Adopting Model Uncertainties for Tropical Cyclone Intensity Prediction

Abstract

uantifying and reducing the uncertainty of model parameterizations using observations is evaluated for tropical cyclone (TC) intensity prediction. This is accomplished using a nonlinear inverse modeling technique that produces a joint probability density function (PDF) for a set of parameters. The dependence of estimated parameter values and associated uncertainty on two types of observable quantities is analyzed using an axisymmetric hurricane model. When the observation is only the maximum tangential wind speed, the joint PDF of parameter estimates has large variance and is multimodal. When the full kinematic field within the inner core of the TC is used for the observations, however, the joint parameter estimates are well constrained. These results suggest that model parameterizations may not be optimized using the maximum wind speed. Instead, the optimization should be based on observations of the TC structure to improve the intensity forecasts.