Bayesian Updating of Track-Forecast Uncertainty for Tropical Cyclones

Abstract

he accuracy of track forecasts for tropical cyclones (TCs) is well studied, but less attention has been paid to the representation of track-forecast uncertainty. Here, Bayesian updating is employed on the radius of the 70% probability circle using 72-h operational forecasts with comparisons made to the classical approach based on the empirical cumulative density (ECD). Despite an intuitive and efficient way of treating track errors, the ECD approach is statistically less informative than Bayesian updating. Built on a solid statistical foundation, Bayesian updating is shown to be a useful technique that can serve as a substitute for the classical approach in representing operational TC track-forecast uncertainty.