emc2.core.model.WRF¶

class emc2.core.model.WRF(file_path, z_range=None, time_range=None, mcphys_scheme='nssl', NUWRF=False, bounding_box=None)[source]¶

This load a WRF simulation and all of the necessary parameters from the simulation.

Parameters:

file_path: str: Path to WRF simulation.
time_range: tuple or None: Start and end time to include. If this is None, the entire simulation will be included.
z_range: numpy array or None: The z levels of the vertical grid you want to use. By default, the levels are 0 m to 15000 m, increasing by 500 m.
mcphys_scheme: str: 3ice: Goddard 3ICE scheme morrison: Morrison microphysics
NUWRF: bool: If true, model is NASA Unified WRF.
bounding_box: None or 4-tuple: If not none, then a tuple representing the bounding box (lat_min, lon_min, lat_max, lon_max).

__init__(file_path, z_range=None, time_range=None, mcphys_scheme='nssl', NUWRF=False, bounding_box=None)[source]¶

This load a WRF simulation and all of the necessary parameters from the simulation.

Parameters:

file_path: str: Path to WRF simulation.
time_range: tuple or None: Start and end time to include. If this is None, the entire simulation will be included.
z_range: numpy array or None: The z levels of the vertical grid you want to use. By default, the levels are 0 m to 15000 m, increasing by 500 m.
mcphys_scheme: str: 3ice: Goddard 3ICE scheme morrison: Morrison microphysics
NUWRF: bool: If true, model is NASA Unified WRF.
bounding_box: None or 4-tuple: If not none, then a tuple representing the bounding box (lat_min, lon_min, lat_max, lon_max).

Methods

`__init__`(file_path[, z_range, time_range, ...])	This load a WRF simulation and all of the necessary parameters from the simulation.
`check_and_stack_time_lat_lon`([...])	Stack the time dim together with the lat and lon dims (if the lat and/or lon dims are longer than 1) to enable EMC^2 processing of regional model output.
`finalize_subcol_fields`([more_fieldnames])	Remove all zero values from subcolumn output fields enabling better visualization.
`load_subcolumns_from_netcdf`(file_name)	Load all of the subcolumn data from a previously saved netCDF file.
`permute_dims_for_processing`([base_order, ...])	Reorder dims for consistent processing such that the order is: subcolumn x time x height.
`remove_appended_str`([all_appended_in_lat])	Remove appended strings from xr.Dataset coords and fieldnames based on lat/lon coord names (typically required when using post-processed output data files).
`remove_subcol_fields`([cloud_class])	Remove all subcolumn output fields for the given cloud class to save memory (mainly releveant for CESM and E3SM).
`set_hyd_types`(hyd_types)
`subcolumns_to_netcdf`(file_name)	Saves all of the simulated subcolumn parameters to a netCDF file.
`unstack_time_lat_lon`([order_dim, ...])	Unstack the time, lat, and lon dims if they were previously stacked together (self.stacked_time_dim is not None).

Attributes

`hydrometeor_classes`	The list of hydrometeor classes.
`num_hydrometeor_classes`	The number of hydrometeor classes used
`num_subcolumns`	Gets the number of subcolumns in the model.