Assessing the impacts of assimilating satellite SST in addition to along-track SLA into a HYCOM of the Agulhas System - Rapeti, Tharone (Department of Oceanography, 2018)
The greater Agulhas Current System, is considered to be the largest western boundary current in the Southern Hemisphere, with only the Gulf Stream, and possibly the Kuroshio, considered to be larger globally (Bryden et al., 2005). The Current System is a crucial factor for determining the mean state and variability of the regional marine environment, resources and ecosystems in the region, regional weather, as well as the global climate on a broad range of temporal and spatial scales. Due to an absence of a coherent in situ and satellite-based observing system in the area, modelling and data assimilation techniques are utilised. These both further the quantitative understanding of the ocean dynamics as well as providing better forecasts of this complicated western boundary current system. In this study, we compare two assimilation experiments using the Ensemble Optimal Interpolation (EnOI) data assimilation scheme in a regional implementation of the Hybrid Coordinate Ocean Model (HYCOM). In the first experiment, we assimilate along-track satellite sea level anomaly (SLA) data only, and in the second experiment we assimilate both along-track sea level anomaly (SLA) as well as satellite sea surface temperature (SST) data. The objectives of the study are to investigate the impacts of assimilating SST along with SLA into the regional HYCOM model, with the hopes of improving the model performance. The long term aim of this experiment is to develop a regional ocean prediction system. The additional assimilation of SST along with SLA into the HYCOM model, has improved upon the representation of the SST field across the region by reducing the error. However, with regards to velocity, surface eddy kinetic energies (EKE), as well as subsurface velocities, the updated SST model shows less improvement. A velocity bias can be seen as the reason for underperformance in these aspects. The model still struggles to recreate subsurface water masses, underestimating salinity in the upper 500 m; assimilating T/S profiles in the future could improve on this. The assimilation of SST has improved upon the SST-SSH correlation in the model, as well as the spatial distribution and accuracy. The assimilation of SST along with SLA has had many positive impacts, with unfortunately, a few negatives. The shortcomings of the numerical model will have to be improved upon and additional assimilation variables should be tested in further studies, to provide a solid forecasting system.