Using LiDAR Output to Identify Atmospheric Rotors: A Convolutional Neural Network Approach

The identification and characterization of atmospheric turbulence, particularly rotors, is crucial for aviation safety due to their small scale and turbulent nature as well as their difficulty to accurately predict. Traditional methods of detecting rotors rely on manual inspection and reports. These are limited in temporal coverage, requiring significant investment in training to ensure accuracy when observing. This study explores the opportunity to use machine learning methods to identify features within remote sensing data. In particular this study focusses on convolutional neural networks (CNNs), to identify rotors within Light Detection and Ranging (LiDAR) output. LiDAR technology provides high-resolution, three-dimensional wind field data, enabling detailed analysis of atmospheric phenomena. By leveraging this data annotated by field expertise, we developed a robust CNN model capable of detecting rotors with high accuracy. The model was trained on labeled rotor data, with a comprehensive hyperparameter search conducted to optimize its performance. The results indicate that the CNN models trained effectively, achieving high performance on the training dataset, though there was a tendency to overfit. Despite this, the ability to correctly classify rotor images, even with an overpredictive bias, remains valuable for operational meteorologists. This study demonstrates the potential of machine learning techniques to advance turbulence detection in the meteorological domain, ultimately contributing to safer aviation practices. This also opens the door for generating longer datasets that can then be combined with other data sources such as numerical weather prediction data allowing for increased understanding of atmospheric conditions conducive to their formation as well as potentially highlighting more common locations for formation, leading to better asset protection operations.