pyPDAF.assim_offline_lnetf_nondiagr

pyPDAF.assim_offline_lnetf_nondiagr()

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

See pyPDAF.PDAF3.assim_offline() for using diagnoal observation error covariance matrix. The non-linear filter is proposed in [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__likelihood_l_pdaf

   4. core DA algorithm

6. py__prepoststep_state_pdaf

References

[1]

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.

Parameters:

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

• py__obs_op_pdaf (Callable) – Full observation operator

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

• 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__likelihood_l_pdaf (Callable) – Compute likelihood and apply localization

Returns:

outflag – Status flag

Return type:

int