Characterizing the Impacts of Turbulence Closures on Real Hurricane Forecasts: A Comprehensive Joint Assessment of Grid Resolution, Horizontal Turbulence Models, and Horizontal Mixing Length

Hurricanes are highly complex geophysical flows that have caused billions of dollars in damage in recent years. Despite the significance of these extreme weather events, the turbulence mechanisms that derive the dynamics of hurricane flow systems are poorly understood and ineffectively parameterized in numerical weather prediction (NWP) models. The objective of this study is to bridge these knowledge gaps by assessing the accuracy and deficiencies of existing horizontal turbulence models in NWPs for hurricane forecasts. In particular, the Weather and Research Forecasting (WRF) Model is employed to conduct 135 simulations of five real hurricanes by varying the grid resolution, turbulence models, and horizontal mixing length values. Decreasing the default horizontal mixing length values both in low and high resolution WRF simulations significantly improves the wind intensity forecasts. This result indicates that the existing horizontal diffusion parameterizations are overly dissipative for hurricane flows, and thus, generate a weaker vortex compared to observations. These deficiencies are shown to stem from the horizontal mixing-length parameterization in WRF that is prescribed as a function of grid size without considering the physics of the flows (e.g., rotation). The paper provides notable insights into the role of turbulent fluxes in simulated hurricane evolutions that can be useful to advance the turbulence parameterizations of NWP models for hurricane forecasts.

Automated summary

This study assesses the accuracy and deficiencies of existing turbulence models in hurricane forecasts and explores the impact of turbulence mechanisms on hurricane flow. The results show that the existing horizontal diffusion parameterizations are overly dissipative for hurricane flows, indicating the need to improve turbulence parameterizations in models for better prediction accuracy.