Principle Investigator: Bia Villas Boas (Colorado School of Mines)

Co-Investigator(s): Momme Hell, Luc Lenain

Collaborator(s): Fabrice Ardhuin, Gwendal Marechal


The ocean and atmosphere are intricately connected through exchanges of energy, momentum, heat, and CO2 in the air-sea transition zone (ASTZ). These exchanges occur across a wide range of spatial scales, from millimeter-sized capillary waves to mesoscale currents spanning hundreds of kilometers, making observation difficult. SWOT observations offer an unprecedented opportunity to study sea surface topography and roughness at scales where air-sea coupling processes shift dramatically.

Surface gravity waves, which dominate sea surface variability at scales shorter than 10 km, affect how momentum is transferred from the atmosphere to the ocean, impact upper-ocean mixing via Langmuir turbulence, and are key to interpreting radar signals. The interaction between waves, winds, and currents creates spatial gradients in the wave field at meso- and submesoscales, which remain poorly understood. This project seeks to explore the processes that contribute to sea surface height (SSH) variability at scales at scales similar to SWOT's footprint and how these processes might affect SWOT observations. In particular, we focus on the following questions:

  1. How do wave groups and wave-current interactions contribute to sea state gradients at SWOT scales?
  2. How do the spatial scales of wave group and current modulation vary by season and geography? Under what conditions do surface waves dominate the high-wavenumber end of SWOT SSH products?
  3. Are SWOT's bulk wave variable products consistent with theoretical and numerical predictions of significant wave height (Hs) variability? Can we refine the across-swath wave products using analytical tools?
Wave-current interactions and wave-group modulation impact the Significant Wave Height (Hs). Bia Villas Boas
Wave-current interactions and wave-group modulation impact the Significant Wave Height (Hs). This project will combine wave modeling with observations from SWOT and airborne lidar to characterize the spatial variability of surface waves.