Abstract:In view of the characteristics of X-band phased array radar, which offers high spatio-temporal resolution but limited observation coverage and various errors that affect the reliability of data, this study proposes a method of high-quality data fusion between X-band phased array radar and 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 6 min to 1.5 min, synchronized with X-band radar. Subsequently, the optimal interpolation algorithm and pyramid transform algorithm are employed for the spatial fusion of dual-band radar data, and the corresponding products SXnet-O and SXnet-K are obtained. Statistical analysis based on nearly 5000 radar volume scans from the Guangdong-Hong Kong-Macao Greater Bay Area radar network from May to June 2022 demonstrates that the time-matching results extrapolated by the optical flow method outperform traditional persistence forecasting in terms of forecasting scores and correlation coefficients in the temporal dimension. In the spatial dimension, the fused data effectively compensate for the detection blind zones of a single radar, increasing the low-altitude (500 m) echo coverage by approximately 2.5 times compared to the S-band 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. Compared with the single-band network data, SXnet-O greatly reduces the mean deviation from the S-band reference. Moreover, the structural precision of X-band and the observational stability of S-band get effectively integrated.