pyPDAF.PDAF.put_state_enkf

pyPDAF.PDAF.put_state_enkf()

It is recommended to use pyPDAF.PDAF.omi_put_state_global() or pyPDAF.PDAF.omi_put_state_enkf_nondiagR().

PDAF-OMI modules require fewer user-supplied functions and improved efficiency.

Stochastic EnKF (ensemble Kalman filter) [1] for a single DA step without OMI.

Compared to pyPDAF.PDAF.assimilate_enkf(), 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 function should be called at each model time step.

This function executes the user-supplied functions 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 ensemble mean)

  5. py__add_obs_err_pdaf

  6. py__init_obs_pdaf

  7. py__init_obscovar_pdaf

  8. py__obs_op_pdaf (for each ensemble member)

  9. core DA algorithm

Deprecated since version 1.0.0: This function is replaced by pyPDAF.PDAF.omi_put_state_global() and pyPDAF.PDAF.omi_put_state_enkf_nondiagR()

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__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__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__add_obs_err_pdaf (Callable[step:int, dim_obs_p:int, C_p : ndarray[tuple[dim_obs_p, dim_obs_p], np.float64]]) –

    Add obs error covariance R to HPH in EnKF

    Callback Parameters
    • stepint

      • Current time step

    • dim_obs_pint

      • Dimension of observation vector

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

      • Matrix to that observation covariance R is added

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

      • Matrix to that observation covariance R is added

  • py__init_obs_covar_pdaf (Callable[step:int, dim_obs:int, dim_obs_p:int, covar:float, obs_p : ndarray[tuple[dim_obs_p], np.float64], isdiag:bool]) –

    Initialize obs. error cov. matrix R in EnKF

    Callback Parameters
    • stepint

      • Current time step

    • dim_obsint

      • Global size of observation vector

    • dim_obs_pint

      • Size of process-local observation vector

    • covarfloat

      • Observation error covariance matrix

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

      • Process-local vector of observations

    • isdiagbool
    Callback Returns
    • covarfloat

      • Observation error covariance matrix

    • isdiagbool

Returns:

flag – Status flag

Return type:

int