pyPDAF.assim_offline_enkf_nondiagr

pyPDAF.assim_offline_enkf_nondiagr()

Offline assimilation of global or Covariance localised stochastic EnKF for a single DA step using non-diagonal observation error covariance matrix.

See pyPDAF.PDAF3.assim_offline() for diagonal observation error covariance matrix.

This stochastic EnKF is implemented based on [1]

This is the only scheme for covariance localisation with non-diagonal observation error covariance matrix in PDAF.

User-supplied functions are executed in the following sequence:

1. py__prepoststep_state_pdaf

2. py__init_dim_obs_pdaf

3. py__obs_op_pdaf (for each ensemble member)

4. py__localize_pdaf

5. py__add_obs_err_pdaf

6. py__init_obscovar_pdaf

7. py__obs_op_pdaf (repeated to reduce storage)

8. core DA algorithm

9. py__prepoststep_state_pdaf

References

[1]

Houtekamer, P. L., and H. L. Mitchell (1998): Data Assimilation Using an Ensemble Kalman Filter Technique. Mon. Wea. Rev., 126, 796–811, doi: 10.1175/1520-0493(1998)126<0796:DAUAEK>2.0.CO;2.

Parameters:

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

• py__obs_op_pdaf (Callable) – Full observation operator

• py__add_obs_err_pdaf (Callable) – Add observation error covariance matrix

• py__init_obs_covar_pdaf (Callable) – Initialize mean observation error variance

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

Returns:

outflag – Status flag

Return type:

int