RAPID: Dual-Doppler Analysis of the Boundary Layer in Category-4 Hurricane Laura

Hurricane Laura made landfall in southwestern Louisiana in August 2020 as a high-end Category 4 storm. The landfall represented a unique opportunity to make scientific measurements of one of the strongest storms to hit the United States. Two Doppler on Wheels mobile radars captured fine-scale details of the low-level winds of the hurricane, including the eyewall.

This award is for analysis of the radar data in relation to scientific questions about the lowest levels of a hurricane, known as the hurricane boundary layer. This data has particular relevance to the hurricane forecasting community, as measurements of properties that are important for accurate numerical modeling are difficult to conduct. Thus, the data may have significant relevance to forecasting of future hurricane landfalls, which is important for public safety and the economy.

The research team plans to analyze dual-Doppler data collected in the eyewall of Hurricane Laura to improve understanding of hurricane boundary layer structure in an intense hurricane. Multiple hours of continuous dual-Doppler data were collected in the inner bands and eyewall of Laura, which is a unique dataset for major hurricane landfalls at or near peak intensity. The researchers will derive 3D wind structure very near the surface (<100m above ground level) as a function of time, along with Turbulent Kinetic Energy (TKE), Momentum Flux, and the Momentum Exchange (Drag) Coefficient (Cd). The main questions to be addressed using these data include: 1) How do hurricane boundary layer roll characteristics and associated TKE, fluxes, and Cd vary as a function of background wind speed and location in the hurricane, and 2) How do hurricane boundary layer structures affect measured wind gusts?

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.