A wind Radii of 13.9 m/s, 24.5 m/s, and 32.7 m/s forecast method for tropical cyclone in the Northwest Pacific Ocean and the south China sea based on subjective track intensity forecast and parametric wind field model (named as STI-RF-G) is developed. Here the improved CE wind field model by Shanghai Typhoon Institute, is chosen as the simplified wind model.

A wind Radii of 13.9 m/s, 24.5 m/s, and 32.7 m/s forecast method for tropical cyclone in the Northwest Pacific Ocean and the south China sea based on subjective track intensity forecast and parametric wind field model (named as STI-RF-G) is developed. Here the Georgiou wind field model, which can reflect the equilibrium equation of the tropical cyclone vortex kinematics, is chosen as the simplified wind model.

The TC track correction algorithm based on the depth series model (conv-lstm) (Figure 1) has been preliminarily developed, and the deep learning method based on ECMWF ensemble prediction has been designed. The learning data uses the typhoon prediction path of 51 members of EC ensemble prediction and 52 models of deterministic prediction, as well as the real-time typhoon positioning of the National Meteorological Centre. Based on the 6-hour prediction error feedback mechanism, the typhoon track AI prediction model (AI-TYTEC) is established.

Intensity and size are principal attributes of tropical cyclones for these largely determine the tropical cyclone deaths and economic damages. The DeepTCNet product and supporting real-time guidance are designed to offer quick, objective and skillful estimations of TC intensity and size from satellites. It is not intended to substitute for the official guidance from TC warning centers, but rather to provide the community and forecasters supplemental references. The DeepTCNet intensity data are provided beginning from storm formation to dissipation, and the size will be provided when the storm