基于TimeSformer模型的台风强度实时预报OA

This study proposes a real-time 24-hour typhoon intensity forecasting model based on the TimeSformer architecture,which is capable of effectively extracting spatiotemporal features from meteorological fields.The model was trained on ERA5 reanalysis data(1990-2021)and validated using both ERS5 test data and real-time ECMWF forecast data.To enhance model performance,a track representation layer was incorporated into the input data,which reduced the Mean Square Error(MSE)on the test set by 1.84 m2·s-2 compared to the control experiment.Additionally,sensitivity experiments on upper-level isobaric elements reveals that optimal performance was achieved using only the five pressure levels(200 hPa,500 hPa,700 hPa,850 hPa,and 950 hPa),with a mean absolute error(MAE)of 3.24 m·s-1 on 623 ERA5 test samples from 2022-2023.When applied to real-time ECMWF forecast data(260 test samples during 2022-2023),the model achieved an MAE of 3.56 m·s-1,a slight increase compared to direct ERA5-based validation(with an MAE increase of 0.32 m·s-1),but still substantially lower than those errors from the two mainstream operational NCEP(4.52 m·s-1)and ECMWF(8.11 m·s-1)models,respectively.These results demonstrate that the proposed model offers a robust and reliable tool for operational typhoon intensity forecasting.