Ensemble Square-Root Filter (EnSRF / std)

Description

Compute an Ensemble Square Root Filter (EnSRF) based on CTDAS implementation (Peters et al., 2005).

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:

Mandatory arguments

nsample: (mandatory)

Number of random samples in the ensemble

accepted type: <class ‘int’>

Optional arguments

reload_results: (optional): False

Reload results from previous simulations

accepted type: <class ‘bool’>

sequential: (optional): False

Compute control vector updates sequentially for each observation.

accepted type: <class ‘bool’>

batch_sampling: (optional): False

Compute samples into a single observation operator or as separate jobs if False

accepted type: <class ‘bool’>

moving_window: (optional)

Information about the moving windows.

accepted structure:

fwd_step: (mandatory)

Length of each step forward between segments

accepted type: <class ‘str’>

step_per_segment: (mandatory)

Number of forward step in each assimilation segment

accepted type: <class ‘int’>

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: EnSRF
    version: std
    type: mode

  # Mandatory arguments
  nsample: XXXXX

  # Optional arguments
  reload_results: XXXXX
  sequential: XXXXX
  batch_sampling: XXXXX
  moving_window: XXXXX