pyPDAF.assim_offline_local_nondiagr

pyPDAF.assim_offline_local_nondiagr()

Offline assimilation of domain local filters for a single DA step using non-diagnoal observation error covariance matrix.

Here, this function call is used for LE(S)TKF [1] and LSEIK [1] 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. py__init_obs_l_pdaf

   4. py__prodRinvA_l_pdaf

   5. core DA algorithm

6. py__prepoststep_state_pdaf

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

Parameters:

• 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__prodrinva_l_pdaf (Callable) – Provide product of inverse of R with matrix A

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

Returns:

outflag – Status flag

Return type:

int