CIDM: A comprehensive inpainting diffusion model for missing weather radar data with knowledge guidance

Abstract

Addressing data gaps in meteorological radar scan regions remains a significant challenge. Existing radar data recovery methods tend to perform poorly under different types of missing data scenarios, often due to over-smoothing. The actual scenarios represented by radar data are complex and diverse, making it difficult to simulate missing data. Recent developments in generative models have yielded new solutions for the problem of missing data in complex scenarios. Here, we propose a comprehensive inpainting diffusion model (CIDM) for weather radar data, which improves the sampling approach of the original diffusion model. This method utilises prior knowledge from known regions to guide the generation of missing information. The CIDM formalises domain knowledge into generative models, treating the problem of weather radar completion as a generative task, eliminating the need for complex data preprocessing. During the inference phase, prior knowledge of known regions guides the process and incorporates domain knowledge learned by the model to generate information for missing regions, thus supporting radar data recovery in scenarios with arbitrary missing data. Experiments were conducted on various missing data scenarios using Multi-Radar/MultiSensor System data sourced from the National Oceanic and Atmospheric Administration, and the results were compared with those of traditional and deep learning radar restoration methods. Compared with these methods, the CIDM demonstrated superior recovery performance for various missing data scenarios, particularly those with extreme amounts of missing data, in which the restoration accuracy was improved by 5%–35%. These results indicate the significant potential of the CIDM for quantitative applications. The proposed method showcases the capability of generative models in creating fine-grained data for remote sensing applications.