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

Abstract

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. Plain Language Summary With climate change and global warming, more intense hurricanes are projected to occur, potentially causing significant hazards to humans and infrastructure. It is thus imperative to improve hurricane forecasts in order to mitigate their economic ramifications. Despite the significance of turbulence in the evolution of hurricanes, our knowledge on the impact of turbulence models on real hurricane simulations is limited. This study bridges this gap by conducting multiple real hurricane simulations using different turbulence models. The results show that increasing the grid resolution generally improves the wind intensity forecasts in hurricanes. However, all considered turbulence closures underpredict the observed maximum surface wind speed in the investigated cases. This underperformance is partly associated with the improper horizontal mixing parameterizations in weather models that do not consider hurricane dynamics. We conducted new hurricane simulations by modifying the default turbulence models. The results indicated a remarkable improvement in hurricane intensity forecasts. The study shows that such physical parameterizations in weather models need to consider the unique structure of hurricane flows in order to enhance their forecasts.