pycif.plugins.models.wrfchem — API reference

Configuration reference: wrfchem plugin

pycif.plugins.models.wrfchem.ini_mapper.ini_mapper(model, transform_type, general_mapper={}, backup_comps={}, transforms_order=[], ref_transform='', transform_name='', **kwargs)

Build the data-flow mapper for the WRF-Chem model.

Defines the input/output streams for WRF-Chem:

• Inputs — anthropogenic emissions (flux), meteorological lateral boundary conditions (lbc), initial concentrations (inicond), WRF namelist (namelist), WRF executable (model).

• Outputs — simulated concentrations per active species.

Uses meteo_dates for the meteorological input date grid (which may differ from input_dates).

Parameters:

• model – WRF-Chem model plugin instance.

• transform_type (str) – unused; kept for API compatibility.

• general_mapper (dict) – unused.

• backup_comps (dict) – unused.

• transforms_order (list) – unused.

• ref_transform (str) – unused.

• transform_name (str) – unused.

• **kwargs – unused.

Returns:

mapper with inputs, outputs, and outputs2inputs.

Return type:

dict

pycif.plugins.models.wrfchem.ini_periods.ini_periods(self, **kwargs)

Compute temporal discretisation for the WRF-Chem model.

Splits the full simulation window into sub-simulation periods (using self.periods if set, otherwise a single window) and derives the output and input date arrays from the WRF namelist output interval.

Sets on self:

• subsimu_dates — period boundary dates.

• tstep_dates — per-period time-step arrays (at WRF output frequency).

• meteo_dates — per-period meteorological input date arrays.

• input_dates — per-period initial/end-concentration date arrays.

• tstep_all — sorted unique merge of all time steps.

• iniobs, reset_obs — per-period observation bookkeeping flags.

• chain — per-period flag indicating chained restarts.

Parameters:

• self – WRF-Chem model plugin instance with datei, datef, and domain (WRF namelist) set.

• **kwargs – unused.

pycif.plugins.models.wrfchem.perturb_model.perturb_model(self, nsamples, transf_mapper)

Extend the WRF-Chem chemistry scheme to accommodate ensemble members.

Creates nsamples copies of each active species using the __sample#NNN naming convention, then removes the original un-suffixed species.

Note

This function currently emits debug warnings (warn) for diagnostic purposes.

Parameters:

• self – WRF-Chem model plugin instance.

• nsamples (int) – number of ensemble members.

• transf_mapper (dict) – unused; kept for API consistency.

pycif.plugins.models.wrfchem.run.run(self, runsubdir, mode, workdir, ddi, do_simu=True, **kwargs)

Run WRF

Parameters:

• runsubdir (str) – working directory for the current run

• mode (str) – forward or backward

• workdir (str) – pycif working directory

• do_simu (bool) – if False, considers that the simulation was already run

VERSION HISTORY

2021-08-19 Original code from online doc and from

plugins/models/lmdz/run.py

From ctdas-wrf’s wrchem utilities.

class pycif.plugins.models.wrfchem.utilities.utilities

Bases: object

Utilities for wrfchem model

static check_out_err(process)

Displays stdout and stderr, returns returncode of the process. Only makes sense if stdout and stderr are directed to PIPE, else process.communicate doesn’t see them (I think). If verbose, only display errors if there are any.

Created on Mon Jul 22 15:03:02 2019

VERSION HISTORY 2021-09-25 freum Adapted from CTDAS-WRF wrfchem_helper.py - Everything not (yet) needed is commented

@author: friedemann

class pycif.plugins.models.wrfchem.wrfchem_helper.WRFChemHelper(settings)

Bases: object

Contains helper functions for sampling WRF-Chem

static read_namelist(nml_file)

Read run settings from namelist file

static get_int_coefs(pb_ret, pb_mod, level_def)

Computes a coefficients matrix to transfer a model profile onto a retrieval pressure axis.

If level_def==”layer_average”, this assumes that profiles are constant in each layer of the retrieval, bound by the pressure boundaries pb_ret. In this case, the WRF model layer is treated in the same way, and coefficients integrate over the assumed constant model layers. This works with non-staggered WRF variables (on “theta” points). However, this is actually not how WRF is defined, and the implementation should be changed to z-staggered variables. Details for this change are in a comment at the beginning of the code.

If level_def==”pressure_boundary” (IMPLEMENTATION IN PROGRESS), assumes that profiles, kernel and pwf are defined at pressure boundaries that don’t have a thickness (this is how OCO-2 data are defined, for example). In this case, the coefficients linearly interpolate adjacent model level points. This is incompatible with the treatment of WRF in the above-described layer-average assumption, but is closer to how WRF is actually defined. The exception is that pb_mod is still constructed and non-staggered variables are not defined at psurf. This can only be fixed by switching to z-staggered variables.

In cases where retrieval surface pressure is higher than model surface pressure, and in cases where retrieval top pressure is lower than model top pressure, the model profile will be extrapolated with constant tracer mixing ratios. In cases where retrieval surface pressure is lower than model surface pressure, and in cases where retrieval top pressure is higher than model top pressure, only the parts of the model column that fall within the retrieval presure boundaries are sampled.

Arguments

pb_ret (array_like)

Pressure boundaries of the retrieval column

pb_mod (array_like)

Pressure boundaries of the model column

level_def (string)

“layer_average” or “pressure_boundary” (IMPLEMENTATION IN PROGRESS). Refers to the retrieval profile.

Note 2021-09-13: Inspected code for pressure_boundary. Should be correct. Interpolates linearly between two model levels.

Returns

coefs (array_like)

Integration coefficient matrix. Each row sums to 1.

Usage

import numpy as np
pb_ret = np.linspace(900., 50., 5)
pb_mod = np.linspace(1013., 50., 7)
model_profile = 1. - np.linspace(0., 1., len(pb_mod)-1)**3
coefs = get_int_coefs(pb_ret, pb_mod, "layer_average")
retrieval_profile = np.matmul(coefs, model_profile)

static times_in_wrf_file(filename)

Returns the times in netcdf file as datetime object

wrf_times(file_list)

Read all times in a list of wrf files

Output

• 1D-array containing all times

• 1D-array containing start indices of each file