meteociel.soundings.sounding_arome

meteociel.soundings.sounding_arome(*, lon: float = None, lat: float = None, city_name: str = '', timestep: int = 1)

Extract the data of the upper air sounding simulated by the AROME model. You can pass coordinates or a city name. If you pass both, the city name will take priority.

Parameters

lonfloat, keyword-only, optionnal: By default: None. The longitude of the sounding.
latfloat, keyword-only, optionnal: By default: None. The latitude of the sounding.
city_namestr, keyword-only, optionnal: By default: "". The name of the city where the simulated sounding is to be extracted.
timestepint, keyword-only, optionnal: By default: 1. The number of hours elapsed since the start of the AROME run.

Returns

outtuple(str, pd.DataFrame)

A tuple that contains two elements:

the name of the city
A DataFrame that contains all the variables from the sounding.

Exemples

From city name:

>>> from meteociel.soundings import sounding_arome
>>> data = sounding_arome(city_name="Rennes")
>>> data[0]  # city name
'Rennes'
>>> data[1]  # the sounding data
    altitude  pressure  temperature  ...      wind_v
0       40.0    1018.0         14.0  ...  -28.123613
1       58.0    1015.0         13.9  ...  -43.175688
2       73.0    1013.0         13.8  ...  -60.051451
3       88.0    1012.0         13.7  ...  -70.619716
4      113.0    1009.0         13.5  ...  -87.114112
5      138.0    1006.0         13.3  ...  -99.465583
         ...       ...          ...  ...         ...
48   15026.0     125.0        -50.9  ...  -16.390010

From coordinates and with 12 hours of run:

>>> from meteociel.soundings import sounding_arome
>>> data = sounding_arome(lon=5, lat=42, timestep=12)
>>> data[0]
'42N-5E'
>>> data[1]
    altitude  pressure  temperature              wind_v
0        2.0    1016.0         17.5  ...  -5.353045e+00
1       20.0    1013.0         17.4  ...  -2.890644e+01
2       35.0    1011.0         17.2  ...  -2.917409e+01
3       50.0    1010.0         17.1  ...  -4.476484e+01
4       75.0    1007.0         16.9  ...  -6.075706e+01
5      100.0    1004.0         16.7  ...  -6.718071e+01
         ...       ...         ...   ...            ...
48   15029.0     125.0        -51.0  ...  -2.098393e+01