Experimental configuration

Filters

  • The Ensemble Kalman Filter (Evensen, 2003 1 )

  • The Ensemble Adjustment Kalman Filter (Anderson, 2003 2 )

  • The Ensemble Kalman Filter with Exact Second Order Perturbation Sampling (Hoteit et al. 2015 3 )

Note

sort_obs_inc must be set to .true. for EnKF-esops and EnKF.

Important

For Moha’s diagnostics: compute_posterior = .true.

Inflation

Inverse gamma adaptive (Gharamti, 2018 4 ) prior inflation will be used for each of the three experiments.

Saved output

The following files will be saved from the integration:

  • All obs_seq.final files

  • All prior inflation mean and standard deviation files

  • The preassim and output stages will be saved.

References

1

Evensen, G., 2003: The Ensemble Kalman Filter: theoretical formulation and practical implementation. Ocean Dynamics, 53, 343–367, https://doi.org/10.1007/s10236-003-0036-9.

2

Anderson, J. L., 2003: A Local Least Squares Framework for Ensemble Filtering. Monthly Weather Review, 131, 634–642.

3

Hoteit, I., D.-T. Pham, M. E. Gharamti, and X. Luo, 2015: Mitigating Observation Perturbation Sampling Errors in the Stochastic EnKF. Monthly Weather Review, 143, 2918–2936.

4

Gharamti M., 2018: Enhanced Adaptive Inflation Algorithm for Ensemble Filters. Monthly Weather Review, 2, 623-640, doi:10.1175/MWR-D-17-0187.1