A hybrid ensemble-OI Kalman filter for efficient data assimilation into a 3-D biogeochemical model of the Mediterranean

Tsiaras, Kostas P.; Hoteit, Ibrahim; Kalaroni, Sofia; Petihakis, George; Triantafyllou, George

A hybrid ensemble-OI Kalman filter for efficient data assimilation into a 3-D biogeochemical model of the Mediterranean

Files

ODYN-D-16-00181.pdf (3.23 MB)

Type

Article

Authors

Tsiaras, Kostas P.
Hoteit, Ibrahim

Kalaroni, Sofia
Petihakis, George
Triantafyllou, George

KAUST Department

Earth Fluid Modeling and Prediction Group
Earth Science and Engineering Program
Physical Science and Engineering (PSE) Division

Online Publication Date

2017-04-20

Print Publication Date

2017-06

Date

2017-04-20

Abstract

A hybrid ensemble data assimilation scheme (HYBRID), combining a flow-dependent with a static background covariance, was developed and implemented for assimilating satellite (SeaWiFS) Chl-a data into a marine ecosystem model of the Mediterranean. The performance of HYBRID was assessed against a model free-run, the ensemble-based singular evolutive interpolated Kalman (SEIK) and its variant with static covariance (SFEK), with regard to the assimilated variable (Chl-a) and non-assimilated variables (dissolved inorganic nutrients). HYBRID was found more efficient than both SEIK and SFEK, reducing the Chl-a error by more than 40% in most areas, as compared to the free-run. Data assimilation had a positive overall impact on nutrients, except for a deterioration of nitrates simulation by SEIK in the most productive area (Adriatic). This was related to SEIK pronounced update in this area and the phytoplankton limitation on phosphate that lead to a built up of excess nitrates. SEIK was found more efficient in productive and variable areas, where its ensemble exhibited important spread. SFEK had an effect mostly on Chl-a, performing better than SEIK in less dynamic areas, adequately described by the dominant modes of its static covariance. HYBRID performed well in all areas, due to its “blended” covariance. Its flow-dependent component appears to track changes in the system dynamics, while its static covariance helps maintaining sufficient spread in the forecast. HYBRID sensitivity experiments showed that an increased contribution from the flow-dependent covariance results in a deterioration of nitrates, similar to SEIK, while the improvement of HYBRID with increasing flow-dependent ensemble size quickly levels off.

Citation

Tsiaras KP, Hoteit I, Kalaroni S, Petihakis G, Triantafyllou G (2017) A hybrid ensemble-OI Kalman filter for efficient data assimilation into a 3-D biogeochemical model of the Mediterranean. Ocean Dynamics. Available: http://dx.doi.org/10.1007/s10236-017-1050-7.

Acknowledgements

This work was supported by EU OPEC project, funded from the European Union’s Seventh Framework Program (FP7/2007-2013) under grant agreement n° 283291. We thank Dionysios Raitsos for kindly providing the SeaWiFS Chl-a data.