The S-band WSR-88D weather radar is sensitive enough to observe biological scatterers like birds and insects. However, their non-spherical shapes and frequent collocation in the radar resolution volume create challenges in identifying their echoes. We propose a method of extracting bird (or insect) features by coherently averaging dual polarization measurements from multiple radar scans, containing bird (insect) migration. Additional features are also computed to capture aspect and range dependence, and the variation of these echoes over local regions.

Next, ridge classifier and decision tree machine learning algorithms are trained, first only 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 from the test data. Further studies on differ- ent patterns of birds/insects, including roosting birds, bird migration and insect migration cases, are used to investigate the generality of our models. Overall, the ridge classifier using only dual polarization variables was found to perform con- sistently well across all these tests. Hereafter, this model is called the Bird-Insect Ridge Classifier (BIRC).

Enhancements of the Velocity Azimuth Display (VAD) Wind Profile (VWP) are explored by integrating BIRC to generate three new VAD products namely: the insects-birds ratio, VAD focused on birds and insects focused VADs. Two mains findings are drawn from experiments on these products. First, the insects-birds ratio is found to have an inverse relationship to wind biases, confirming bird contamination as a cause of the latter. Second, VAD wind biases can be reduced by focusing VAD on insects instead of all biological echoes.

It is recommended that BIRC can be used on the WSR-88D, for classifying biological echoes from the HCA as birds or insects. Furthermore, the insects-birds ratio, bird VAD and insect VAD products can be incorporated into the VWP. To the best of our knowledge, this is the first machine learning classifier that has been demonstrated to simultaneously classify diverse patterns of bird and insect echoes, and improve clear-air VAD wind estimation by incorporating taxonomic information.