GeoAttX: A Novel Framework for Cloud Image Forecasting Using Geostationary Satellite Observations

Cloud imagery provides critical parameters - including temperature, precipitation, and cloud properties - that are valuable for scientific research. Real-time cloud image forecasting could greatly extend the utility of such data for practical applications, yet large-scale cloud extrapolation and prediction remain challenging due to limitations in accuracy, high computational costs, and cumulative errors. To address these challenges, we introduce GeoAttX, a geographic attention network for diverse tasks, is proposed, a novel deep learning framework that integrates geographic attention mechanisms to forecast cloud movement using data from the FengYun-4B (FY-4B) geostationary meteorological satellite, which updates every 15 min. The framework employs two core algorithms: a reversed greedy algorithm (RGA), which reduces the number of autoregressive steps by reversing the order of the standard greedy approach, and secondary-order standardization (SoS), which renormalizes pixel distributions to maintain consistent brightness statistics across inputs, mitigating error accumulation and improving accuracy. GeoAttX demonstrates strong performance in cloud dynamics prediction, achieving R2 values of 0.9938, 0.9539, and 0.7489 for lead times of 15, 60, and 180 min, respectively. These results represent a substantial advance in satellite-based cloud image forecasting via deep learning, offering valuable insights for atmospheric modeling and disaster management. The source code is available at https://github.com/songzijiang/GeoAttX