reskit.weather.IconlamSource ============================ .. py:module:: reskit.weather.IconlamSource Submodules ---------- .. toctree:: :maxdepth: 1 /_readthedocs/reskit/weather/IconlamSource/IconlamSource/index Classes ------- .. autoapisummary:: reskit.weather.IconlamSource.IconlamSource Package Contents ---------------- .. py:class:: IconlamSource(source, bounds=None, index_pad=5, **kwargs) Bases: :py:obj:`reskit.weather.NCSource` SChen: The IconlamSource from the output of ICON-LAM model minics the ERA5soure. Note: ----- Various constants can have been set for this weather source which can impact later simulation workflows. These constants include: MAX_LON_DIFFERENCE = 0.034 # SChen this is for 0.033 ICON-LAM grid The maximum longitude difference to accept between a grid cell's center and the coordinates to extract data for MAX_LAT_DIFFERENCE = 0.034 # SChen this is for 0.033 ICON-LAM grid 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 = 100 The suggested altitude of wind speed data to use for wind-energy simulations WIND_SPEED_HEIGHT_FOR_SOLAR_ENERGY = 10 The suggested altitude of wind speed data to use for wind-energy simulations LONG_RUN_AVERAGE_WINDSPEED : /weather/Era5Source/data/ERA5_wind_speed_100m_mean.tiff 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 2019 (the time of first calculation) * The averaging is performed globally LONG_RUN_AVERAGE_WINDDIR : /weather/Era5Source/data/ERA5_wind_direction_100m_mean.tiff A path to a raster file with the long-time average wind direction 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 2019 (the time of first calculation) * The averaging is performed globally LONG_RUN_AVERAGE_GHI : /weather/Era5Source/data/ERA5_surface_solar_radiation_downwards_mean.tiff 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 1980 until the end of 2019 (the time of first calculation) * The averaging is performed globally LONG_RUN_AVERAGE_DNI : /weather/Era5Source/data/ERA5_total_sky_direct_solar_radiation_at_surface_mean.tiff A path to a raster file with the long-time average direct horizontal irradiance in each grid cell * Can be used in solar energy simulations * Calculated at the surface and on a horizontal plane (not DNI!) * Time range includes 1980 until the end of 2019 (the time of first calculation) * The averaging is performed globally .. seealso:: :obj:`reskit.weather.MerraSource`, :obj:`reskit.weather.SarahSource`, :obj:`reskit.weather.Era5Source`, :obj:`Initialize`, :obj:`Compared`, :obj:`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 .. seealso:: :obj:`MerraSource`, :obj:`SarahSource`, :obj:`Era5Source` .. py:attribute:: ELEVATED_WIND_SPEED_HEIGHT :value: 100 .. py:attribute:: SURFACE_WIND_SPEED_HEIGHT :value: 10 .. py:attribute:: MAX_LON_DIFFERENCE :value: 0.034 .. py:attribute:: MAX_LAT_DIFFERENCE :value: 0.034 .. py:attribute:: loc_to_index Returns the closest X and Y indexes corresponding to a given location or set of locations :param loc: 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! :type loc: Anything acceptable by geokit.LocationSet :param outside_okay: 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 :type outside_okay: bool, optional :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) .. py:method:: sload_boundary_layer_height() Standard loader function for the variable 'boundary_layer_height' in meters from the surface .. py:method:: sload_elevated_wind_speed() Standard loader function for the variable 'elevated_wind_speed' Automatically reads the variables "ws" from the given ERA5 source and saves it as the variable 'elevated_wind_speed' in the data library Where '' is the height specified by `Era5Source.ELEVATED_WIND_SPEED_HEIGHT` The "ws" variable also needs to be precomputed from the raw variables "u" and "v" TODO: Update function to also be able to handle raw ERA5 inputs for u & v .. py:method:: sload_surface_wind_speed() Standard loader function for the variable 'surface_wind_speed' Automatically reads the variables "ws" from the given ERA5 source and saves it as the variable 'surface_wind_speed' in the data library Where '' is the height specified by `Era5Source.SURFACE_WIND_SPEED_HEIGHT` The "ws" variable also needs to be precomputed from the raw variables "u" and "v" TODO: Update function to also be able to handle raw ERA5 inputs for u & v .. py:method:: sload_wind_speed_at_100m() Standard loader function for the variable 'wind_speed_at_100m' Automatically reads the variables "ws100" from the given ERA5 source and saves it as the variable 'wind_speed_at_100m' in the data library The "ws100" variable also needs to be precomputed from the raw variables "u100" and "v100" TODO: Update function to also be able to handle raw ERA5 inputs for u & v .. py:method:: sload_wind_speed_at_10m() Standard loader function for the variable 'wind_speed_at_10m' Automatically reads the variables "ws10" from the given ERA5 source and saves it as the variable 'wind_speed_at_10m' in the data library The "ws10" variable also needs to be precomputed from the raw variables "u10" and "v10" TODO: Update function to also be able to handle raw ERA5 inputs for u & v .. py:method:: sload_elevated_wind_direction() Standard loader function for the variable 'elevated_wind_direction' Automatically reads the variables "wd" from the given ERA5 source and saves it as the variable 'elevated_wind_direction' in the data library Where '' is the height specified by `Era5Source.ELEVATED_WIND_SPEED_HEIGHT` The "wd" variable also needs to be precomputed from the raw variables "u" and "v" and made available in the raw dataset TODO: Update function to also be able to handle raw ERA5 inputs for u & v .. py:method:: sload_surface_pressure() Standard loader function for the variable 'surface_pressure' Automatically reads the variable "sp" from the given ERA5 source and saves it as the variable 'surface_pressure' in the data library .. py:method:: sload_surface_air_temperature() Standard loader function for the variable 'surface_air_temperature' Automatically reads the variable "t2m" from the given ERA5 source and saves it as the variable 'surface_air_temperature' in the data library Temperature values are also converted from kelvin to degrees celsius .. py:method:: sload_surface_dew_temperature() Standard loader function for the variable 'surface_dew_temperature' Automatically reads the variable "d2m" from the given ERA5 source and saves it as the variable 'surface_dew_temperature' in the data library Temperature values are also converted from kelvin to degrees celsius .. py:method:: sload_direct_horizontal_irradiance() Standard loader function for the variable 'direct_horizontal_irradiance' Automatically reads the variable "fdir" from the given ERA5 source and saves it as the variable 'direct_horizontal_irradiance' in the data library .. py:method:: sload_global_horizontal_irradiance() Standard loader function for the variable 'global_horizontal_irradiance' Automatically reads the variable "ssrd" from the given ERA5 source and saves it as the variable 'global_horizontal_irradiance' in the data library