meteociel.forecasts.forecast

meteociel.forecasts.forecast(*, city_name: str = '', city_id: str = '', mode: str = 'forecasts', model: str = 'gfs')

Get the data of the forecasts or trends from the city name or id and given model. You can give the name or the id, but at least one of the both. If you give both, the id will have an higher priority.

Warning

Some cities across the world have the same name, so be careful when dumping data. To be sure, you can search by city id. Moreover, all the models aren’t available everywhere.

Available models

Model name	Coverage	Resolution
GFS	Global	25km
WRF	Western Europe [1]	5km
AROME	France	1km
ARPEGE	Europe	10km
ICON-EU	Europe	7km
ICON-D2	Central Europe	2.2km

[1]

Western Europe: France, UK, Germany, Spain and Italy.

Parameters

city_namestr, keyword-only, optionnal

By default: "". The name of the requested city.

city_idstr, keyword-only, optionnal

By default, this feature is disabled. By passing directly the city id, the API will search by id rather than by name. The city id can be manually found by accessing: https://www.meteociel.fr/prevville.php, then search for the city you want, you will have an url of the form: https://www.meteociel.fr/previsions/CityId/CityName.htm where CityId should be a number.

Important

Search by identifier takes precedence over search by name.

modestr, keyword_only, optionnal

By default: "forecasts". There is two available modes:

• "forecasts" that provides data for the three days ahead;

• "trends" that provides data for the ten days ahead but with lower confidence.

modelstr, keyword_only, optionnal

By default: "gfs". The model from which the data are obtained. The available models are:

• "gfs": an american global model (25km);

• "wrf", "wrf-1h": an american non-hydrostatic limited area model (5km) you can pass "wrf-1h" to have hourly data;

• "arome", "arome-1h": a french limited area model (1km), hourly data are also available;

• "arpege-1h": a french global area (10km), only hourly data available;

• "iconeu": a german limited-area model (7km);

• "icond2": a german limited-area model (2.2km).

Note

The model parameter is only available in "forecasts" mode, otherwise it will ignored.

Returns

outtuple(str, pd.DataFrame)

A tuple that contains two elements:

• the name of the city;

• the requested forecast data in a DataFrame.

Raises

UnknownModeError

This exception is raised if the given mode is unknown.

UnknownModelError

This exception is raised if the given model is unknown.

Exemples

Extraction of Paris (France) weather forecasts data with the hourly arome model:

>>> from meteociel.forecasts import forecast
>>> city_name, data = forecast(city_name="Paris (75000)", model="arome-1h")
>>> data
                  date  temperature  windchill  ...  rain  humidity  pressure
0  2024-06-23 03:00:00         15.0       15.0  ...     0      80.0    1019.0
1  2024-06-23 04:00:00         14.0       14.0  ...     0      79.0    1020.0
2  2024-06-23 05:00:00         14.0       14.0  ...     0      78.0    1020.0
3  2024-06-23 06:00:00         14.0       14.0  ...     0      78.0    1020.0
4  2024-06-23 07:00:00         14.0       14.0  ...     0      77.0    1020.0
5  2024-06-23 08:00:00         15.0       15.0  ...     0      73.0    1021.0
...                ...          ...        ...  ...   ...       ...       ...
41 2024-06-24 20:00:00         27.0       30.0  ...     0      41.0    1014.0

Extraction of weather trends by id (here for Berlin, Germany):

>>> from meteociel.forecasts import forecast
>>> city_name, data = forecast(city_id=49679, mode="trends")
>>> city_name
berlin
>>> data
                  date  temperature  windchill  ...  rain  humidity  pressure
0  2024-06-26 20:00:00         27.0       32.0  ...   0.0      53.0    1009.0
1  2024-06-27 02:00:00         20.0       26.0  ...   1.6      82.0    1010.0
2  2024-06-27 08:00:00         22.0       27.0  ...   0.0      72.0    1009.0
3  2024-06-27 14:00:00         32.0       36.0  ...   0.0      39.0    1008.0
4  2024-06-27 20:00:00         23.0       31.0  ...   1.7      82.0    1009.0
5  2024-06-28 02:00:00         21.0       28.0  ...   1.5      91.0    1008.0
...                ...          ...        ...  ...   ...       ...       ...
25 2024-07-03 02:00:00         14.0       12.0  ...   2.6      94.0    1007.0