The Impact of Simulated SWOT Observations on the Ocean Model Subsurface Through Assimilation

Navy ocean models rely on altimeter observations to constrain the ocean mesoscale. This information is projected into the subsurface via data assimilation. Studies have shown the potential impact of Surface Water Ocean Topography (SWOT) observations on the surface topography, but how much it improves the ocean subsurface through assimilation is an open question. This study aims to examine this through a simulated observation study using two methods of data assimilation: a three-dimensional variational method (3DVAR) and a multiscale 3DVAR. While 3DVAR aims to correct the ocean model using a single prescribed correction scale, multiscale 3DVAR employs a multi-step process to refine the correction through a cascade of scales. For this study, we use an Observing System Simulated Experiment (OSEE) and JPL’s SWOT simulator to provide observations that are sampled from the nature run in a Southern California Navy Coastal Ocean Model (NCOM) domain from October-December 2021. The model’s performance will be evaluated in terms of important oceanographic quantities such as mixed layer depth (MLD) and thermocline depth (TD). The results of this study will help to evaluate the impact of SWOT observations on the ocean model and determine which data assimilation method best utilizes these observations for this purpose.