A Multi-Source Radar Data Complementary Enhancement Generation Method Based on Diffusion Model

Multi-source radar data fusion has become increasingly vital for advancing weather monitoring and forecasting. However, effectively integrating Doppler radar with an X-band phased-array radar remains challenging. Doppler radar offers only low and inconsistent spatial resolution, whereas an X-band phased-array radar provides high resolution but is limited by short detection range, severe signal attenuation, and high deployment costs, constraining its use to localized monitoring. To address the aforementioned challenges, this paper proposes the Multi-source Radar Reflectivity Complementary Enhancement method (MSR-CE). By constructing a paired training dataset, real X-band phased-array radar reflectivity data serve as the starting samples for the forward diffusion process, while paired S-band Doppler radar reflectivity data act as conditional guidance. Leveraging a conditional diffusion model, the method generates high-resolution pseudo X-band phased-array reflectivity fields. Additionally, a Radar-Physics-Aware Loss (RPA Loss) is introduced to enhance spatial detail fidelity and physical consistency. Experiments on multi-source radar observations from Northeast China in 2025 demonstrate that MSR-CE achieves an SSIM of 0.892 and a PSNR of 41.6 dB, outperforming traditional interpolation methods and state-of-the-art generative approaches in radar reflectivity enhancement.