Forecasting Precision: The Role of Graph Neural Networks and Dynamic GNNs in Weather Prediction

Abstract

Weather forecasting is essential for addressing global climate change concerns, relying on the analysis of multivariate data from diverse meteorological sensors. These sensors include ground-based, radiosonde, and satellite-mounted sensors, providing a comprehensive understanding of atmospheric conditions. To analyze this data effectively, a weather forecasting model based on Graph Neural Networks (GNNs) is employed. GNNs, built on graph-based learning principles, have shown significant empirical efficacy in various machine learning paradigms. In this study, we evaluate the weather forecasting performance of GNNs in comparison to traditional machine learning models. By leveraging the capabilities of GNNs, which excel in capturing complex relationships within data, we aim to enhance the accuracy and reliability of weather predictions. This research contributes to advancing the field of weather forecasting by exploring novel methodologies that can potentially improve prediction accuracy and provide more robust insights into climate dynamics. Additionally, by assessing the performance of GNNs against conventional models, we gain valuable insights into the strengths and limitations of different forecasting approaches. Ultimately, the findings of this study have implications for improving weather forecasting techniques, which are crucial for mitigating the impacts of climate change and enhancing resilience to extreme weather events on a global scale.