Removal of interfering RLAN signals from C-band weather radar data

The algorithm of removing interfering RLAN signals (so called spikes) in weather radar data is implemented in the Institute of Meteorology and Water Management – National Research Institute (IMGW) as a component of the RADVOL-QC system for the radar data quality control. Eliminating the interfering signals in C-band (5 GHz) radars is important for accurate weather monitoring. The main difficulty in spike removal are their unique shapes, and the task is especially challenging while they overlap the precipitation.

The process of detecting interference caused by signals from the RLAN network is carried out by evaluating the variability of echoes along and across the beam for each bin at various elevation angles. Such echoes are considered potential spikes. For each azimuth, the number of bins containing potential spike echoes is determined. If this count exceeds the established threshold for a given azimuth, the echoes are treated as real spikes.

The spike correction process consists of analyzing each bin with detected real spike and its surroundings. The analysis extends to bins in adjacent and further azimuths on left and right until bins without detected spikes are encountered. Depending on the specific case, these echoes may be replaced with an arithmetic mean if classified as precipitation or removed entirely. While removing spikes, the analysis extends to adjacent azimuths within a range of 3 to 4 bins on either side to ensure accurate identification and removal of false echoes. This extended analysis considers potential anomalies in adjacent data that may have been overlooked during the detection process.

Examples of applied techniques are presented using the weather radar product maximum reflectivity (CMAX). The examples illustrate the enhancement of the radar data, where the extended analysis effectively eliminates RLAN interference that was not identified by the detection algorithm but falls within the analysis area. This improvement is crucial from a meteorological perspective, as high-quality radar data significantly impacts meteorological and hydrological models, leading to more accurate forecasts.