Skip to main content

Interpolates global chemistry fields for use with WRF-CHEM

Project description

Interpolator for WRF-CHEM

Interpolator for WRF-CHEM is a preprocessing application for using global chemistry model fields with WRF-CHEM. It interpolates the global chemistry model fields to the WRF-CHEM grid and vertical levels. Think of it like WPS but for chemistry fields.

Some features:

  • Interpolates 3D fields
  • Can do species/size bin mapping through linear combinations of source fields
  • Potentially support many global models (currently supports CAMS EAC4)

The application is written in Python and is meant to be used a command-line tool. Some potential limitations:

  • The application currently assumes that fields are mixing ratios.
  • The global model fields should be on a regular lat-lon grid.

Installation

The application is available on PyPI and can be installed using pip:

pip install interpolator-for-wrfchem

Usage

The workflow for using the interpolator is as follows:

  1. Use WPS and real.exe as usual to generate the met_em, wrfinput, and wrfbdy files.
  2. Download global chemistry model fields (e.g. CAMS EAC4) for the same time period as the WRF simulation.
  3. Run the interpolator to interpolate the global chemistry model fields to the WRF-CHEM grid and vertical levels.
  4. Run WRF-CHEM.

The interpolator will update wrfinput and wrfbdy files to include the chemistry information.

Workflow

The interpolator is a command-line tool and can be run as follows:

interpolator-for-wrfchem <global model name> <global model data path> <met_em path> <species map path> <wrfinput path>

The wrfinput and wrfbdy files WILL BE MODIFIED! The global model name can be one of the following:

  • cams_eac4: CAMS EAC4 data (w/ 60 vertical levels)
  • `cams_eac4_adsbeta``: CAMS EAC4 data from the ADS-Beta
  • cams_global_forecasts: CAMS global forecasts (w/ 137 vertical levels)

There are some optional flags:

  • --wrfbdy=: Path to the wrfbdy_d01 file, if not provided, the boundary is not updated.
  • --copy-icbc: Make a backup of the wrfinput and wrfbdy files before updating them.
  • --no-ic: Do not update the wrfinput. You must nonetheless provide the path to the wrfinput file as it is required to read some information.
  • --diagnostics: Store some diagnostic information in the diag_cams_interp.nc file.

When you use nested domains, you can run the application multiple times, each time pointing to a different wrfinput file. You can omit the wrfbdy file when running the application for the nested domains' wrfinput files. If you need to update wrfbdy files for the future without touching wrfinput (e.g. for a cycling run), point to a correct wrfinput file (correct means it's the same model grid and configuration) and use the --no-ic flag.

Species mapping

In many cases, the available fields of the global model do not directly correspond to the ones used by the chemistry/dust scheme you want to use in WRF-CHEM. For example, you might have dust concentrations available in different size bins. The application supports "species mapping", through which the WRF-CHEM fields are created through a linear combination of global model fields, after interpolation.

Detailed description of the species file format is available in species_maps.md.

Use in other projects

To automate use of the interpolator by using it as a library in your project, it is recommended to use the do_initial_conditions() and do_boundary_conditions() functions from the interpolator_for_wrfchem module. Provided with the correct arguments, these functions will mirror the command-line behavior of the interpolator. Exact details of how to set up all the magic objects required by these functions are available inside the main CLI entrypoint (file interpolator_for_wrfchem/__init__.py, main() function).

An example usage would be:

# Contains information on how to map from global model species to WRF-CHEM species
mapping = SpeciesMap(mapping_path)

# Represents a wrfinput file. The original netCDF file can be accessed as a
# `netcdf4.Dataset` in the `WRFInput.nc_file` attribute.
wrf = WRFInput(wrfinput_path)

# You can get the main coordinates in dataset form by calling wrf.get_dataset()
wrf_ds = wrf.get_dataset()

# Represents a set of global model files. Available choices in `global_models/__init__.py`.
global_model = CAMS_ADSBeta_Base(input_files, mapping.required_source_species)
global_model_ds = global_model.get_dataset(wrf.time)

# Do initial condition interpolation and MODIFY wrfinput_d01
do_initial_conditions(wrf, wrf_ds, global_model_ds, mapping, diagnostics)
wrf.close()

# Represents a folder of met_em files. Required only for the boundary conditions.
met_em = MetEm(met_em_path, wrf_ds)
# Do boundary condition interpolation and MODIFY wrfbdy_d01
do_boundary_conditions(wrfbdy, met_em, wrf_ds, global_model_ds, mapping)

The interpolation routines are available inside the interpolation.py file and are applied to xarray Datasets, so they might be useful in other projects as well.

License

The interpolator is licensed under the MIT License. See LICENSE for more information. Please cite the project if you use it for your research!

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

interpolator_for_wrfchem-1.3.1.tar.gz (22.8 kB view details)

Uploaded Source

Built Distribution

interpolator_for_wrfchem-1.3.1-py3-none-any.whl (26.8 kB view details)

Uploaded Python 3

File details

Details for the file interpolator_for_wrfchem-1.3.1.tar.gz.

File metadata

File hashes

Hashes for interpolator_for_wrfchem-1.3.1.tar.gz
Algorithm Hash digest
SHA256 5e07ffc5647d790e5da7488023fc12d0ffa7b87a167f7b05beaa2772e6238f65
MD5 703a072ebc29bb011a3d51da267559fe
BLAKE2b-256 ef013c9981dc7dabde1d419024e6edab5997d9b47192dde7821a90112d0769ce

See more details on using hashes here.

File details

Details for the file interpolator_for_wrfchem-1.3.1-py3-none-any.whl.

File metadata

File hashes

Hashes for interpolator_for_wrfchem-1.3.1-py3-none-any.whl
Algorithm Hash digest
SHA256 bdab8ba7a69284aade6295303ab0cdd3faa2ed39765710218291a1c060733045
MD5 f9058db73c0caa24a2e5be4425dd49ce
BLAKE2b-256 a9590aaab65c295a323c635dd2e5a92b401c1e67f5fe85817ded7709cc88e46e

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page