pyPDAF.assimilate_enkf_nondiagr

pyPDAF.assimilate_enkf_nondiagr()

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

See pyPDAF.PDAF3.assimilate() 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__collect_state_pdaf

2. py__prepoststep_state_pdaf

3. py__init_dim_obs_pdaf

4. py__obs_op_pdaf (for each ensemble member)

5. py__localize_pdaf

6. py__add_obs_err_pdaf

7. py__init_obscovar_pdaf

8. py__obs_op_pdaf (repeated to reduce storage)

9. core DA algorith

10. py__prepoststep_state_pdaf

11. py__distribute_state_pdaf

12. py__next_observation_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__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__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

• py__next_observation_pdaf (Callable) – Provide information on next forecast

Returns:

outflag – Status flag

Return type:

int