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