Ground Clutter Filtering in Reflectivity-Only Weather Radar Remote Sensing Imagery: Event-Grouped Learning with Auxiliary Gauge Validation

Ground clutter persists as residual stationary or recurrent echo in gridded reflectivity-only weather radar products, then is converted into spurious light rainfall when reflectivity is mapped to rain rate. This study presents an image-level quality control filter for Météo-France MeteoNet composites from northwestern France. Trained annotators labeled 346,629 pixels from clear-air and weather windows; complete events were withheld from training during validation. Intensity, spatial, temporal, and object features were combined in a random forest (RF) that separated clutter from weather echo on unseen events, reaching F1 = 0.833 with weather echo preservation of 0.952 and clutter suppression of 0.763. Choosing a 95% preservation operating point from training events alone maintained 0.953 preservation on outer test events; a gradient-boosted decision tree model achieved comparable discrimination. Rain gauges supported period screening, station-level recurrence review, and paired 30-min quantitative precipitation estimation (QPE). At nine reviewed recurrent-echo stations, targeted RF gating reduced false alarm ratio from 0.277 to 0.211 and mean absolute error from 0.0549 to 0.0526 mm by filtering the light rain echoes that produced false precipitation, while retaining most genuine rainfall. A mixed-echo analysis preserved 0.961 of overlapping labeled pixels, and a model trained only on 2016 data transferred to a 2017 test day with reduced error at the same stations. The resulting framework filters false precipitation effectively at locations with recurrent clutter, providing an auditable event-grouped workflow for quality control of reflectivity-only radar imagery.