spacepy.data_assimilation.ensemble

class spacepy.data_assimilation.ensemble(ensembles=50)[source]

Ensemble-based data assimilation subroutines for the Radiation Belt Model

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

EnKF_oneobs(A, Psi, Inn, HAp)

analysis subroutine for a single observations with the EnKF.

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 and ensemble matrix A

getHAprime(HA)

calculate ensemble perturbation of HA HA' = HA-HA_mean

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)[source]

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)[source]

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)[source]

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)[source]

this routine will add a standard error to the ensemble states

Parameters
model
A
Lobs
y
Returns
out
getHA(model, Lobs, A)[source]

compute HA provided L vector of observations and ensemble matrix A

Parameters
model
Lobs
A
Returns
out
getHAprime(HA)[source]

calculate ensemble perturbation of HA HA’ = HA-HA_mean

Parameters
HA
Returns
out
getHPH(Lobs, Pfxx)[source]

compute HPH

Parameters
Lobs
Pfxx
Returns
out
getInnovation(y, Psi, HA)[source]

compute innovation ensemble D’

Parameters
y
Psi
HA
Returns
out
getperturb(model, y)[source]

compute perturbations of observational vector

Parameters
model
y
Returns
out