pyPDAF.assimilate_nonlin_nondiagr

pyPDAF.assimilate_nonlin_nondiagr()

Online assimilation of global nonlinear filters for a single DA step using non-diagonal observation error covariance matrix.

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

Here, this function call is used for global NETF [1], and particle filter [2]. The filter type is set in pyPDAF.PDAF.init(). This function should be called at each model time step.

The function is a combination of pyPDAF.PDAF.omi_put_state_global_nondiagR() and pyPDAF.PDAF.get_state().

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 ensemble mean)

5. py__obs_op_pdaf (for each ensemble member)

6. py__likelihood_pdaf

7. core DA algorithm

8. py__prepoststep_state_pdaf

9. py__distribute_state_pdaf

10. py__next_observation_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.

[2]

Van Leeuwen, P. J., Künsch, H. R., Nerger, L., Potthast, R., & Reich, S. (2019). Particle filters for high‐dimensional geoscience applications: A review. Quarterly Journal of the Royal Meteorological Society, 145(723), 2335-2365.

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__likelihood_pdaf (Callable) – Compute likelihood

• 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