Improving Quantitative Precipitation Estimation Using Adaptive Z – R Relationship Adjustment: Merging Weather Radar With Commercial Microwave Links

Meteorological operations rely heavily on quantitative precipitation estimation (QPE) derived from weather radars, calibrated against ground-based rain gauges. However, the limitations of rain gauge networks, namely their low spatial representativeness and often sparse and inhomogeneous distribution, pose challenges. Against this backdrop, commercial microwave links (CMLs), which opportunistically offer denser rainfall observations, emerge as viable alternatives for fusion with radar data, with particular potential in economically underdeveloped developing countries. This article introduces an adaptive data fusion method that dynamically calibrates the relationship between radar reflectivity factor (Z) and rain rate (R) to adapt the variations in weather systems, leveraging attenuation data from CMLs and radar reflectivity factor data. The experimental study was conducted based on data from 47 CMLs and one S-band weather radar in Jiangyin City, alongside eight rain gauges used for validation purposes. The results of the proposed method agree well with the reference measurements and reach a Pearson’s correlation coefficient of 0.65. Furthermore, it improves the coefficient of variation and root-mean-square error by 17% (24%) and 15% (27%), respectively, over the results from the empirical Z–R relationship (CMLs-based radar mean-field bias adjustment method). This study not only validates the method’s efficacy in delivering precise quantitative precipitation estimates but also delves into the influence of weather system instability and observation time on its performance.