A Machine Learning Framework for Predicting Microphysical Properties of Ice Crystals From Cloud Particle Imagery

Abstract The microphysical properties of ice crystals are important because they significantly alter the radiative properties and spatiotemporal distributions of clouds, which in turn strongly affect Earth's climate. However, it is challenging to measure key properties of ice crystals, such as mass or morphological features. Here, we present a proof‐of‐concept framework for predicting three‐dimensional (3D) microphysical properties of ice crystals from in situ two‐dimensional (2D) imagery. First, we computationally generated synthetic ice crystals using 3D modeling software along with geometric parameters estimated from the 2021 Ice Cryo‐Encapsulation Balloon (ICEBall) field campaign. Then, we used synthetic crystals to train machine learning (ML) models to predict effective density , effective surface area , and number of bullets from synthetic rosette imagery. On unseen synthetic images, our ML models accurately predicted ice crystal properties. ResNet‐18 performed best, achieving values of 0.99 and 0.98 for and , respectively, and MAE of 0.10 for in single view tasks. Stereo view ResNet‐18 further reduced RMSE by 40% for and and reduced MAE by 0.08 for . This work provides a novel ML‐driven framework for estimating ice microphysical properties from in situ imagery, which will allow for downstream constraints on microphysical parameterizations, such as the mass‐size relationship.