A Fuzzy-Logic-Based Approach for Eliminating Interference Lines in Micro Rain Radar (MRR-2) - Kwonil Kim; GyuWon Lee (Remote Sensing, 2024)
This research presents a novel fuzzy-logic-based algorithm aimed at detecting and removing interference lines from Micro Rain Radar (MRR-2) data. Interference lines, which are non-meteorological echoes with unknown origins, can severely obscure meteorological signals. Leveraging an understanding of interference line characteristics, such as temporal continuity, we identified and utilized eight key variables to distinguish interference lines from meteorological signals. These variables include radar moments, Doppler spectrum peaks, and the spatial/temporal continuity of Doppler velocity. The algorithm was developed and validated using data from MRR installations at three sites (Seoul, Suwon, and Incheon) in South Korea, from June to September 2021–2023. While there is a slight tendency to eliminate some weak precipitation, results indicate that the algorithm effectively removes interference lines while preserving the majority of genuine precipitation signals, even in complex scenarios where both interference and precipitation signals are present. The developed software, written in Python 3 and available as open-source, outputs in NetCDF4 format, with customizable parameters for user flexibility. This tool offers a significant contribution to the field, facilitating the accurate interpretation of MRR-2 data contaminated by interference.