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

TL;DR

This paper introduces a machine learning framework that accurately predicts 3D microphysical properties of ice crystals from 2D imagery, aiding climate modeling and understanding of cloud microphysics.

Contribution

The study develops a novel ML-based approach using synthetic data to estimate ice crystal properties from imagery, including stereo views for improved accuracy.

Findings

High accuracy in predicting effective density and surface area (R^2 > 0.98)

Stereo view models significantly reduce prediction errors

Enhanced microphysical property estimation from in situ 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 framework for predicting three-dimensional (3D) microphysical properties of ice crystals from in situ two-dimensional (2D) imagery. First, we computationally generate synthetic ice crystals using 3D modeling software along with geometric parameters estimated from the 2021 Ice Cryo-Encapsulation Balloon (ICEBall) field campaign. Then, we use synthetic crystals to train machine learning (ML) models to predict effective density ($\rho_{e}$), effective surface area ($A_e$), and number of bullets ($N_b$) from synthetic rosette imagery. When tested on unseen synthetic images, we find that our ML models can predict microphysical properties with high accuracy. For $\rho_{e}$ and $A_e$, respectively, our best-performing single view models achieved $R^2$ values of 0.99 and 0.98. For $N_b$, our best single view model achieved a balanced accuracy and F1 score of 0.91. We also quantify the marginal prediction improvements from incorporating a second view. A stereo view ResNet-18 model reduced RMSE by 40% for both $\rho_e$ and $A_e$, relative to a single view ResNet-18 model. For $N_b$, we find that a stereo view ResNet-18 model improved the F1 score by 8%. 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.