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 |
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 : : |
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Returns: | out : : |
analysis subroutine for a single observations with the EnKF. This is a special case.
Parameters: | A : : Psi : : Inn : : HAp : : |
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Returns: | out : : |
this routine will add a standard error to the ensemble states
Parameters: | model : : A : : PSDdata : : |
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Returns: | out : : |
this routine will add a standard error to the ensemble states
Parameters: | model : : A : : Lobs : : y : : |
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Returns: | out : : |
compute HA provided L vector of observations and ensemble matrix A
Parameters: | model : : Lobs : : A : : |
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Returns: | out : : |
calculate ensemble perturbation of HA HA’ = HA-HA_mean
Parameters: | HA : : |
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Returns: | out : : |