用GAN模型从C波段SAR数据反演降雨，提升高风速下精度
Rainfall regression from C-band Synthetic Aperture Radar using Multi-Task Generative Adversarial Networks
- 设计多任务生成对抗网络，引入像素块级与对抗机制增强建模
- 在15米/秒风速下仍保持良好性能，较以往方法提升反演精度
- 利用完整NEXRAD档案库匹配哨兵1号数据，缓解数据对齐难题
本文提出一种数据驱动的方法，基于200米空间分辨率的C波段合成孔径雷达（SAR）数据估算降水率。针对以往研究中SAR与气象雷达数据存在配准偏差及强风条件下降雨样本稀缺的问题，本文采用多目标建模范式，引入局部图像块组件和对抗性损失。通过利用完整的NEXRAD雷达档案库，寻找与哨兵1号（Sentinel-1）数据潜在的时空共现位置。结合训练策略优化与额外输入信息后，所提模型在降雨估计精度上表现更优，并可有效拓展至15米/秒风速场景。
原文摘要 · Abstract (English)
This paper introduces a data-driven approach to estimate precipitation rates from Synthetic Aperture Radar (SAR) at a spatial resolution of 200 meters per pixel. It addresses previous challenges related to the collocation of SAR and weather radar data, specifically the misalignment in collocations and the scarcity of rainfall examples under strong wind. To tackle these challenges, the paper proposes a multi-objective formulation, introducing patch-level components and an adversarial component. It exploits the full NEXRAD archive to look for potential co-locations with Sentinel-1 data. With additional enhancements to the training procedure and the incorporation of additional inputs, the resulting model demonstrates improved accuracy in rainfall estimates and the ability to extend its performance to scenarios up to 15 m/s.
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