Airborne Radar Quality Control with Machine Learning

Airborne Doppler radar provides detailed and targeted observations of winds and precipitation in a variety of weather systems to improve scientific understanding and weather forecasts. Quality Control (QC) is necessary to remove non-weather echos from raw radar data for subsequent analysis. The complex decision-making ability of the machine learning random forest technique is employed to create a generalized QC method for airborne radar data in convective weather systems. A manually QCed dataset was used to train the model containing data from the Electra Doppler Radar (ELDORA) in mature and developing tropical cyclones, a tornadic supercell, and a bow echo. Successful classification of ~ 96% and ~ 93% of weather and non-weather radar gates, respectively, in withheld testing data indicate the generalizability of the method. Dual-Doppler analysis using data from the genesis phase of Hurricane Ophelia (2005) not previously seen by the model produced a comparable wind field to that from manual QC. The framework demonstrates a proof of concept which can be applied to newer airborne Doppler radars.