Removal of bird-contaminated wind profiler data based on neural networks

This paper presents the results of a study that relied on trainable neural network classifiers to identify and remove bird-contaminated data from wind measurements recorded by a 1290-MHz wind profiler. A wind profiler is a Doppler radar system measuring the three-dimensional wind field. Migrating birds crossing the radar beam can lead to erroneous wind observations. Bird removal was performed by training conventional feedforward neural networks (FFNNs) and quantum neural networks (QNNs) to identify and remove bird-contaminated data recorded by a 1290-MHz wind profiler. A series of experiments evaluated several sets of input features extracted from wind profiler data, various FFNNs and QNNs of different sizes, and criteria employed for identifying birds in wind profiler data.