A new data processing methodology, based on the statistical analysis of ground-clutter\udechoes and aimed at investigating the stability of the weather radar relative calibration, is\udpresented. A Bayesian classification scheme has been used to identify meteorological and/\udor ground-clutter echoes. The outcome is evaluated on a training dataset using statistical\udscore indexes through the comparison with a deterministic clutter map. After discriminating\udthe ground clutter areas, we have focused on the spatial analysis of robust and stable returns\udby using an automated region-merging algorithm. The temporal series of the groundclutter\udstatistical parameters, extracted from the spatial analysis and expressed in terms of\udpercentile and mean values, have been used to estimate the relative clutter calibration and\udits uncertainty for both co-polar and differential reflectivity. The proposed methodology has\udbeen applied to a dataset collected by a C-band weather radar in southern Italy.
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