Deep learning-based tropical cyclone intensity prediction through synergistic fusion of geostationary satellite and numerical prediction model

The accurate forecasting of the intensity of tropical cyclones (TCs) is able to effectively reduce the overall costs of disaster management. In this study, we proposed a deep learning-based model for TC forecasting with the lead time of 24, 48, and72 hours following the event, based on the fusion of geostationary satellite images and numerical forecast model output. A total of 268 TCs which developed in the Northwest Pacific from 2011 to 2019 were used in this study. The Communications system, the Ocean and Meteorological Satellite (COMS) Meteorological Imager (MI) data were used to extract the images of TCs, and the Climate Forecast System version 2 (CFSv2) provided by the National Center of Environmental Prediction (NCEP) was employed to extract atmosphere and ocean forecasting data. In this study, we suggested hybrid convolutional neural network (hybrid-CNN)-based TC forecasting models. It enables to efficiently consider not only the physical but also the spatial characteristics of variables. The Joint Typhoon Warning Center (JTWC) was used for validating the suggested model, and Korea Meteorological Administrator (KMA)-based operational TC predictions were utilized for evaluating the performance of the model. A hybrid-CNN-based prediction model obtained mean absolute errors (MAE) of 13.58, 16.48, and 21.64 kts and skill scores (SS) of 29%, 19%, and 1.6% for 24h, 48h, and 72h forecasts, respectively. Since the rapid intensification (RI) is one of the challenging tasks in the TC intensity prediction, the performance of suggested model for all RIs in 2019 were additionally evaluated. Compared to KMA-based predictions, the suggested models achieved average SS of 66%. Furthermore, using an explainable artificial intelligence (XAI) approach, it is possible to verify how the suggested model works for forecasting TC intensity and propose the feasibility of the suggested model in the meteorology field.