Detecting birds and insects in the atmosphere using machine learning on NEXRAD radar echoes.

NEXRAD radars detect biological scatterers in the atmosphere, i.e., birds and insects, without distinguishing between them. A method is proposed to discriminate bird and insect echoes. Multiple scans are collected for mass migration of birds (insects) and coherently averaged along their different aspects to improve the data quality. Additional features are also computed to capture the dependence of bird (insect) echoes on their aspect, range, and spatial locality. Next, ridge classifier and decision tree machine learning algorithms are trained on the collected data. For each method, classifiers are trained, first with the averaged dual pol inputs and then different combinations of the remaining features are added. The performance of all models for both methods, are analyzed using metrics computed on a held-out test data set. Further case studies on roosting birds, bird migration and insect migration cases, are conducted to investigate the performance of the classifiers when applied to new scenarios. Overall, the ridge classifier using only dual polarization variables was found to perform consistently well on both the test data and in the case studies. This classifier is recommended for operational use on the US Next-Generation Radars (NEXRAD) in conjunction with the existing Hydrometeor Classification Algorithm (HCA). The HCA would be used first to separate biological from non-biological echoes, then the ridge classifier could be applied to categorize biological echoes into birds and insects. To the best of our knowledge, this study is the first to train a machine learning classifier that can detect diverse patterns of bird and insect echoes, based on dual polarization variables at each range gate.