Abstract
NEXRAD radars detect biological scatterers in the atmosphere, i.e., birds and insects, without distinguishing between them. A method is proposed to discriminate these 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 the observed aspect, range, and local regions of space. 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 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.
Highlights
The Next-Generation Radar (NEXRAD) network consists of 160+ S-band polarimetricDoppler weather radars (WSR-88D), deployed across the continental US, Alaska, Hawaii, and Puerto Rico
We propose a machine learning model that can classify diverse orientations of bird and insect echoes, by operating on individual radar range gates
Our goal was to train an algorithm for distinguishing bird from insect echoes, that goal was to train an algorithm for distinguishing birdfuzzy fromlogic insect echoes, that couldOur be implemented operationally on Next-Generation Radars (NEXRAD)
Summary
Doppler weather radars (WSR-88D), deployed across the continental US, Alaska, Hawaii, and Puerto Rico. Another machine learning system was developed in [28] that locates roosts within images and tracks them across frames These methods are useful, they are designed to detect one orientation of birds while using the entire radar image as an input. We propose a machine learning model that can classify diverse orientations of bird and insect echoes, by operating on individual radar range gates. Dual polarization radar scans containing separate large-scale bird and insect migration were collected (Section 2). Both machine learning methods are trained, first on only dual polarization variables and on different combinations of the remaining features (Section 6) Their performances are evaluated using metrics computed on test data (Section 7).
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