We perform an Observing System Simulation Experiment that simulates the satellite sampling and the mapping procedure on the sea surface of the high-resolution model CROCO-MED60v40, to investigate the reliability and the accuracy of the eddy detection. The main result of this study is a strong cyclone-anticyclone asymmetry of the eddy detection on the altimetry products AVISO/CMEMS in the Mediterranean Sea. Large-scale cyclones having a characteristic radius larger than the local deformation radius are much less reliable than large-scale anticyclones. We estimate that less than 60% of these cyclones detected on gridded altimetry product are reliable, while more than 85% of mesoscale anticyclones are reliable. Besides, both the barycenter and the size of these mesoscale anticyclones are relatively accurate. This asymmetry comes from the difference of stability between cyclonic and anticyclonic eddies. Large mesoscale cyclones often split into smaller sub-mesoscale structures having a rapid dynamical evolution. The numerical model CROCO-MED60v40 shows that this complex dynamic is too fast and too small to be accurately captured by the gridded altimetry products. The spatio-temporal interpolation smoothes out this sub-mesoscale dynamics and tends to generate an excessive number of unrealistic mesoscale cyclones in comparison with the reference field. On the other hand, large mesoscale anticyclones, which are more robust and which evolve more slowly, can be accurately tracked by standard altimetry products. We also confirm that the AVISO/CMEMS products induce a bias on the eddy intensity. The azimuthal geostrophic velocities are always underestimated for large mesoscale anticyclones.

Sea Surface Height from igh-resolution model of the Mediteranean Sea

We used the results of a realistic numerical simulation that was carried out for the Mediterranean Sea using the CROCO numerical model (http://www.croco-ocean.org). For more details on the numerical characteristics of the CROCO model we refer to Shchepetkin and McWilliams (2005), Debreu et al (2012), and Auclair et al (2018). The simulation under investigation, CROCO-MED60v40-15-16, was forced at the ocean surface with ARPEGE HR analysed meteorological fields (winds, pressure, air temperature, relative humidity) thanks to the classical bulk COARE formula (Fairall et al (2003). The standard primitive equations are solved with an horizontal resolution of 1/60° in both longitudinal and latitudinal directions. The vertical coordinate used is a generalised terrain following one. It is stretched to keep as flat as possible the levels near the surface whatever the bathymetry gradient is. Fourty unevenly distributed vertical levels discretized the water column. They are closer one from each other next to the surface and more spaced by the bottom where the vertical gradients of hydrology parameters (temperature or salinity) are weak. The initial and the boundary conditions were built from CMEMS global system analysis optimally interpolated on the computational grid. CROCO-MED60v40-15-16 is a result of a free run simulation (no nudging nor assimilation of any kind) that started on the 1st of August 2012 when the water column stability is at its maximum to avoid static instability in the spinning up phase. It ran till the end of December 2016.
 

Sea Surface Height from Observing System Simulation Experiment

An Observing System Simulation Experiment (OSSE) is performed in a four-satellite configuration, composed of the reference mission Jason-3 and three other missions Sentinel3-A, Sentinel3-B, and Cryosat-2. As the CROCO-MED60v40-15-16 resolves the response of the ocean to atmospheric pressure disturbances, it contains large-scale high-frequency signals that cannot be handled by the mapping method based on Optimal Interpolation. The SWOT simulator software (Gaultier et al, 2016) is then used to generated along track with realistic measurement errors and noise. The resulting ground truth references are finally ingested in the AVISO/CMEMS mapping procedure (Taburet et al, 2019) to compute gridded fields. Two distinct OSSE were performed. For the OSSE-SSH-D, the optimal interpolation of individual altimetry tracks (LeTraon et al, 1998) is made on backward and forward tracks within a time window of +/- 5 days. On the other hand, the OSSE-SSH-NRT maps are built every day with the altimetry tracks of the past ten days.

Reference

Auclair, F., Benshila, R., Debreu, L., Ducousso, N., Dumas, F., Marchesiello, P., & Lemari ́e, F. (2018, February). Some Recent Developments around the CROCO Initiative for Complex Regional to Coastal Modeling. In COMOD 2018 - Workshop on Coastal Ocean Modelling(p. 1-47). Hambourg, Germany.

Debreu, L., Marchesiello, P., Penven, P., & Cambon, G.(2012). Two-way nesting in split-explicit ocean models: Algorithms, implementation and validation. Ocean Modelling, 49-50, 1–21.

Fairall, C. W., Bradley, E. F., Hare, J. E., Grachev, A. A., & Edson, J. B.(2003,02).Bulk Parameterization of Air–Sea Fluxes: Updates and Verification for the COARE Algorithm.Journal of Climate,16(4), 571-591.

Gaultier, L., Ubelmann, C., & Fu, L.-L.(01 Jan. 2016). The challenge of using future swot data for oceanic field reconstruction. Journal of Atmospheric and Oceanic Technology,33(1), 119 - 126.

Le Traon, P. Y., Nadal, F., & Ducet, N. (1998, 04). An Improved Mapping Method of Multisatellite Altimeter Data. Journal of Atmospheric and Oceanic Technology,15(2), 522-534.

Shchepetkin, A. F., & McWilliams, J. C. (2005). The regional oceanic modeling system (roms): a split-explicit, free-surface, topography-followingcoordinate oceanic model. Ocean Modelling,9(4), 347–404.

Taburet, G., Sanchez-Roman, A., Ballarotta, M., Pujol, M.-I., Legeais, J.-F., Fournier,... F., Dibarboure, G. (2019). Duacs dt 2018: 25 years of reprocessed sea level altimetry products. Ocean Science, 15(5), 1207–1224.

SSH_CROCO-MED60v40-15-16.nc contains SSH from croco model outputs
with corresponding longitude, latitude and land_mask in lon_lat_CROCO.nc

SSH_OSSE_DT_2015_2016.nc and SSH_OSSE_NRT_2015_2016.nc contains SSH from the Delayed (DT) and Near-Real Time (NRT) Observing System Simulation Experiment of the Delayed
with corresponding longitude, latitude and data_mask in lon_lat_OSSE.nc