Inpainting Radar Missing Data via Denoising Iterative Restoration

The issue of missing radar data has long presented a challenge for meteorologists. Meteorological radars often suffer from data gaps and poor quality due to various factors, such as random partial beam blockage (PBB), instrument malfunctions, or irregular blind spots in detection locations. Therefore, it is essential that low-quality weather radar observations be remedied. Traditional deep learning methods are not well suited to this task, as they are incompatible with pixel-level remote sensing data and struggle to identify discrete features, leading to inaccurate repairs in areas of high reflectivity. In light of the aforementioned challenges, in this article, we propose a method for radar data restoration using a noise-guided diffusion probability model driven by known information. The aim of this approach is to effectively address missing and poor-quality radar data in various scenarios. We performed experiments in the central region of the United States using seamlessly blended scanning reflectivity radar data provided by the National Oceanic and Atmospheric Administration. We simulated two data gap types: random PBB and short radar outage periods. We tested our method on data from different years and compared its performance to those of other deep learning models under the same missing data conditions. The experimental results indicate that, compared with other methods, our method improves the inpainting accuracy index by approximately 30% and the inpainting quality index by approximately 10% on the missing inpainting of outage. Moreover, it shows excellent inpainting effects on beam blockage, highlighting its considerable potential to improve quantitative applications and various tasks under inclement weather conditions.