meteoserver.weatherforecast module

Functions to obtain, read and write 2 (HARMONIE) or 4-10 (GFS) day hourly weather-forecast (“Uurverwachting”) data from Meteoserver.nl.

meteoserver.weatherforecast.extract_hourly_forecast_dataframes_from_dict(dataDict, numeric)[source]

Extract the location and forecast-data Pandas dataframe from a data dictionary.

Parameters:

  • dataDict (dict) – The data dictionary to convert.

  • numeric (bool) – Convert dataframe content from strings to numeric/datetime format (default=True). Set this to False if you intend to write a JSON file that is (nearly) identical to the original format.

Returns:

Tuple containing (location, data):

  • location (str): Location the data are for.

  • data (df): Pandas dataframe containing forecast data for the specified location (or region).

Return type:

tuple (str, df)

meteoserver.weatherforecast.read_json_file_weatherforecast(fileJSON, full=False, loc=False, numeric=True)[source]

Read a Meteoserver weather-forecast-data JSON file from disc and return the data as a dataframe.

This uses the “Uurverwachting” Meteoserver data.

Parameters:

  • fileJSNO (string) – The name of the JSON file to read.

  • full (bool) – Return the full dataframe (currently 31 columns). If false, obsolescent and duplicate (in non-SI units) columns are removed (currently, 22 columns are returned). Default: False.

  • loc (bool) – Return the location name as a second return value (default=False).

  • numeric (bool) – Convert dataframe content from strings to numeric/datetime format (default=True). Set this to False if you intend to write a JSON file that is (nearly) identical to the original format.

Returns:

Tuple containing (data, location):

  • data (df): Pandas dataframe containing forecast data for the specified location (or region).

  • location (str): The name of the location the data are for (only returned if loc=True) - in this case, the two return values are returned as a tuple).

Return type:

tuple (df, str)

meteoserver.weatherforecast.read_json_url_weatherforecast(key, location, model='GFS', full=False, loc=False, numeric=True)[source]

Get hourly weather-forecast data from the Meteoserver server and return them as a dataframe.

This uses the “Uurverwachting” Meteoserver API/data.

Parameters:

  • key (string) – The Meteoserver API key.

  • location (string) – The name of the location (in the Netherlands) to obtain data for (e.g. ‘De Bilt’).

  • model (string) – Weather model to use: ‘HARMONIE’ or ‘GFS’ (default: GFS)

    • HARMONIE: use high-resolution HARMONIE model for BeNeLux and HiRLAM for the rest of Europe. Hourly predictions up to 48 hours in advance. New data available at 5:30, 11:30, 17:30 and 23:30 CE(S)T.

    • GFS: use GFS model for BeNeLux. Hourly predictions for 4 days, then three-hourly predictions for the next 10 days. New data are available at 0:30, 7:30, 12:30 and 18:30 CE(S)T.

  • full (bool) – Return the full dataframe (currently 31 columns). If false, obsolescent and duplicate (in non-SI units) columns are removed (currently, 22 columns are returned). Default: False.

  • loc (bool) – Return the location name as a second return value (default=False).

  • numeric (bool) – Convert dataframe content from strings to numeric/datetime format (default=True). Set this to False if you intend to write a JSON file that is (nearly) identical to the original format.

Returns:

Tuple containing (data, retLoc):

  • data (df): Pandas dataframe containing forecast data for the specified location (or region).

  • retLoc (str): The name of the location the data are for (only returned if loc=True - in this case, the two return values are returned as a tuple).

Return type:

tuple (df, str)

meteoserver.weatherforecast.remove_unused_hourly_forecast_columns(dataFrame)[source]

Remove the (probably) unused columns from a weather-forecast dataframe.

This removes the following columns (if they exist):

  • obsolescent ‘loc’ column.

  • wind speed/force ‘windb’ (Beaufort), ‘windknp’ (knots) and ‘windkmh’ (km/h) columns, which can be computed from SI ‘winds’ (m/s) column.

  • wind gust columns: ‘gustb’ (Beaufort), ‘gustkt’ (knots) and ‘gustkmh’, which can be computed from SI ‘gust’ column (m/s).

  • air-pressure columns: ‘luchtdmmhg’ and ‘luchtdinhg’, which can be computed from SI luchtd (hPa/mbar).

The number of columns is reduced from 27 to 21 for HARMONIE data, and from 31 to 22 for GFS data.

Parameters:

dataFrame (df) – Original Pandas dataframe.

Returns:

Pruned Pandas dataframe.

Return type:

dataFrame (df)

meteoserver.weatherforecast.write_json_file_weatherforecast(fileName, location, data)[source]

Write a Meteoserver weather-forecast-data JSON file to disc.

The resulting file has the same format as a downloaded file (barring some spacing).

Parameters:

  • fileName (string) – The name of the JSON file to write.

  • location (string) – The location the data are for.

  • data (df) – Pandas dataframe containing forecast data for the specified location (or region).