Support vector machine techniques to predict tropical cyclone re-intensification following extratropical transition
In this study we extend this subset to the full volume of the western North Pacific for both potential temperature and equivalent potential temperature for input into the support vector machine (SVM) classifier, attaining ~80% PD with a ~26% FAR. In addition, we apply SVM to the full model data for the western North Pacific, a data set of ~600 million points, which a computationally intensive problem. One of the limitations of SVM is that it is a supervised learning technique, and does not elucidate the natural manifold of physically meaningful data embedded in the model data. For this reason we also apply Riemannian Manifold Learning (RML) to the model data for all variables and the volume of the western North Pacific region. The implementation of RML is being accomplished utilizing the CUDA framework, which uses consumer grade graphics cards (NVidia GTX 465s) for fast parallel computing. RML will potentially reduce the dimension of the relevant data, and give us a physically-meaningful manifold. The resulting manifold can then be submitted to a supervised learning algorithm for binary classification. We present our SVM results and introduce RML in this presentation.