DiffSR: Learning Radar Reflectivity Synthesis via Diffusion Model from Satellite Observations

DiffSR: Learning Radar Reflectivity Synthesis via Diffusion Model from Satellite Observations 1 st Xuming He Open Science Lab Shanghai AI Laboratory Shanghai, China hexuming773@gmail.com 2 nd Zhiwang Zhou Open Science Lab Shanghai AI Laboratory Shanghai, China zhouzhiwang@pjlab.org.cn 3 rd Wenlong Zhang null Open Science Lab Shanghai AI Laboratory Shanghai, China zhangwenlong@pjlab.org.cn 4 th Xiangyu Zhao Department of Computing Hong Kong Polytechnic University HongKong SAR, China 22123675r@connect.polyu.hk 5 th Hao Chen Open Science Lab Shanghai AI Laboratory Shanghai, China chenhao1@pjlab.org.cn Abstract --Weather radar data synthesis can fill in data for areas where ground observations are missing. Existing methods often employ reconstruction-based approaches with MSE loss to reconstruct radar data from satellite observation. However, such methods lead to over-smoothing, which hinders the generation of high-frequency details or high-value observation areas associated with convective weather . T o address this issue, we propose a two-stage diffusion-based method called DiffSR.