Classification of ice particle shapes using machine learning on forward light scattering images

Abstract

Machine Learning (ML) has rapidly transitioned from a niche activity to a mainstream tool for environmental research applications including atmospheric science cloud microphysics studies. Two recently developed cloud particle probes measure the light scattered in the near forward direction and save digital images of the scattering light. Scattering pattern images collected by the Particle Phase Discriminator (PPD-2K) and the Small Ice Detector version 3 (SID-3) provide valuable information for particle shape and size characterization. Since different particle shapes have distinctly different light scattering characteristics, the images are ideally suited for ML. Here results of a ML project to characterize ice particle shapes sampled by the PPD-2K in ice fog and diamond dust during a 3-year project in Fairbanks, Alaska. 2.15 million light scattering pattern images were collected during three years of measurements with the PPD-2K. Visual Geometry Group (VGG) Convolutional Neural Network (CNN) was trained to categorize light scattering patterns into 8 categories. Initial training images (120 each category) were selected by human visual examination of data and the training dataset was augmented using an automated iterative method for image identification of further images which were all visually inspected by a human. Results were well correlated to similar categories identified from previously developed classification algorithms. ML identify characteristics not included in automated analysis such as sublimation. Of the 2.15 million images analyzed, 1.3% were categorized as spherical (liquid), 43.5% were categorized as having rough surfaces, 15.3% were pristine, 16.3% were categorized as sublimating and the remaining 23.6% did not fit into any of those categories (irregular or saturated).