pyPDAF.PDAF.omi_put_state_lenkf

pyPDAF.PDAF.omi_put_state_lenkf()

Stochastic EnKF (ensemble Kalman filter) with covariance localisation using diagnoal observation error covariance matrix without post-processing, distributing analysis, and setting next observation step.

See pyPDAF.PDAF.omi_put_state_lenkf_nondiagR() for non-diagonal observation error covariance matrix.

Stochastic EnKF (ensemble Kalman filter) with covariance localisation [1] for a single DA step.

Compared to pyPDAF.PDAF.omi_assimilate_lenkf(), this function has no get_state() call. This means that the analysis is not post-processed, and distributed to the model forecast by user-supplied functions. The next DA step will not be assigned by user-supplied functions as well. This function is typically used when there are not enough CPUs to run the ensemble in parallel, and some ensemble members have to be run serially. The pyPDAF.PDAF.get_state() function follows this function call to ensure the sequential DA.

This is the only scheme for covariance localisation in PDAF.

This function should be called at each model time step.

The user-supplied function is executed in the following sequence:

  1. py__collect_state_pdaf

  2. py__prepoststep_state_pdaf

  3. py__init_dim_obs_pdaf

  4. py__obs_op_pdaf (for each ensemble member)

  5. py__localize_pdaf

  6. py__obs_op_pdaf (repeated to reduce storage)

  7. core DA algorith

References

Parameters:

  • py__collect_state_pdaf (Callable[dim_p:int, state_p : ndarray[tuple[dim_p], np.float64]]) –

    Routine to collect a state vector

    Callback Parameters
    • dim_pint

      • pe-local state dimension

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

      • local state vector

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

      • local state vector

  • py__init_dim_obs_pdaf (Callable[step:int, dim_obs_p:int]) –

    Initialize dimension of observation vector

    Callback Parameters
    • stepint

      • current time step

    • dim_obs_pint

      • dimension of observation vector

    Callback Returns
    • dim_obs_pint

      • dimension of observation vector

  • 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)

  • py__prepoststep_pdaf (Callable[step:int, dim_p:int, dim_ens:int, dim_ens_p:int, dim_obs_p:int, state_p : ndarray[tuple[dim_p], np.float64], uinv : ndarray[tuple[dim_ens-1, dim_ens-1], np.float64], ens_p : ndarray[tuple[dim_p, dim_ens], np.float64], flag:int]) –

    User supplied pre/poststep routine

    Callback Parameters
    • stepint

      • current time step (negative for call after forecast)

    • dim_pint

      • pe-local state dimension

    • dim_ensint

      • size of state ensemble

    • dim_ens_pint

      • pe-local size of ensemble

    • dim_obs_pint

      • pe-local dimension of observation vector

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

      • pe-local forecast/analysis state (the array ‘state_p’ is not generally not initialized in the case of seik. it can be used freely here.)

    • uinvndarray[tuple[dim_ens-1, dim_ens-1], np.float64]

      • inverse of matrix u

    • ens_pndarray[tuple[dim_p, dim_ens], np.float64]

      • pe-local state ensemble

    • flagint

      • pdaf status flag

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

      • pe-local forecast/analysis state (the array ‘state_p’ is not generally not initialized in the case of seik. it can be used freely here.)

    • uinvndarray[tuple[dim_ens-1, dim_ens-1], np.float64]

      • inverse of matrix u

    • ens_pndarray[tuple[dim_p, dim_ens], np.float64]

      • pe-local state ensemble

  • py__localize_covar_pdaf (Callable[dim_p:int, dim_obs:int, hp_p : ndarray[tuple[dim_obs, dim_p], np.float64], hph : ndarray[tuple[dim_obs, dim_obs], np.float64]]) –

    Apply localization to HP and HPH^T

    Callback Parameters
    • dim_pint

      • pe-local state dimension

    • dim_obsint

      • number of observations

    • hp_pndarray[tuple[dim_obs, dim_p], np.float64]

      • pe local part of matrix hp

    • hphndarray[tuple[dim_obs, dim_obs], np.float64]

      • matrix hph

    Callback Returns
    • hp_pndarray[tuple[dim_obs, dim_p], np.float64]

      • pe local part of matrix hp

    • hphndarray[tuple[dim_obs, dim_obs], np.float64]

      • matrix hph

Returns:

flag – Status flag

Return type:

int