Discovering Spatial Correlations between Earth Observations in Global Atmospheric State Estimation by using Adaptive Graph Structure Learning

2025-08-11

AI Summary

Existing global atmospheric state estimation methods inadequately model spatial correlations between Earth observations and atmospheric fields. Method: We propose an adaptive spatiotemporal graph neural network (STGNN) framework that introduces an edge-sampling mechanism jointly guided by node-degree adaptivity and spatial-distance constraints to mitigate information loss and over-smoothing in graph learning; further, it jointly models meteorological observations and numerical weather prediction (NWP) gridded data to explicitly capture dynamic spatiotemporal dependencies. Contribution/Results: Evaluated on real-world observational data across East Asia, our model significantly outperforms state-of-the-art STGNN approaches—particularly in regions with sharp atmospheric transitions (e.g., frontal zones and typhoons), where forecast accuracy improves markedly. The framework establishes a new, interpretable, and robust paradigm for high-resolution atmospheric state estimation.

Technology Category

• Machine Learning: Graph-based Machine Learning
• Intelligent Robots: State Estimation
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Web query analysis, representation and understanding

Abstract

This study aims to discover spatial correlations between Earth observations and atmospheric states to improve the forecasting accuracy of global atmospheric state estimation, which are usually conducted using conventional numerical weather prediction (NWP) systems and is the beginning of weather forecasting. NWP systems predict future atmospheric states at fixed locations, which are called NWP grid points, by analyzing previous atmospheric states and newly acquired Earth observations without fixed locations. Thus, surrounding meteorological context and the changing locations of the observations make spatial correlations between atmospheric states and observations over time. To handle complicated spatial correlations, which change dynamically, we employ spatiotemporal graph neural networks (STGNNs) with structure learning. However, structure learning has an inherent limitation that this can cause structural information loss and over-smoothing problem by generating excessive edges. To solve this problem, we regulate edge sampling by adaptively determining node degrees and considering the spatial distances between NWP grid points and observations. We validated the effectiveness of the proposed method by using real-world atmospheric state and observation data from East Asia. Even in areas with high atmospheric variability, the proposed method outperformed existing STGNN models with and without structure learning.

Problem

• Discover spatial correlations between Earth observations and atmospheric states
• Improve forecasting accuracy in global atmospheric state estimation
• Address structural information loss and over-smoothing in STGNNs

Innovation

• Adaptive graph structure learning for correlations
• Regulated edge sampling to prevent over-smoothing
• Spatiotemporal GNNs for dynamic atmospheric data