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: int

Optional arguments

reload_results: (optional): False

Reload results from previous simulations

accepted type: bool

sequential: (optional): False

Compute control vector updates sequentially for each observation.

accepted type: bool

batch_sampling: (optional): False

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

accepted type: bool

moving_window: (optional)

Information about the moving windows.

accepted structure:

fwd_step: (mandatory)

Length of each step forward between segments

accepted type: str

step_per_segment: (mandatory)

Number of forward step in each assimilation segment

accepted type: 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:

 1mode:
 2  plugin:
 3    name: EnSRF
 4    version: std
 5    type: mode
 6
 7  # Mandatory arguments
 8  nsample: XXXXX  # int
 9
10  # Optional arguments
11  reload_results: XXXXX  # bool
12  sequential: XXXXX  # bool
13  batch_sampling: XXXXX  # bool
14  moving_window:
15    fwd_step: XXXXX  # str
16    step_per_segment: XXXXX  # int