Reconstructing Fine‐Scale Ocean Variability via Data Assimilation of the SWOT Pre‐Launch In Situ Observing System

After the surface water and ocean topography (SWOT) satellite launches in 2022, an in-situ field campaign will be conducted to estimate the ocean state for calibration and validation (CalVal) purposes. It is demonstrably difficult to capture the sea surface height (SSH) features that are the focus of SWOT, with short time periods (<20 days) and fine-scale spatial structures (15-150 km). Therefore, a critical component of the SWOT CalVal will be a data assimilation (DA) system coupled to a primitive equation numerical model that can estimate: (a) the 2D SSH over the SWOT swaths, and (b) the 3D dynamical (velocity) field. To explore the ability of DA to meet the challenges of SWOT fine-scale observations, a multiscale DA system based on an extended 3D variational method has been developed. Here, we present a strategic evaluation of this DA system, with a focus to assimilate in-situ data from an observing system to best represent the fine-scale ocean variability at the CalVal site. The DA estimate is compared to independent observations taken during the 2019 pre-launch field campaign 300-km off Monterey Bay, California. The key result is this DA system can reconstruct the upper 500-m steric height with O(1-cm) error at hourly resolution, and subcentimeter error for periods longer than 2 days.