Improving Hurricane Intensity and Streamflow Forecasts in Coupled Hydrometeorological Simulations by Analyzing Precipitation and Boundary Layer Schemes

Abstract

Abstract Hurricanes have been the most destructive and expensive hydrometeorological event in U.S. history, causing catastrophic winds and floods. Hurricane dynamics can significantly impact the amount and spatial extent of storm precipitation. However, the complex interactions of hurricane intensity and precipitation and the impacts of improving hurricane dynamics on streamflow forecasts are not well established yet. This paper addresses these gaps by comprehensively characterizing the role of vertical diffusion in improving hurricane intensity and streamflow forecasts under different planetary boundary layer, microphysics, and cumulus parameterizations. To this end, the Weather Research and Forecasting (WRF) atmospheric model is coupled with the WRF hydrological (WRF-Hydro) model to simulate four major hurricanes landfalling in three hurricane-prone regions in the United States. First, a stepwise calibration is carried out in WRF-Hydro, which remarkably reduces streamflow forecast errors compared to the U.S. Geological Survey (USGS) gauges. Then, 60 coupled hydrometeorological simulations were conducted to evaluate the performance of current weather parameterizations. All schemes were shown to underestimate the observed intensity of the considered major hurricanes since their diffusion is overdissipative for hurricane flow simulations. By reducing the vertical diffusion, hurricane intensity forecasts were improved by ∼39.5% on average compared to the default models. These intensified hurricanes generated more intense and localized precipitation forcing. This enhancement in intensity led to ∼16% and ∼34% improvements in hurricane streamflow bias and correlation forecasts, respectively. The research underscores the role of improved hurricane dynamics in enhancing flood predictions and provides new insights into the impacts of vertical diffusion on hurricane intensity and streamflow forecasts. Significance Statement Despite significant recent improvements, numerical weather prediction models struggle to accurately forecast hurricane intensity and track due to many reasons such as inaccurate physical parameterization for hurricane flows. Furthermore, the performance of existing physics schemes is not well studied for hurricane flood forecasting. This study bridges these knowledge gaps by extensively evaluating different physical parameterizations for hurricane track, intensity, and flood forecasts using an atmospheric model coupled with a hydrological model. Then, a reduced diffusion boundary layer scheme is developed, making remarkable improvements in hurricane intensity forecasts due to the overdissipative nature of the considered schemes for major hurricane simulations. This reduced diffusion model is shown to significantly enhance hurricane flood forecasts, indicating the significance of hurricane dynamics on its induced precipitation.