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	ObsVect	False	True	standard	std
controlvect	ControlVect	True	True	standard	std
obsoperator	ObsOperator	True	True	standard	std
minimizer	Minimizer	True	True	M1QN3	std
simulator	Simulator	True	True	gausscost	std
platform	Platform	True	True	None	None

Yaml template

Please find below a template for a Yaml configuration:

mode:
  plugin:
    name: 4dvar
    version: std
    type: mode


  # Optional arguments
  save_out_netcdf: XXXXX
  montecarlo: XXXXX