An efficient methodology for detection of anomalous propagation echoes in radar reflectivity data using neural networks

To detect anomalous propagation echoes in radar data. an automated procedur e based on a neural network classification scheme has been developed. Earli er results had indicated that algorithms used to detect anomalous propagati on must be calibrated before they can be applied to new sites. Developing a calibration dataset is typically laborious as it involves a human expert. To eliminate this problem, an efficient methodology of calibrating and vali dating neural network-based detection is proposed. Using volume scan radar reflectivity data from two WSR-88D locations, the authors demonstrate that the procedure can be calibrated easily and applied successfully to differen t sires.