用气象站+卫星数据，实时生成高精度降雨分布图。
Station2Radar: query conditioned gaussian splatting for precipitation field
- 根据查询区域选择性渲染，只处理有雨区域
- 相比传统方法RMSE降低50%以上
- 适合需要实时、高分辨率降雨预测的场景
降水预报依赖多源异构数据。气象雷达精度高但覆盖范围有限且维护成本高；气象站提供精确但稀疏的点状观测；卫星虽覆盖密集、分辨率高，却无法直接反演降水量。为此，我们提出查询条件高斯点绘（QCGS），首个融合自动气象站（AWS）观测与卫星影像生成降水场的框架。不同于传统2D高斯点绘整体渲染图像平面，QCGS仅对查询的降水区域进行渲染，避免非降水区的无效计算，同时保持降水结构清晰。该框架结合雷达点提议网络识别可能降雨位置，以及隐式神经表示（INR）网络预测每个点的高斯参数。实验表明，通过与基准降水产品对比，QCGS在多个时空尺度上均实现超过50%的RMSE性能提升，支持高效、可变分辨率的实时降水场生成。
原文摘要 · Abstract (English)
Precipitation forecasting relies on heterogeneous data. Weather radar is accurate, but coverage is geographically limited and costly to maintain. Weather stations provide accurate but sparse point measurements, while satellites offer dense, high-resolution coverage without direct rainfall retrieval. To overcome these limitations, we propose Query-Conditioned Gaussian Splatting (QCGS), the first framework to fuse automatic weather station (AWS) observations with satellite imagery for generating precipitation fields. Unlike conventional 2D Gaussian splatting, which renders the entire image plane, QCGS selectively renders only queried precipitation regions, avoiding unnecessary computation in non-precipitating areas while preserving sharp precipitation structures. The framework combines a radar point proposal network that identifies rainfall-support locations with an implicit neural representation (INR) network that predicts Gaussian parameters for each point. QCGS enables efficient, resolution-flexible precipitation field generation in real time. Through extensive evaluation with benchmark precipitation products, QCGS demonstrates over 50\% improvement in RMSE compared to conventional gridded precipitation products, and consistently maintains high performance across multiple spatiotemporal scales.
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