow depth in ERA5-Land tends to be overestimated. As the spatial resolution for snow depth is limited, please use it with care.
vapour_pressure_deficit	Instant	kPa	Vapor Pressure Deificit (VPD) in kilopascal (kPa). For high VPD (>1.6), water transpiration of plants increases. For low VPD (<0.4), transpiration decreases
soil_temperature_0_to_7cm
soil_temperature_7_to_28cm
soil_temperature_28_to_100cm
soil_temperature_100_to_255cm	Instant	°C (°F)	Average temperature of different soil levels below ground.
soil_moisture_0_to_7cm
soil_moisture_7_to_28cm
soil_moisture_28_to_100cm
soil_moisture_100_to_255cm	Instant	m³/m³	Average soil water content as volumetric mixing ratio at 0-7, 7-28, 28-100 and 100-255 cm depths.
Daily Parameter Definition
Aggregations are a simple 24 hour aggregation from hourly values. The parameter &daily= accepts the following values:

Variable	Unit	Description
weather_code	WMO code	The most severe weather condition on a given day
temperature_2m_max
temperature_2m_min	°C (°F)	Maximum and minimum daily air temperature at 2 meters above ground
apparent_temperature_max
apparent_temperature_min	°C (°F)	Maximum and minimum daily apparent temperature
precipitation_sum	mm	Sum of daily precipitation (including rain, showers and snowfall)
rain_sum	mm	Sum of daily rain
snowfall_sum	cm	Sum of daily snowfall
precipitation_hours	hours	The number of hours with rain
sunrise
sunset	iso8601	Sun rise and set times
sunshine_duration	seconds	The number of seconds of sunshine per day is determined by calculating direct normalized irradiance exceeding 120 W/m², following the WMO definition. Sunshine duration will consistently be less than daylight duration due to dawn and dusk.
daylight_duration	seconds	Number of seconds of daylight per day
wind_speed_10m_max
wind_gusts_10m_max	km/h (mph, m/s, knots)	Maximum wind speed and gusts on a day
wind_direction_10m_dominant	°	Dominant wind direction
shortwave_radiation_sum	MJ/m²	The sum of solar radiaion on a given day in Megajoules
et0_fao_evapotranspiration	mm	Daily sum of ET₀ Reference Evapotranspiration of a well watered grass field
JSON Return Object
On success a JSON object will be returned.

      

  "latitude": 52.52,
  "longitude": 13.419,
  "generationtime_ms": 2.2119,
  "timezone": "Europe/Berlin",
  "timezone_abbreviation": "CEST",
  "hourly": {
    "time": ["2022-07-01T00:00", "2022-07-01T01:00", "2022-07-01T02:00", ...],
    "temperature_2m": [13, 12.7, 12.7, 12.5, 12.5, 12.8, 13, 12.9, 13.3, ...]
  },
  "hourly_units": {
    "temperature_2m": "°C"
  },

      
    
Parameter	Format	Description
latitude, longitude	Floating point	WGS84 of the center of the weather grid-cell which was used to generate this forecast. This coordinate might be a few kilometers away from the requested coordinate.
elevation	Floating point	The elevation from a 90 meter digital elevation model. This effects which grid-cell is selected (see parameter cell_selection). Statistical downscaling is used to adapt weather conditions for this elevation. This elevation can also be controlled with the query parameter elevation. If &elevation=nan is specified, all downscaling is disabled and the averge grid-cell elevation is used.
generationtime_ms	Floating point	Generation time of the weather forecast in milliseconds. This is mainly used for performance monitoring and improvements.
utc_offset_seconds	Integer	Applied timezone offset from the &timezone= parameter.
timezone
timezone_abbreviation	String	Timezone identifier (e.g. Europe/Berlin) and abbreviation (e.g. CEST)
hourly	Object	For each selected weather variable, data will be returned as a floating point array. Additionally a time array will be returned with ISO8601 timestamps.
hourly_units	Object	For each selected weather variable, the unit will be listed here.
daily	Object	For each selected daily weather variable, data will be returned as a floating point array. Additionally a time array will be returned with ISO8601 timestamps.
daily_units	Object	For each selected daily weather variable, the unit will be listed here.
Errors
In case an error occurs, for example a URL parameter is not correctly specified, a JSON error object is returned with a HTTP 400 status code.



  "error": true,
  "reason": "Cannot initialize WeatherVariable from invalid String value tempeture_2m for key hourly"

      
Citation & Acknowledgement
We encourage researchers in the field of meteorology and related disciplines to cite Open-Meteo and its sources in their work. Citing not only gives proper credit but also promotes transparency, reproducibility, and collaboration within the scientific community. Together, let's foster a culture of recognition and support for open-data initiatives like Open-Meteo, ensuring that future researchers can benefit from the valuable resources it provides.

APA
MLA
Harvard
BibTeX
Zippenfenig, P. (2023). Open-Meteo.com Weather API [Computer software]. Zenodo. https://doi.org/10.5281/ZENODO.7970649

Hersbach, H., Bell, B., Berrisford, P., Biavati, G., Horányi, A., Muñoz Sabater, J., Nicolas, J., Peubey, C., Radu, R., Rozum, I., Schepers, D., Simmons, A., Soci, C., Dee, D., Thépaut, J-N. (2023). ERA5 hourly data on single levels from 1940 to present [Data set]. ECMWF. https://doi.org/10.24381/cds.adbb2d47

Muñoz Sabater, J. (2019). ERA5-Land hourly data from 2001 to present [Data set]. ECMWF. https://doi.org/10.24381/CDS.E2161BAC

Schimanke S., Ridal M., Le Moigne P., Berggren L., Undén P., Randriamampianina R., Andrea U., Bazile E., Bertelsen A., Brousseau P., Dahlgren P., Edvinsson L., El Said A., Glinton M., Hopsch S., Isaksson L., Mladek R., Olsson E., Verrelle A., Wang Z.Q. (2021). CERRA sub-daily regional reanalysis data for Europe on single levels from 1984 to present [Data set]. ECMWF. https://doi.org/10.24381/CDS.622A565A

Generated using Copernicus Climate Change Service information 2022.