pyPDAF.assim_offline

pyPDAF.assim_offline()

Offline ensemble filters and smoothers except for 3DVars for a single DA step using diagnoal observation error covariance matrix.

Here, this function call is used for global stochastic EnKF [1], E(S)TKF [2], EAKF, EnSRF, SEEK [2], SEIK [2], NETF [3], and particle filter [4]. The filter type is set in pyPDAF.PDAF.init(). This function should be called at each model time step.

User-supplied functions are executed in the following sequence:

  1. py__prepoststep_state_pdaf

  2. py__init_n_domains_p_pdaf

  3. py__init_dim_obs_pdaf

  4. py__obs_op_pdaf (for each ensemble member)

  5. loop over each local domain:

    1. py__init_dim_l_pdaf

    2. py__init_dim_obs_l_pdaf

    3. core DA algorithm

  6. py__prepoststep_state_pdaf

References

Parameters:

  • py__collect_state_pdaf (Callable) – Routine to collect a state vector

  • py__distribute_state_pdaf (Callable) – Routine to distribute a state vector

  • py__init_dim_obs_pdaf (Callable) – Initialize dimension of full observation vector

  • py__obs_op_pdaf (Callable) – Full observation operator

  • py__init_n_domains_p_pdaf (Callable) – Provide number of local analysis domains

  • py__init_dim_l_pdaf (Callable) – Init state dimension for local ana. domain

  • py__init_dim_obs_l_pdaf (Callable) – Initialize local dimimension of obs. vector

  • py__prepoststep_pdaf (Callable) – User supplied pre/poststep routine

  • py__next_observation_pdaf (Callable) – Provide information on next forecast

  • outflag (int) – Status flag

Returns:

outflag – Status flag

Return type:

int