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SarahSource

Classes:

  • SarahSource –

    The SarahSource object manages weather data (as netCDF4 files) coming from the

SarahSource 

SarahSource(source, bounds=None, index_pad=5, **kwargs)

Bases: NCSource

The SarahSource object manages weather data (as netCDF4 files) coming from the SARAH satellite-reconstructed data products<https://climatedataguide.ucar.edu/climate-data/surface-solar-radiation-data-set-heliosat-sarah-edition-1>

If furthermore allows access a number of common functionalities and constants which are often encountered when simulating renewable energy technologies

Note:

Various constants can have been set for this weather source which can impact later simulation workflows.

For this source, these constants include: MAX_LON_DIFFERENCE = 0.06 The maximum longitude difference to accept between a grid cell's center and the coordinates to extract data for

MAX_LAT_DIFFERENCE = 0.06
    The maximum latitude difference to accept between a grid cell's center and the coordinates
        to extract data for
See Also

reskit.weather.MerraSource reskit.weather.SarahSource reskit.weather.Era5Source

Initialize a SARAH style netCDF4 file source

Compared to the generic NCSource object, the following parameters are automatically set: * tz = None * time_name = "time" * lat_name = "lat" * lon_name = "lon" * flip_lat = False * flip_lon = False * time_offset_minutes = 0

Parameters:

  • path

    (str or list of str) –

    The path to the main data file(s) to load

    If multiple files are given, or if a directory of netCDF4 files is given, then it is assumed that all files ending with the extension '.nc' or '.nc4' should be managed by this object. * Be sure that all the netCDF4 files given share the same time and spatial dimensions!

  • bounds

    (Anything acceptable to geokit.Extent.load(), default: None ) –

    The boundaries of the data which is needed * Usage of this will help with memory management * If None, the full dataset is loaded in memory * The actual extent of the loaded data depends on the source's available data

  • index_pad

    (int, default: 5 ) –

    The padding to apply to the boundaries * Useful in case of interpolation * Units are in longitudinal degrees

  • verbose

    (bool) –

    If True, then status outputs are printed when searching for and reading weather data

  • forward_fill

    (bool) –

    If True, then missing data in the weather file is forward-filled * Generally, there should be no missing data at all. This option is only intended to catch the rare scenarios where one or two timesteps are missing

See Also

MerraSource SarahSource Era5Source

Methods:

Source code in reskit/weather/SarahSource.py 
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def __init__(self, source, bounds=None, index_pad=5, **kwargs):
    """Initialize a SARAH style netCDF4 file source

    Compared to the generic NCSource object, the following parameters are automatically set:
        * tz = None
        * time_name = "time"
        * lat_name = "lat"
        * lon_name = "lon"
        * flip_lat = False
        * flip_lon = False
        * time_offset_minutes = 0


    Parameters
    ----------
    path : str or list of str
        The path to the main data file(s) to load

        If multiple files are given, or if a directory of netCDF4 files is given, then it is assumed
        that all files ending with the extension '.nc' or '.nc4' should be managed by this object.
        * Be sure that all the netCDF4 files given share the same time and spatial dimensions!

    bounds : Anything acceptable to geokit.Extent.load(), optional
        The boundaries of the data which is needed
          * Usage of this will help with memory management
          * If None, the full dataset is loaded in memory
          * The actual extent of the loaded data depends on the source's
            available data

    index_pad : int, optional
        The padding to apply to the boundaries
          * Useful in case of interpolation
          * Units are in longitudinal degrees

    verbose : bool, optional
        If True, then status outputs are printed when searching for and reading weather data

    forward_fill : bool, optional
        If True, then missing data in the weather file is forward-filled
        * Generally, there should be no missing data at all. This option is only intended to
            catch the rare scenarios where one or two timesteps are missing

    See Also
    --------
    MerraSource
    SarahSource
    Era5Source
    """
    super().__init__(
        source=source,
        bounds=bounds,
        time_name="time",
        lat_name="lat",
        lon_name="lon",
        index_pad=index_pad,
        _max_lon_diff=self.MAX_LON_DIFFERENCE,
        _max_lat_diff=self.MAX_LAT_DIFFERENCE,
        tz=None,
        **kwargs,
    )

sload_direct_normal_irradiance 

sload_direct_normal_irradiance()

Standard loader function for the variable 'direct_normal_irradiance'

Automatically reads the variable "DNI" from the given SARAH source and saves it as the variable 'direct_normal_irradiance' in the data library

Source code in reskit/weather/SarahSource.py 
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def sload_direct_normal_irradiance(self):
    """Standard loader function for the variable 'direct_normal_irradiance'

    Automatically reads the variable "DNI" from the given SARAH source and saves it as the
    variable 'direct_normal_irradiance' in the data library
    """
    self.load("DNI", name="direct_normal_irradiance")
    sel = np.logical_or(
        self.data["direct_normal_irradiance"] < 0,
        np.isnan(self.data["direct_normal_irradiance"]),
    )
    self.data["direct_normal_irradiance"][sel] = 0

sload_global_horizontal_irradiance 

sload_global_horizontal_irradiance()

Standard loader function for the variable 'global_horizontal_irradiance'

Automatically reads the variable "SIS" from the given SARAH source and saves it as the variable 'global_horizontal_irradiance' in the data library

Source code in reskit/weather/SarahSource.py 
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def sload_global_horizontal_irradiance(self):
    """Standard loader function for the variable 'global_horizontal_irradiance'

    Automatically reads the variable "SIS" from the given SARAH source and saves it as the
    variable 'global_horizontal_irradiance' in the data library
    """
    self.load("SIS", name="global_horizontal_irradiance")
    sel = np.logical_or(
        self.data["global_horizontal_irradiance"] < 0,
        np.isnan(self.data["global_horizontal_irradiance"]),
    )
    self.data["global_horizontal_irradiance"][sel] = 0