Tropical Cyclone Information Extraction and Forecast Based on Satellite Infrared Images and Deep Learning Technology

Tropical cyclones are intense weather phenomena that originate over tropical oceans, posing significant threats to human life and property safety. This paper introduces methods for extracting and forecasting tropical cyclone information based on deep learning and satellite infrared images. It includes tropical cyclone wind radii estimation (global), tropical cyclone center location (Northwest Pacific), and tropical cyclone intensity forecast (Northwest Pacific). Utilizing infrared images and ERA5 reanalysis data, datasets for tropical cyclone wind radii estimation from 2004 to 2016, tropical cyclone center location from 2015 to 2018, and 24-hour tropical cyclone intensity forecasts from 1979 to 2021 have been constructed. Firstly, the DL-TCR model with an asymmetric branch is designed to infer the asymmetric tropical cyclone wind radii (R34, R50 and R64) of global tropical cyclones. A modified MAE-weighted loss function is introduced to enhance the model's underestimation of large-sized tropical cyclone wind radii. The results indicate that the DL-TCR model achieves MAEs for R34 wind radii of 18.8, 19.5, 18.6, and 18.8 n mi in the NE, SE, SW, and NW quadrants, respectively. For R50 wind radii, the MAEs are 11.3, 11.3, 11.1, and 10.8 n mi, and for R64 wind radii, the MAEs are 8.9, 9.9, 9.2, and 8.7 n mi. These values represent an improvement of 12.1-35.5% compared to existing methods. Then, employing transfer learning by transferring pre-trained models based on the ImageNet natural image dataset significantly improved the precision of tropical cyclone center location models. The results demonstrate that the transfer-learning-based model enhances the location accuracy by 14.1% compared to models without transfer learning. The location error for the tropical cyclone centers in the test data is 29.3 km, and for H2-H5 category, the tropical cyclone center location error is less than 20 km. Finally, a deep learning model, named the TCIF-fusion model, was developed with two distinct branches engineered to learn multi-factor information and forecast the intensity of TCs over a 24-hour period. Ultimately, heatmaps were generated to capture the model's insights, which were then utilized to augment the original input data, leading to an improved dataset that significantly enhanced the accuracy of the TC intensity forecasting. Utilizing the refined input, the heatmaps (referred to as model knowledge, MK) were employed to direct the modeling process of the TCIF-fusion model. Consequently, the model guided by MK achieved a 24-hour forecast error of 3.56 m/s for Northwest Pacific TCs during the period from 2020 to 2021. The MK-based TCIF-fusion model has improved the forecasting performance by 12.1-35.5% compared to existing methods. In summary, deep learning exhibits significant potential in the extraction and forecasting of tropical cyclone information, positioning it as a crucial tool for future tropical cyclone monitoring and forecasting.