emc2.core (emc2.core)¶
The procedures in this module contain the core data structures of EMC^2. In particular, the Instrument class that describes the characteristics of the instrument to be simulated is stored here. In addition, global constants used by EMC^2 are also stored in this module.
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This stores the information for the KAZR (Ka-band radar). |
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This stores the information for 532 nm lidars ,e.g., the High Spectral Resolution Lidar (HSRL), micropulse lidar (MPL). |
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This stores the information for the ARM CSAPR. |
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This stores the information for the NOAA NEXRAD radar Based on https://www.roc.noaa.gov/WSR88D/Engineering/NEXRADTechInfo.aspx. |
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This stores the information for the AMF XSACR. |
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This stores the information for the 1064 nm lidars, e.g., the 2-ch HSRL. |
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This loads a ModelE simulation with all of the necessary parameters for EMC^2 to run. |
This is a test Model structure used only for unit testing. |
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This loads a DHARMA simulation with all of the necessary parameters for EMC^2 to run. |
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This load a WRF simulation and all of the necessary parameters from the simulation. |
This is a test Model structure used only for unit testing. |
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This is a test Model structure used only for unit testing. |
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This is a test Model structure used only for unit testing. |
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This is a test Model structure used only for unit testing. |
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This is the base class which holds the information needed to contain the instrument parameters for the simulator. |
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This class stores the model specific parameters for the radar simulator. |
In addition the emc2.core.quantity() is equivalent to pint’s
UnitRegistry().Quantity object. This allows for the use of quantities
with units, making EMC^2 unit aware.
- class emc2.core.Model[source]¶
This class stores the model specific parameters for the radar simulator.
- Attributes:
- Rho_hyd: dict
A dictionary whose keys are the names of the model’s hydrometeor classes and whose values are the density of said hydrometeors in \(kg\ m^{-3}\)
- fluffy: dict
A dictionary whose keys are the names of the model’s ice hydrometeor classes and whose values are the ice fluffiness factor for the fwd calculations using r_e, where values of 0 - equal volume sphere, 1 - fluffy sphere i.e., diameter = maximum dimension.
- lidar_ratio: dict
A dictionary whose keys are the names of the model’s hydrometeor classes and whose values are the lidar_ratio of said hydrometeors.
- vel_param_a: dict
A dictionary whose keys are the names of the model’s hydrometeor classes and whose values are the \(a\) parameters to the equation \(V = aD^b\) used to calculate terminal velocity corresponding to each hydrometeor.
- vel_param_b: dict
A dictionary whose keys are the names of the model’s hydrometeor classes and whose values are the \(b\) parameters to the equation \(V = aD^b\) used to calculate terminal velocity corresponding to each hydrometeor.
- N_field: dict
A dictionary whose keys are the names of the model’s hydrometeor classes and whose values are the number concentrations in \(cm^{-3}\) corresponding to each hydrometeor class.
- T_field: str
A string containing the name of the temperature field in the model.
- q_field: str
A string containing the name of the water vapor mixing ratio field (in kg/kg) in the model.
- p_field: str
A string containing the name of the pressure field (in mbar) in the model.
- z_field: str
A string containing the name of the height field (in m) in the model.
- conv_frac_names: dict
A dictionary containing the names of the convective fraction corresponding to each hydrometeor class in the model.
- strat_frac_names: dict
A dictionary containing the names of the stratiform fraction corresponding to each hydrometeor class in the model.
- conv_frac_names_for_rad: dict
A dictionary containing the names of the convective fraction corresponding to each hydrometeor class in the model for the radiation scheme.
- strat_frac_names_for_rad: dict
A dictionary containing the names of the stratiform fraction corresponding to each hydrometeor class in the model for the radiation scheme.
- conv_re_fields: dict
A dictionary containing the names of the effective radii of each convective hydrometeor class
- strat_re_fields: dict
A dictionary containing the names of the effective radii of each stratiform hydrometeor class
- asp_ratio_func: dict
A dictionary that returns hydrometeor aspect ratios as a function of maximum dimension in mm.
- hyd_types: list
list of hydrometeor classes to include in calcuations. By default set to be consistent with the model represented by the Model subclass.
- time_dim: str
The name of the time dimension in the model.
- height_dim: str
The name of the height dimension in the model.
- lat_dim: str
Name of the latitude dimension in the model (relevant for regional output)
- lon_dim: str
Name of the longitude dimension in the model (relevant for regional output)
- mcphys_scheme: str
- Name of the microphysics scheme used by the model. Current options are:
“MG2”, “MG”, “Morrison” - essentially treated the same.
“NSSL”
“P3”
- rad_scheme_family: str
Radiation scheme family. Many models share the same radiation scheme or bulk scattering LUT ancestor (inc. same PSD parameters, ice habit description, etc.). This parameters determines the single particle or bulk LUT to use given the Model class.
- stacked_time_dim: str or None
This attribute becomes a string of the original time dimension name only if stacking was required to enable EMC2 to processes a domain output (time x lat x lon).
- process_conv: bool
If True, then processing convective model output (can typically be set to False for some models).
- model_name: str
The name of the model.
