Dense CTD survey versus glider fleet sampling: comparing data assimilation performance in a regional ocean model west of Sardinia

Abstract

Abstract. The REP14-MED sea trial carried out off the west coast of Sardinia in June 2014 provided a rich set of observations from both ship-based conductivity–temperature–depth (CTD) probes and a fleet of underwater gliders. We present the results of several simulations assimilating data either from CTDs or from different subsets of glider data, including up to eight vehicles, in addition to satellite sea level anomalies, surface temperature and Argo profiles. The Western Mediterranean OPerational forcasting system (WMOP) regional ocean model is used with a local multi-model ensemble optimal interpolation scheme to recursively ingest both lower-resolution large-scale and dense local observations over the whole sea trial duration. Results show the capacity of the system to ingest both types of data, leading to improvements in the representation of all assimilated variables. These improvements persist during the 3-day periods separating two analyses. At the same time, the system presents some limitations in properly representing the smaller-scale structures, which are smoothed out by the model error covariances provided by the ensemble. An evaluation of the forecasts using independent measurements from shipborne CTDs and a towed ScanFish deployed at the end of the sea trial shows that the simulations assimilating initial CTD data reduce the error by 39 % on average with respect to the simulation without data assimilation. In the glider-data-assimilative experiments, the forecast error is reduced as the number of vehicles increases. The simulation assimilating CTDs outperforms the simulations assimilating data from one to four gliders. A fleet of eight gliders provides similar performance to the 10 km spaced CTD initialization survey in these experiments, with an overall 40 % model error reduction capacity with respect to the simulation without data assimilation when comparing against independent campaign observations.