pyPDAF.PDAF.localomi_put_state

pyPDAF.PDAF.localomi_put_state()

Domain local filters for a single DA step using diagnoal observation error covariance matrix without post-processing, distributing analysis, and setting next observation step.

Here, this function call is used for LE(S)TKF [1], LSEIK [1], LNETF [2], and LKNETF [3]. The filter type is set in pyPDAF.PDAF.init(). Compared to pyPDAF.PDAF.localomi_assimilate_local(), 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.

The LESTKF is a more efficient equivalent to the LETKF.

This function should be called at each model time step.

User-supplied functions are executed in the following sequence:

1. py__collect_state_pdaf

2. py__prepoststep_state_pdaf

3. py__init_n_domains_p_pdaf

4. py__init_dim_obs_pdaf

5. py__obs_op_pdaf (for each ensemble member)

6. loop over each local domain:

   1. py__init_dim_l_pdaf

   2. py__init_dim_obs_l_pdaf

   3. core DA algorithm

References

[1] (1,2)

Nerger, L., Janjić, T., Schröter, J., Hiller, W. (2012). A unification of ensemble square root Kalman filters. Monthly Weather Review, 140, 2335-2345. doi:10.1175/MWR-D-11-00102.1

[2]

Tödter, J., and B. Ahrens, 2015: A second-order exact ensemble square root filter for nonlinear data assimilation. Mon. Wea. Rev., 143, 1347–1367, doi:10.1175/MWR-D-14-00108.1.

[3]

Nerger, L.. (2022) Data assimilation for nonlinear systems with a hybrid nonlinear Kalman ensemble transform filter. Q J R Meteorol Soc, 620–640. doi:10.1002/qj.4221

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 full 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]]) –

  Full 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__init_n_domains_p_pdaf (Callable[step:int, n_domains_p:int]) –

  Provide number of local analysis domains

  Callback Parameters

  • stepint

    • current time step

  • n_domains_pint

    • pe-local number of analysis domains

  Callback Returns

  • n_domains_pint

    • pe-local number of analysis domains

• py__init_dim_l_pdaf (Callable[step:int, domain_p:int, dim_l:int]) –

  Init state dimension for local ana. domain

  Callback Parameters

  • stepint

    • current time step

  • domain_pint

    • current local analysis domain

  • dim_lint

    • local state dimension

  Callback Returns

  • dim_lint

    • local state dimension

• py__init_dim_obs_l_pdaf (Callable[domain_p:int, step:int, dim_obs_f:int, dim_obs_l:int]) –

  Initialize local dimimension of obs. vector

  Callback Parameters

  • domain_pint

    • index of current local analysis domain

  • stepint

    • current time step

  • dim_obs_fint

    • full dimension of observation vector

  • dim_obs_lint

    • local dimension of observation vector

  Callback Returns

  • dim_obs_lint

    • local dimension of observation vector

• outflag (int) – Status flag

Returns:

outflag – Status flag

Return type:

int