Lagrangian Tracer Prediction Using NCOM and Its 4DVAR Analysis System with Simulated SWOT Observations

NASA’s Surface Water and Ocean Topography (SWOT) satellite, scheduled for launch in 2022, is expected to provide observations of the ocean sea surface height (SSH) with greater spatial coverage and resolution than current altimeters. Including SWOT observations in modern ocean data assimilation and forecasting should lead to greatly improved model representation of the SSH at the mesoscale to sub-mesoscale, depending on model resolution. This present work aims to investigate the potential impact of SWOT observations on the ocean model’s ability to properly represent the surface ocean current in the presence of mesoscale eddies and fronts. To do this, an Observing System Simulation Experiment (OSSE) is set up using the Navy Coastal Ocean Model (NCOM) and its four-dimensional variational (4D-Var) data analysis component. SWOT observations are sampled from a model “nature” run using the SWOT Simulator, made available from the SWOT science team. The model surface current is evaluated by way of simulated Lagrangian drifter trajectories. The Lagrangian drifters are advected by the surface currents in the “nature” run and compared to the assimilative model, with mean separation distance over time as the primary metric for success. Three assimilative model runs are made, (1) assimilation of SSH observations from a set of “traditional” nadir-looking altimeters, (2) from the simulated SWOT sampling, and (3) a combination of traditional altimeters and SWOT. A non-assimilative model is also run for comparison. The results shown here indicate that assimilating SWOT observations constrains the model surface current field more accurately than using only nadir-looking altimeters. In addition to this, including all altimeters together gives the best representation, as determined by the smaller Lagrangian drifter separation distance over 24, 48, 72, and 96 hours.