MCA: Inhomogeneous Non-equilibrium Marine Atmospheric Boundary Layer in Coastal Region: A Call for Data-driven Modeling to Interconnect Turbulence, Oceanography, and Meteorology

Population density and growth are high in coastal regions in the US and around the globe, resulting in more and more people being affected by weather that spans the land-atmosphere-ocean interface. This award will address processes in the atmosphere near the ocean surface, including turbulence and air-sea interactions that result in sea-spray aerosol production and transport. Turbulence and sea-spray aerosol are important for air quality, storm preparedness, safeguarding and restoration planning of near-shore ecosystems, ship operations, national defense, and offshore renewable energies. As an NSF Mid-Career Advancement award, it will also allow for the lead researcher to expand scientific horizons to become well-versed in coastal meteorology and physical oceanography. This NSF award is intended to fill a knowledge gap in understanding and modeling of Marine Atmospheric Boundary Layer (MABL) turbulence and Sea Spray Aerosol (SSA) transport in coastal regions. The project will quantify limitations in current techniques that are not optimized for coastal environments, identify interactions between waves and atmospheric turbulence, and develop machine learning models of MABL wind turbulence and SSA transport. To address these goals, the researcher and collaborators will make field observations of wind and aerosols in the vicinity of Martha’s Vineyard, Massachusetts using a scanning Doppler LiDAR and existing meteorological and oceanographic data. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.