pyPDAF.PDAF.omi_prodRinvA_hyb_l_cb

pyPDAF.PDAF.omi_prodRinvA_hyb_l_cb()

This function is an internal PDAF-OMI function that is used as a call-back function to perform the matrix multiplication inverse of local observation error covariance and a matrix A in LKNETF. This could be used to modify the observation variance when OMI is used with pyPDAF.PDAF.assimilate_xxx instead of pyPDAF.PDAF.omi_assimilate_xxx.

Parameters:

  • domain_p (int) – Index of current local analysis domain

  • step (int) – Current time step

  • dim_obs_l (int) – Dimension of local observation vector

  • rank (int) – Rank of initial covariance matrix

  • obs_l (ndarray[tuple[dim_obs_l], np.float64]) –

    Local vector of observations

    The array dimension dim_obs_l is Dimension of local observation vector.

  • alpha (float) – Hybrid weight

  • A_l (ndarray[tuple[dim_obs_l, rank], np.float64]) –

    Input matrix

    The1st-th dimension dim_obs_l is Dimension of local observation vector; the2nd-th dimension rank is Rank of initial covariance matrix.

  • C_l (ndarray[tuple[dim_obs_l, rank], np.float64]) –

    Output matrix

    The1st-th dimension dim_obs_l is Dimension of local observation vector; the2nd-th dimension rank is Rank of initial covariance matrix.

Returns:

  • A_l (ndarray[tuple[dim_obs_l, rank], np.float64]) – Input matrix

    The 1st-th dimension dim_obs_l is Dimension of local observation vector The 2nd-th dimension rank is Rank of initial covariance matrix

  • C_l (ndarray[tuple[dim_obs_l, rank], np.float64]) – Output matrix

    The 1st-th dimension dim_obs_l is Dimension of local observation vector The 2nd-th dimension rank is Rank of initial covariance matrix