- variable_density: dict
If the model allows for particle density for vary (e.g. 2-moment NSSL), then this is a dict pointing to the variable with the density for each hydrometeor class
- check_and_stack_time_lat_lon(out_coord_name='time_lat_lon', file_path=None, order_dim=True)[source]¶
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. Otherwise, squeezing the lat and lon dims (if they exist in dataset). Finally, the method reorder dimensions to time x height for proper processing by calling the “permute_dims_for_processing” class method. NOTE: tmp variables for lat, lon, and time are produced as xr.Datasets still have many unresolved bugs associated with pandas multi-indexing implemented in xarray for stacking (e.g., new GitHub issue #5692). Practically, after the subcolumn processing the stacking information is lost so an alternative dedicated method is used for unstacking
- Parameters:
- out_coord_name: str
Name of output stacked coordinate.
- file_path: str
Path and filename of model simulation output.
- order_dim: bool
When True, reorder dimensions to time x height for proper processing.
- finalize_subcol_fields(more_fieldnames=[])[source]¶
Remove all zero values from subcolumn output fields enabling better visualization. Can be applied over additional fields using the more_fieldnames input parameter.
- property hydrometeor_classes¶
The list of hydrometeor classes.
- load_subcolumns_from_netcdf(file_name)[source]¶
Load all of the subcolumn data from a previously saved netCDF file. The dataset being loaded must match the current number of subcolumns if there are any generated.
- Parameters:
- file_name: str
Name of the file to save.
- property num_hydrometeor_classes¶
The number of hydrometeor classes used
- property num_subcolumns¶
Gets the number of subcolumns in the model. Will return 0 if the number of subcolumns has not yet been set.
- permute_dims_for_processing(base_order=None, base_dim_first=True)[source]¶
Reorder dims for consistent processing such that the order is: subcolumn x time x height. Note: lat/lon dims are assumed to already be stacked with the time dim.
- Parameters:
- base_order: list or None
List of preffered dimension order. Use default if None
- base_dim_first: bool
Make the base dims (height and time) the first ones in the permutation if True.
- remove_appended_str(all_appended_in_lat=False)[source]¶
Remove appended strings from xr.Dataset coords and fieldnames based on lat/lon coord names (typically required when using post-processed output data files). Note: in the case of E3SM-like machinery output, the method can handle output from multiple sub-domains assuming there is only a single spatial dimension (typically ncol).
- Parameters:
- all_appended_in_lat: bool
If True using only the appended str portion to the lat_dim. Otherwise, combining the appended str from both the lat and lon dims (relevant if appended_str is True).
- remove_subcol_fields(cloud_class='conv')[source]¶
Remove all subcolumn output fields for the given cloud class to save memory (mainly releveant for CESM and E3SM).
- subcolumns_to_netcdf(file_name)[source]¶
Saves all of the simulated subcolumn parameters to a netCDF file.
- Parameters:
- file_name: str
The name of the file to save to.
- unstack_time_lat_lon(order_dim=True, squeeze_single_dims=True)[source]¶
Unstack the time, lat, and lon dims if they were previously stacked together (self.stacked_time_dim is not None). Finally, the method reorder dimensions to time x height for proper processing by calling the “permute_dims_for_processing” class method. Note (relevant for squeeze_single_dims == True): If the time dimension size is 1, that dimension is squeezed. Similarly, if the number of subcolumns is 1 (subcolumn generator is turned off), the subcolumn dimension is squeezed as well. NOTE: This is a dedicated method written because xr.Datasets still have many unresolved bugs associated with pandas multi-indexing implemented in xarray for stacking (e.g., new GitHub issue #5692). Practically, after the subcolumn processing the stacking information is lost so this is an alternative dedicated method.
- Parameters:
- order_dim: bool
When True, reorder dimensions to subcolumn x time x height for proper processing.
- squeeze_single_dims: bool
If True, squeezing the time and/or subcolumn dimension(s) if their lengh is 1.
- class emc2.core.Instrument(frequency=None, wavelength=None)[source]¶
This is the base class which holds the information needed to contain the instrument parameters for the simulator.
- Attributes:
- instrument_str: str
The name of the instrument.
- instrument_class: str
The class of the instrument. Currently must be one of ‘radar,’ or ‘lidar’.
- freq: float
The frequency of the instrument.
- wavelength: float
The wavelength of the instrument
- beta_p_phase_thresh: list of dicts or None
If a list, each index contains a dictionaly with class name, class integer value (mask order), LDR value bounds, and the corresponding beta_p threshold (thresholds are linearly interpolated between LDR values). In order for the method to operate properly, the list should be arranged from the lowest to highest beta_p threshold values for a given LDR, that is, beta_p[i+1 | LDR=x] >= beta_p[i | LDR=x].
- ext_OD: float
The optical depth where we have full extinction of the lidar signal.
- OD_from_sfc: Bool
If True (default), optical depth will be calculated from the surface. If False, optical depth will be calculated from the top of the atmosphere.
- eta: float
Multiple scattering coefficient.
- K_w: float
The index of refraction of water used for Ze calculation. See the ARM KAZR handbook (Widener et al. 2012)
- eps_liq: float
The complex dielectric constant for liquid water.
- pt: float
Transmitting power in Watts.
- theta: float
3 dB beam width in degrees
- gain: float
The antenna gain in linear units.
- Z_min_1km: float
The minimum detectable signal at 1 km in dBZ
- lr: float
Attenuation based on the the general attributes in the spectra files.
- pr_noise_ge: float
Minimum detectable signal in mW.
- tau_ge: float
Pulse width in mus.
- tau_md: float
Pulse width in mus.