Station2Radar: 基于查询条件的高斯散射用于降水场
Station2Radar: query conditioned gaussian splatting for precipitation field
- Korea Advanced Institute of Science and Technology (KAIST)（韩国科学技术院）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
Station2Radar通过融合自动天气站观测与卫星图像，实现高效、灵活的实时降水场生成，提升降水预报精度。
AI中文摘要：
降水预报依赖于异质数据。天气雷达准确，但覆盖范围受地理限制且维护成本高。天气站提供准确但稀疏的点测量，而卫星提供密集、高分辨率的覆盖，但无法直接获取降雨量。为克服这些限制，我们提出了查询条件高斯散射（QCGS）框架，这是首个将自动天气站（AWS）观测与卫星图像融合以生成降水场的框架。与传统2D高斯散射不同，后者渲染整个图像平面，而QCGS仅选择性地渲染查询降水区域，在非降水区域避免不必要的计算，同时保留降水结构的锐利度。该框架结合了一个雷达点提案网络，用于识别降雨支持位置，以及一个隐式神经表示（INR）网络，用于预测每个点的高斯参数。QCGS能够在实时中高效、灵活地生成降水场。通过与基准降水产品进行广泛评估，QCGS在RMSE上比传统栅格化降水产品提高了超过50%，并在多种时空尺度上保持高性能。
英文摘要：
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.
