Atmospheric simulation data present richer information in terms of spatiotemporal resolution, spatial dimension, and the number of physical quantities compared to observational data; however, such simulations do not perfectly correspond to the real atmospheric conditions. Additionally, extensive simulation data aids machine learning-based image classification in atmospheric science. In this study, we applied a machine learning model for tropical cyclone detection, which was trained using both simulation and satellite observation data. Consequently, the classification performance was significantly lower than that obtained with the application of simulation data. Owing to the large gap between the simulation and observation data, the classification model could not be practically trained only on the simulation data. Thus, the representation capability of the simulation data must be analyzed and integrated into the observation data for application in real problems.
