Ice crystal images from optical array probes. Compatibility of morphology specific size distributions, retrieved with specific and global Convolutional Neural Networks for HVPS, PIP, CIP, and 2DS

Abstract. The convolutional network methodology is applied to train classification tools for hydrometeor images from optical array probes. Two models were developed in a previous article for the PIP and 2DS and are further tested. Three additional models are presented: for the CIP, HVPS, and a global model trained on a data set that includes all available data from all four instruments. A methodology to retrieve morphology-specific size distributions from the OAP data is provided. Size distributions for each morphological class, obtained with the specific or global classification models, are compared for the ICE GENESIS data set, where all four probes were used simultaneously. The reliability and coherence of these newly obtained machine learning classification tools are demonstrated clearly. The analysis shows significant advantages of using the global model over the specific ones, in terms of compatibility of the size distributions. The obtained morphology-specific size distributions effectively reduce OAP data to a level of detail pertinent to systematically identify microphysical processes. This study emphasizes the potential to improve insights in ice and mixed-phase microphysics based on hydrometeor morphological classification from machine learning algorithms.