spacepy.data_assimilation.ensemble

class spacepy.data_assimilation.ensemble(ensembles=50)

Ensemble-based data assimilation subroutines for the Radiation Belt Model

EnKF(A, Psi, Inn, HAp)	analysis subroutine after code example in
EnKF_oneobs(A, Psi, Inn, HAp)	analysis subroutine for a single observations
add_model_error(model, A, PSDdata)	this routine will add a standard error to the ensemble states
add_model_error_obs(model, A, Lobs, y)	this routine will add a standard error to the ensemble states
getHA(model, Lobs, A)	compute HA provided L vector of observations
getHAprime(HA)	calculate ensemble perturbation of HA
getHPH(Lobs, Pfxx)	compute HPH
getInnovation(y, Psi, HA)	compute innovation ensemble D’
getperturb(model, y)	compute perturbations of observational vector

EnKF(A, Psi, Inn, HAp)

analysis subroutine after code example in Evensen 2003 this will take the prepared matrices and calculate the analysis most efficiently, A will be returned

Parameters:

A : :

Psi : :

Inn : :

HAp : :

Returns:

out : :

EnKF_oneobs(A, Psi, Inn, HAp)

analysis subroutine for a single observations with the EnKF. This is a special case.

Parameters:

A : :

Psi : :

Inn : :

HAp : :

Returns:

out : :

add_model_error(model, A, PSDdata)

this routine will add a standard error to the ensemble states

Parameters:

model : :

A : :

PSDdata : :

Returns:

out : :

add_model_error_obs(model, A, Lobs, y)

this routine will add a standard error to the ensemble states

Parameters:

model : :

A : :

Lobs : :

y : :

Returns:

out : :

getHA(model, Lobs, A)

compute HA provided L vector of observations and ensemble matrix A

Parameters:

model : :

Lobs : :

A : :

Returns:

out : :

getHAprime(HA)

calculate ensemble perturbation of HA HA’ = HA-HA_mean

Parameters:HA : : Returns:out : :

getHPH(Lobs, Pfxx)

compute HPH

Parameters:

Lobs :

Pfxx :

Returns:

out :

getInnovation(y, Psi, HA)

compute innovation ensemble D’

Parameters:

y : :

Psi : :

HA : :

Returns:

out : :

getperturb(model, y)

compute perturbations of observational vector

Parameters:

model : :

y : :

Returns:

out : :