Classification of different physical scatterers in weather radar data using machine learning techniques

The present study classifies echoes from meteorological and biological targets using dual-polarization Doppler weather radar data from the Next Generation Weather Radar (NEXRAD). Preliminary results are presented using six key variables namely, Base reflectivity, Base velocity, Spectrum width, Differential reflectivity, Correlation Coefficient, and Differential Phase. A threshold-based filtering methodology was implemented for biological scatterers and heavy precipitation events. To automate the classification, machine learning algorithms were implemented. Multiple machine learning algorithms were implemented and fine-tuned for the highest classification accuracy. Through the integration of machine learning techniques with dual-polarization Doppler weather radar data, this research endeavors to contribute to the development of robust models capable of distinguishing multiple types of physical scatterers from each other.