pyPDAF.PDAF.omit_obs_omi

pyPDAF.PDAF.omit_obs_omi()

This function computes innovation and omit corresponding observations in assimilation if the innovation is too large. This function is used by some of the global filters, e.g. EnKF, LEnKF, PF, NETF, with OMI.

Parameters:

  • state_p (ndarray[tuple[dim_p], np.float64]) –

    on exit: PE-local forecast mean state

    The array dimension dim_p is PE-local dimension of model state

  • ens_p (ndarray[tuple[dim_p, dim_ens], np.float64]) –

    PE-local state ensemble

    The 1st-th dimension dim_p is PE-local dimension of model state The 2nd-th dimension dim_ens is Size of ensemble

  • obs_p (ndarray[tuple[dim_obs_p], np.float64]) –

    PE-local observation vector

    The array dimension dim_obs_p is PE-local dimension of observation vector

  • py__init_obs_pdaf (Callable[step:int, dim_obs_p:int, observation_p : ndarray[tuple[dim_obs_p], np.float64]]) –

    Initialize observation vector

    Callback Parameters
    • stepint

      • Current time step

    • dim_obs_pint

      • Size of the observation vector

    • observation_pndarray[tuple[dim_obs_p], np.float64]

      • Vector of observations

    Callback Returns
    • observation_pndarray[tuple[dim_obs_p], np.float64]

      • Vector of observations

  • py__obs_op_pdaf (Callable[step:int, dim_p:int, dim_obs_p:int, state_p : ndarray[tuple[dim_p], np.float64], m_state_p : ndarray[tuple[dim_obs_p], np.float64]]) –

    Observation operator

    Callback Parameters
    • stepint

      • Current time step

    • dim_pint

      • Size of state vector (local part in case of parallel decomposed state)

    • dim_obs_pint

      • Size of observation vector

    • state_pndarray[tuple[dim_p], np.float64]

      • Model state vector

    • m_state_pndarray[tuple[dim_obs_p], np.float64]

      • Observed state vector (i.e. the result after applying the observation operator to state_p)

    Callback Returns
    • m_state_pndarray[tuple[dim_obs_p], np.float64]

      • Observed state vector (i.e. the result after applying the observation operator to state_p)

  • compute_mean (int) –

    1. compute mean; (0) state_p holds mean

  • screen (int) – Verbosity flag

Returns:

  • state_p (ndarray[tuple[dim_p], np.float64]) – on exit: PE-local forecast mean state

    The array dimension dim_p is PE-local dimension of model state

  • obs_p (ndarray[tuple[dim_obs_p], np.float64]) – PE-local observation vector

    The array dimension dim_obs_p is PE-local dimension of observation vector