pyPDAF.PDAF.assimilate_prepost

pyPDAF.PDAF.assimilate_prepost()

It is used to preprocess and postprocess of the ensemble.

No DA is performed in this function. Compared to pyPDAF.PDAF.prepost(), this function sets assimilation flag, which means that it is acted as an assimilation in PDAF.

The function is a combination of pyPDAF.PDAF.put_state_prepost() and pyPDAF.PDAF.get_state().

This function executes the user-supplied functions in the following sequence:

  1. py__collect_state_pdaf

  2. py__prepoststep_state_pdaf (preprocess, step < 0)

  3. py__prepoststep_state_pdaf (postprocess, step > 0)

  4. py__distribute_state_pdaf

  5. py__next_observation_pdaf

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

    Routine to distribute 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__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__next_observation_pdaf (Callable[stepnow:int, nsteps:int, doexit:int, time:float]) –

    Provide time step and time of next observation

    Callback Parameters
    • stepnowint

      • number of the current time step

    • nstepsint

      • number of time steps until next obs

    • doexitint

      • whether to exit forecasting (1 for exit)

    • timefloat

      • current model (physical) time

    Callback Returns
    • nstepsint

      • number of time steps until next obs

    • doexitint

      • whether to exit forecasting (1 for exit)

    • timefloat

      • current model (physical) time

Returns:

flag – Status flag

Return type:

int