Analytical inversions (analytic / std)

Description

Computes an analytical inversion based on a given observation operator, control vector and observation vector.

It first computes explicitly the linear observation operator \(\mathbf{H}\) by running so-called base functions or response functions. To do so, it iterates on every dimension \(i\) of the control vector and computes \(\mathcal{H}(\mathbf{x}_i)\) with \(\mathbf{x}_i\) a control vector whose values are null, with the exception of the element \(i\) set to 1.

Base functions are computed as individual pyCIF forward simulations and are stored in $workdir/base_functions/

Warning

As one forward simulation per dimension of the control vector is needed for this mode, please first check the dimension of your control vector and the time required for each forward run. You can check this by using the option dryrun (see below)

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

dump_nc_base_control: (optional): False

Dumps elementary control vectors used to build the base functions as NetCDF format; this helps diagnosing whether base functions are computed based on e.g., fluxes consistent with the user expectations

accepted type: bool

dryrun: (optional): False

Runs a dry analytical inversion to estimate the time needed to compute the full observation operator

accepted type: bool

resp_func_only: (optional): False

Does not run the inversion, only the response functions to build the H matrix

accepted type: 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
platform	Platform	True	True	None	None

Yaml template

Please find below a template for a Yaml configuration:

mode:
  plugin:
    name: analytic
    version: std
    type: mode

  # Optional arguments
  dump_nc_base_control: XXXXX  # bool
  dryrun: XXXXX  # bool
  resp_func_only: XXXXX  # bool