Application of a new parametric wind field model for improving hurricane storm surge predictions of SLOSH

The fidelity of hurricane storm surge forecasts is largely based on accurate specification of the storm wind forcing. In both real-time forecasting and long-term risk analysis, parametric wind models are often used to describe the surface wind field. The surface wind field can be estimated as the sum of an axisymmetric wind profile of the storm and a background wind field of the environment. The Sea, Lake, and Overland Surge from Hurricanes (SLOSH) model is the operational storm surge model of the National Hurricane Center, and uses a radial wind speed profile that is dependent on the maximum windspeed V_max and its radius. The same profile is used to nonlinearly scale the translation speed of the storm to model the background wind field. However, recent advances have shown that the surface wind field may be more accurately characterized by including Coriolis effects in the storm wind profile, and scaling the translation speed by a constant factor. In this work, we apply these findings to the SLOSH model. We also remove the iterative procedure the SLOSH model uses to solve for V_max from pressure gradients, and input the true value directly. We hindcast historical hurricanes to show that storm surges can be more accurately estimated with these changes.