Abstract:To address the characteristics of X-band phased array radar, which offers high spatiotemporal resolution but limited observation coverage and various data reliability errors, this study proposes a high-quality data fusion method with S-band operational radar. First, the optical flow method is applied to extrapolate the motion trends of S-band radar data, enhancing its temporal resolution from the original 6 minutes to 1.5 minutes, synchronized with X-band radar. Subsequently, the optimal interpolation algorithm and pyramid transform algorithm are employed for spatial fusion of dual-band radar data, yielding the corresponding products SXnet-O and SXnet-K. Statistical analysis based on nearly 5,000 radar volume scanscases from the Guangdong-Hong Kong-Macao Greater Bay Area radar network during May–June 2022 demonstrates that the fused data achieve a 12% improvement in temporal correlation coefficient and a 28% reduction in root mean square error compared to the original X-band data. The spatial coverage reaches 96% of the union area of both radars, approximately 30% higher than that of a single radar. Comparative results indicate that SXnet-O significantly outperforms SXnet-K in system bias control (92% of deviations within ±2 dB), parameter consistency, and boundary stability. For data correction, SXnet-O reduces the deviation from the S-band reference by 5% compared to the original X-band data while effectively integrating the structural precision of X-band with the observational stability of S-band. This method provides a methodological foundation for data fusion applications in dense phased array weather radar networks.