reskit.weather.MerraSource

reskit.weather.MerraSource#

Submodules#

Classes#

MerraSource

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

Package Contents#

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

Bases: reskit.weather.NCSource

The MerraSource object manages weather data (as netCDF4 files) coming from the MERRA2 climate data products<https://gmao.gsfc.nasa.gov/reanalysis/MERRA-2/>

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.

These constants include:
MAX_LON_DIFFERENCE = 0.625
The maximum longitude difference to accept between a grid cell’s center and the coordinates

to extract data for

MAX_LAT_DIFFERENCE = 0.5
The maximum latitude difference to accept between a grid cell’s center and the coordinates

to extract data for

WIND_SPEED_HEIGHT_FOR_WIND_ENERGY = 50

The suggested altitude of wind speed data to use for wind-energy simulations

WIND_SPEED_HEIGHT_FOR_SOLAR_ENERGY = 2

The suggested altitude of wind speed data to use for wind-energy simulations

LONG_RUN_AVERAGE_WINDSPEED :

<RESKit path>/weather/MerraSource/data/merra_average_windspeed_50m-shifted.tif

A path to a raster file with the long-time average wind speed in each grid cell * Can be used in wind energy simulations * Calculated at the height specified in WIND_SPEED_HEIGHT_FOR_WIND_ENERGY * Time range includes 1980 until the end of 2017 (the time of first calculation) * Only a few regions have been precomputed, therefore applications which require this

data will not work outside of these regions. They include: - Europe - Australia - Iceland - Parts of south america - Parts of north america

LONG_RUN_AVERAGE_GHI :

<RESKit path>/weather/MerraSource/data/merra_average_SWGDN_1994-2015_globe.tif

A path to a raster file with the long-time average global horizontal irradiance in

each grid cell

  • Can be used in solar energy simulations

  • Calculated at the surface

  • Time range includes 1994 until the end of 2015 (to match the global solar atlas)

  • Only a few regions have been precomputed, therefore applications which require this

    data will not work outside of these regions. They include: - Europe - Australia - Iceland - Parts of south america - Parts of north america

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

ELEVATED_WIND_SPEED_HEIGHT = 50#
SURFACE_WIND_SPEED_HEIGHT = 2#
LONG_RUN_AVERAGE_WINDSPEED#
LONG_RUN_AVERAGE_GHI#
MAX_LON_DIFFERENCE = 0.625#
MAX_LAT_DIFFERENCE = 0.5#
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)

context_area_at_index(latI, lonI)#

Compute the context area surrounding the specified index of the original source

_load_uv(height)#
_load_wind_speed(height)#
sload_elevated_wind_speed()#

Standard loader function for the variable ‘elevated_wind_speed’

Automatically reads the variables “U<X>M” and “V<X>M” from the given MERRA2 source, computes the total windspeed, and saves it as the variable ‘elevated_wind_speed’ in the data library

Where ‘<X>’ is the height specified by MerraSource.ELEVATED_WIND_SPEED_HEIGHT

sload_surface_wind_speed()#

Standard loader function for the variable ‘surface_wind_speed’

Automatically reads the variables “U<X>M” and “V<X>M” from the given MERRA2 source, computes the total windspeed, and saves it as the variable ‘surface_wind_speed’ in the data library

Where ‘<X>’ is the height specified by MerraSource.SURFACE_WIND_SPEED_HEIGHT

sload_wind_speed_at_2m()#

Standard loader function for the variable ‘wind_speed_at_2m’

Automatically reads the variables “U2M” and “V2M” from the given MERRA2 source, computes the total windspeed, and saves it as the variable ‘wind_speed_at_2m’ in the data library

sload_wind_speed_at_10m()#

Standard loader function for the variable ‘wind_speed_at_10m’

Automatically reads the variables “U10M” and “V10M” from the given MERRA2 source, computes the total windspeed, and saves it as the variable ‘wind_speed_at_10m’ in the data library

sload_wind_speed_at_50m()#

Standard loader function for the variable ‘wind_speed_at_50m’

Automatically reads the variables “U50M” and “V50M” from the given MERRA2 source, computes the total windspeed, and saves it as the variable ‘wind_speed_at_50m’ in the data library

_load_wind_dir(height)#
sload_elevated_wind_direction()#

Standard loader function for the variable ‘elevated_wind_direction’

Automatically reads the variables “U<X>M” and “V<X>M” from the given MERRA2 source, computes the total windspeed, and saves it as the variable ‘elevated_wind_direction’ in the data library

Where ‘<X>’ is the height specified by MerraSource.ELEVATED_WIND_SPEED_HEIGHT

sload_surface_wind_direction()#

Standard loader function for the variable ‘surface_wind_direction’

Automatically reads the variables “U<X>M” and “V<X>M” from the given MERRA2 source, computes the total windspeed, and saves it as the variable ‘surface_wind_direction’ in the data library

Where ‘<X>’ is the height specified by MerraSource.SURFACE_WIND_SPEED_HEIGHT

sload_wind_direction_at_2m()#

Standard loader function for the variable ‘wind_direction_at_2m’

Automatically reads the variables “U2M” and “V2M” from the given MERRA2 source, computes the total windspeed, and saves it as the variable ‘wind_direction_at_2m’ in the data library

sload_wind_direction_at_10m()#

Standard loader function for the variable ‘wind_direction_at_10m’

Automatically reads the variables “U10M” and “V10M” from the given MERRA2 source, computes the total windspeed, and saves it as the variable ‘wind_direction_at_10m’ in the data library

sload_wind_direction_at_50m()#

Standard loader function for the variable ‘wind_direction_at_50m’

Automatically reads the variables “U50M” and “V50M” from the given MERRA2 source, computes the total windspeed, and saves it as the variable ‘wind_direction_at_50m’ in the data library

sload_surface_pressure()#

Standard loader function for the variable ‘surface_pressure’

Automatically reads the variable “PS” from the given MERRA2 source and saves it as the variable ‘surface_pressure’ in the data library

sload_surface_air_temperature()#

Standard loader function for the variable ‘surface_air_temperature’

Automatically reads the variable “T2M” from the given MERRA2 source and saves it as the variable ‘surface_air_temperature’ in the data library

Temperature values are also converted from kelvin to degrees celsius

sload_surface_dew_temperature()#

Standard loader function for the variable ‘surface_dew_temperature’

Automatically reads the variable “T2MDEW” from the given MERRA2 source and saves it as the variable ‘surface_dew_temperature’ in the data library

Temperature values are also converted from kelvin to degrees celsius

sload_global_horizontal_irradiance()#

Standard loader function for the variable ‘global_horizontal_irradiance’

Automatically reads the variable “SWGDN” from the given MERRA2 source and saves it as the variable ‘global_horizontal_irradiance’ in the data library