Hurricane Landfalls

During landfall, atmospheric flow adapts to abrupt changes in surface conditions from water to land, forming an internal boundary layer (IBL). Understanding IBLs is crucial for accurately modeling and predicting hurricane intensity and near-surface wind hazards during landfall. This study presents a new turbulence-resolving modeling framework for investigating the landfall transition of the near-surface wind profile within the IBL, and provides a way to quantify the uncertainty of 10-m wind estimates derived from different observation-based approaches for the first time. "Virtual" sonde wind profiles (upper left plot) sample the winds at different locations and heights downstream, leading to significant uncertainty in 10-m winds estimates by imposing a log-law fit. In contrast, "radar"-based wind retrievals (gray line in lower left plot) show promise to derive the IBL wind profile with sufficient fine vertical resolution. These findings can guide future landfall missions and forecast products.

Chen and Rozoff, GRL, 2025 & Press release [AGU Media Highlight]

Accurately estimating near-surface winds during hurricane landfalls is crucial for enhancing situational awareness and facilitating post-storm recovery efforts. Previous studies have used coastal radar Velocity Azimuth Display (VAD) winds to estimate 10-m winds during hurricane landfalls (Krupar et al., 2016) by applying a log-law fit to the VAD wind profile in the lowest few hundred meters. However, a notable weak bias was observed. This study demonstrates that the significant weak bias results from neglecting the wind structure within a shallow internal boundary layer (IBL), a “knee-like” structure in the VAD wind profiles below 200 m [see right panels of the plot for Ida (2021) and Zeta (2020)]. Results indicate that leveraging the IBL winds is essential for more accurate 10-m wind estimates during hurricane landfalls. This study marks the first time the uncertainty in estimating near-surface wind over inhomogeneous land surfaces has been assessed. 

Leffler, Chen, Carey, Knupp, and Lee, GRL, 2025