It is hypothesized that "the complex multiscale interaction between environment and vortex" is the key to the changes of typhoon track, intensity and structure, and that "the changes of typhoon track, intensity and structure are internally coordinated given an environmental forcing". Based on this new theoretical assumption this subject systematically studies the challenging 3-7-day prediction of typhoons and predictability in the Northwest Pacific.

Tropical cyclones (TCs）are synotpic weather systems that form overthewarm tropical oceans. After their geneses, TCs are often affectd by complex multi-scale processes among convections, storm scale dynamics and the varying environment. Because of their complexity, 3-7 day forecastis o f TC track, structure and intensity are quite chanlenging, and thus how to imrpove the TC track, intensity and structure 3-7 day in advance is one of the difficult issues in the world.

Data assimilation plays a critical role in improving the skills of numerical weather prediction by establishing accurate initial conditions. Especially for forecasting tropical cyclones (TCs), multiscale data assimilation algorithm and ensemble sensitivity analysis, strategies combating dynamically inconsistent and imbalanced initial conditions, and effective usage of advanced observations like the satellite radiances, are investigated.

It focuses on solving the difficult 3-7 day long-term typhoon forecast problem and develops some new integrated schemes or algorithms for operational typhoon track, intensity and scale or structure forecast. The results of the project will be directly applied to operational typhoon forecasting in China, improve the ability to forecast 3-7 days of typhoons, and play an active role in national disaster prevention and reduction.

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 5~7-day ensemble forecasts for Tropical Cyclones (TC) of western North Pacific are provided using an ensemble predication system based on WRF model. The system contains 80 members with initial conditions from the WRF/EnKF ensemble perturbed members (link to “Dataset: WRF/EnKF ensemble analyses for western North Pacific”).

The continuously cycled WRF/EnKF ensemble analyses for western North Pacific products are produced using the WRF model with 57 vertical levels and model top at 10 hPa. For years 2016-2018, 18-km horizontal grid spacing is used for domain 1 with grid numbers 520 x 660, and 6-km horizontal grid spacing is used for vortex following domain 2 with grid numbers 180 x 180 when an advisory TC position is announced. Since year 2019, 12-km horizontal grid spacing has been used for a single domain with grid numbers 560x720.

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