TM5-4DVAR/rodenbeck TM5-4DVAR/rodenbeck

TM5-4DVAR/rodenbeck TM5-4DVAR/rodenbeck#

Description#

Reads background/boundary-condition concentration data produced by the TM5-4DVAR system (Rodenbeck version).

Data are stored as one NetCDF file per calendar year, holding per-station simulated concentration time series. Station identity, network and sampling height (above ground level) are decoded from an encoded station-ID variable, and the simulated concentrations are extracted and scaled (numscale) for use as CIF background data.

write produces prescribed-species background fields for LMDZ, either as a raw binary file or as a NETCDF3_CLASSIC file (same implementation as carboscope_bg).

YAML arguments#

The following arguments are used to configure the plugin. pyCIF will return an exception at the initialization if mandatory arguments are not specified, or if any argument does not fit accepted values or type:

Optional arguments#

dir : str, optional, default “”

Path to the corresponding component. This value is used if not provided in parameters

file : str, optional, default “”

File format in the given directory. This value is used if not provided in parameters

varname : str, optional, default “”

Variable name to use to read data filesinstead of the parameter name if different to the parameter name

file_freq : str, optional, default “”

Temporal frequency to fetch files

split_freq : str, optional

Re-bucket this tracer’s fetched dates/files onto a fixed pandas-frequency grid (e.g. '1D', '1MS', '1YS'), independently of the native frequency the files were found at (file_freq). This fixes the sub-simulation boundaries that fromcontrol inherits for this tracer. For minimal memory usage, match it to the consuming model’s own periodicity: a finer value costs an extra time_interpolation bridge for no memory benefit, while a coarser value keeps the tracer’s data resident in memory across every one of the model’s sub-simulations it spans. See the ‘Splitting the temporal grid’ section on the observation operator page for the full mechanism and worked examples.

time_varname : str, optional, default “time1”

Name of the time variable

id_varname : str, optional, default “identification”

Name of the station ID variable

numscale : float, optional, default 1

Scaling factor to apply to the input data

YAML template#

Please find below a template for a YAML configuration:

 1background:
 2  plugin:
 3    name: TM5-4DVAR
 4    version: rodenbeck
 5    type: background
 6
 7  # Optional arguments
 8  dir: XXXXX  # str
 9  file: XXXXX  # str
10  varname: XXXXX  # str
11  file_freq: XXXXX  # str
12  split_freq: XXXXX  # str
13  time_varname: XXXXX  # str
14  id_varname: XXXXX  # str
15  numscale: XXXXX  # float

See also

API reference (functions & code)