reskit.weather.SarahSource

Classes

SarahSource

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

Module Contents

class reskit.weather.SarahSource.SarahSource(source, bounds=None, index_pad=5, **kwargs)

Bases: reskit.weather.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, Compared, the

param path:

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!

type path:

str or list of str

param bounds:

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

type bounds:

Anything acceptable to geokit.Extent.load(), optional

param index_pad:

The padding to apply to the boundaries

• Useful in case of interpolation

• Units are in longitudinal degrees

type index_pad:

int, optional

param verbose:

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

type verbose:

bool, optional

param forward_fill:

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

type forward_fill:

bool, optional

See also

MerraSource, SarahSource, Era5Source

MAX_LON_DIFFERENCE = 0.06

MAX_LAT_DIFFERENCE = 0.06

loc_to_index

Returns the closest X and Y indexes corresponding to a given location or set of locations

Parameters:

• loc (Anything acceptable by geokit.LocationSet) –

  The location(s) to search for * A single tuple with (lon, lat) is acceptable, or a list of such tuples * A single point geometry (as long as it has an SRS), or a list

  of geometries is okay

  • geokit,Location, or geokit.LocationSet are best!

• outside_okay (bool, optional) – Determines if points which are outside the source’s lat/lon grid are allowed * If True, points outside this space will return as None * If False, an error is raised

Returns:

• If a single location is given (tuple) –

  • Format: (yIndex, xIndex)

  • y index can be accessed with ‘.yi’

  • x index can be accessed with ‘.xi’

• If multiple locations are given (list) –

  • Format: [ (yIndex1, xIndex1), (yIndex2, xIndex2), …]

  • Order matches the given order of locations

Note:

The default form of this function (which is the one used here) is not very efficient, ultimately

leading to much longer look-up than they otherwise need to be. When the weather source has grid cells on a regular lat/lon grid then a more efficient form of this function can be configured using the function generator “_loc_to_index_rect”. In these instances, this is the recommended function to use.

For example, if the weather source uses a latitude spacing of 0.5, and a longitude spacing of

0.625, then the function generator can be used like:

> source.loc_to_index = source._loc_to_index_rect(lat_step=0.5, lon_step=0.625)

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

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