Enhancing Observation Point Analysis for Atmospheric State Estimation Using Self-Supervised Graph Neural Networks

Atmospheric state estimation is an important part of weather forecasting, and its accuracy determines the accuracy of the forecasting results. Traditional methods for atmospheric state estimation mainly rely on assimilation systems, using physical models and dynamic equations to predict the atmospheric state. However, these methods have certain limitations when dealing with large-scale meteorological data and complex meteorological phenomena. In order to solve the above problems, this study first integrates and processes data from multiple datasets including ground, upper-air, satellite, and atmospheric state, representing these data as graph structures. Secondly, a graph neural network-based network model is constructed, which is pre-trained using self-supervised methods and fine-tuned for specific tasks. Finally, gradient-based interpretability analysis is used to evaluate the importance of observed nodes. The experimental results show that both the atmospheric state estimation model and the interpretable analysis method proposed in this paper are superior to some existing representative models and methods.