DeepRI: End-to-end Prediction of Tropical Cyclone Rapid Intensification from Climate Data (Proposals Track)
Renzhi Jing (Princeton University); Ning Lin (Princeton University); Yinda Zhang (Google LLC)
Abstract
Predicting rapid intensification (RI) is extremely critical in tropical cyclone forecasting. Existing deep learning models achieve promising results, however still rely on hand-craft feature. We propose to design an end-to-end deep learning architecture that directly predict RI from raw climate data without intermediate heuristic feature, which allows joint optimization of the whole system for higher performance.