Identification of Tropical Cyclone Storm Types Using Crowdsourcing

Abstract

he Cyclone Center project maintains a website that allows visitors to answer questions based on tropical cyclone satellite imagery. The goal is to provide a reanalysis of satellite-derived tropical cyclone characteristics from a homogeneous historical database composed of satellite imagery with a common spatial resolution for use in long-term, global analyses. The determination of the cyclone ?type? (curved band, eye, shear, etc.) is a starting point for this process. This analysis shows how multiple classifications of a single image are combined to provide probabilities of a particular image?s type using an expectation?maximization (EM) algorithm. Analysis suggests that the project needs about 10 classifications of an image to adequately determine the storm type. The algorithm is capable of characterizing classifiers with varying levels of expertise, though the project needs about 200 classifications to quantify an individual?s precision. The EM classifications are compared with an objective algorithm, satellite fix data, and the classifications of a known classifier. The EM classifications compare well, with best agreement for eye and embedded center storm types and less agreement for shear and when convection is too weak (termed no-storm images). Both the EM algorithm and the known classifier showed similar tendencies when compared against an objective algorithm. The EM algorithm also fared well when compared to tropical cyclone fix datasets, having higher agreement with embedded centers and less agreement for eye images. The results were used to show the distribution of storm types versus wind speed during a storm?s lifetime.