Improving hurricane intensity and streamflow forecasts in coupled hydro-meteorological simulations by analyzing precipitation and boundary layer schemes

Abstract

Hurricanes have been the most destructive and expensive hydro-meteorological event in US 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 and Research Forecasting (WRF) atmospheric model is coupled with the WRF hydrological model (WRF-Hydro) to simulate four major hurricanes landfalling in three hurricane-prone regions in the US. First, a stepwise calibration is carried out in WRF-Hydro, which remarkably reduces streamflow forecast errors compared to the United States Geological Survey (USGS) gauges. Then, 60 coupled hydro-meteorological 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 over-dissipative 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.