Enhancing Weather Services Through Remote Sensing and Converged Media Visualization

Remote sensing data combined with converged media platforms are transforming weather services, though challenges like data heterogeneity and terminal diversity persist. This paper proposes a dual-layer visualization framework: the lower layer uses multi-source registration, intelligent slicing, and GPU rendering to generate high-resolution rasters quickly; the upper layer employs adaptive components to ensure consistent cross-platform delivery of synchronized graphics, text, and audio. To enhance visual clarity, a lightweight rendering pipeline with knowledge distillation sharpens cloud, dust, and rainfall boundary depictions. Built-in plugins support automated disaster map generation and API-level customization, reducing collaboration costs and speeding up product updates. Experiments confirm the framework's efficiency and user-friendliness, while paving the way for future enhancements through CubeSat networks, WebGPU acceleration, and semantic reasoning—offering a scalable model for smart meteorology and media convergence.