Abstract:Polarimetric technology gradually becomes widespread in operational weather radars in China. In addition to conventional radar parameters such as reflectivity factor, Doppler velocity, and spectrum width, polarimetric weather radars also obtain dual-polarisation parameters such as differential reflectivity, differential phase, and correlation coefficient, which further expand the application range of weather radar data. Compared to conventional radar parameters, dual-polarisation parameters are more susceptible to ground clutter, requiring higher performance from ground clutter suppression algorithms. Operational weather radars currently use CMD (Clutter Mitigation Decision) and GMAP (Gaussian Model Adaptive Processing) algorithms for ground clutter identification and filtering. However, existing research indicates that these operational algorithms have insufficient ground clutter suppression capabilities, resulting in lower data quality for dual-polarisation parameters such as correlation coefficients, which affects the performance of radar products such as hydrometeor classification and melting layer identification. This paper proposes a ground clutter suppression algorithm for polarimetric weather radars, improving the existing operational algorithms in terms of ground clutter identification and filtering. For ground clutter identification, the CMD algorithm is initially executed. Based on its preliminary identification results, additional steps such as the correlation coefficient test are added for secondary identification of ground clutter, improving identification performance under low clutter-to-signal ratio conditions. For ground clutter filtering, a time-domain regression filter is used to replace the frequency-domain GMAP algorithm, avoiding the sampling information loss caused by windowing in GMAP. This ensures that ground clutter suppression no longer affects the accuracy of parameter estimation. The algorithm’s ground clutter suppression performance is evaluated based on eight precipitation events (one snowfall and seven rainfalls) observed by the S-band standard weather radar at the Changsha Meteorological Radar Calibration Centre. To quantify the performance of the ground clutter suppression algorithm, a data quality assessment metric based on correlation coefficient, differential reflectivity texture, and differential phase texture is proposed, named High-Quality Polarimetric Data Ratio (HPR). This metric characterises the extent to which dual-polarisation parameter data quality is affected by ground clutter. It is defined as the ratio of the number of range bins where the correlation coefficient, differential reflectivity texture, and differential phase texture meet specific thresholds to the total number of effective range bins, under conditions where meteorological echoes cover ground clutter within a certain distance from the radar. Evaluation results indicate that the algorithm significantly improves ground clutter suppression under low clutter-to-signal ratio conditions compared to existing operational algorithms, with an HPR value increase of about 0.4. This means that the usability of dual-polarisation parameters within a 100 km range of the radar increases by approximately 40%. Next, a more in-depth evaluation of the algorithm continues, along with the initiation of its trial operation in practical applications.
