4DVAR variational inversions (4dvar
/ std
)¶
Description¶
The variational mode computes the minimum of a function (called simulator
) provided a minimizer
.
It can be applied to the minimization of any function, but in practice,
it is mostly used to compute the minimum of the Bayesian Gaussian cost function.
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¶
save_out_netcdf: (optional): False
Save final posterior vector as NetCDF. This argument overwrites the corresponding argument in the
controlvect
.accepted type: <class ‘bool’>
montecarlo: (optional)
Number of perturbed inversions to compute posterior inversions.
- accepted structure:
nsample: (mandatory)
Number of members in the Monte-Carlo. The control run with no perturbation will be computed as the last member of the ensemble.
accepted type: True
perturb_x: (optional): True
Perturb the control vector or not
accepted type: <class ‘bool’>
perturb_y: (optional): True
Perturb the observation vector or not
accepted type: <class ‘bool’>
Requirements¶
The current plugin requires the present plugins to run properly:
Requirement name |
Requirement type |
Explicit definition |
Any valid |
Default name |
Default version |
---|---|---|---|---|---|
obsvect |
False |
True |
standard |
std |
|
controlvect |
True |
True |
standard |
std |
|
obsoperator |
True |
True |
standard |
std |
|
minimizer |
True |
True |
M1QN3 |
std |
|
simulator |
True |
True |
gausscost |
std |
|
platform |
True |
True |
None |
None |
Yaml template¶
Please find below a template for a Yaml configuration:
1mode:
2 plugin:
3 name: 4dvar
4 version: std
5 type: mode
6
7
8 # Optional arguments
9 save_out_netcdf: XXXXX
10 montecarlo: XXXXX