Reconstructing the Aerosol State from Partial Observations with Generative Modeling

TL;DR

This paper introduces a generative modeling approach to infer comprehensive aerosol properties from partial measurements, providing uncertainty estimates and identifying key observational inputs for different diagnostics.

Contribution

It develops a novel conditional generative framework that maps partial aerosol observations to plausible states, improving diagnostic constraints and uncertainty quantification.

Findings

Generated aerosol samples adhere well to labels.

Higher-dimensional observations reduce variability in estimates.

Certain diagnostics are well constrained even with limited data.

Abstract

Key aerosol properties that shape climate -- such as CCN activity, scattering and absorption, and ice nucleation efficiency -- are difficult to infer from measurements that typically capture only a part of the aerosol state. We develop a conditional generative framework that maps a label (a vector of partial observations) to an ensemble of plausible aerosol states and propagates these to diagnostics, yielding mean estimates with confidence intervals. Using synthetic data, we evaluate two label configurations: a low-dimensional setup with limited number distribution and bulk-composition information, and a high-dimensional setup including complete number and total mass distributions plus species bulk masses. Generated samples maintain strong label compliance, and higher-dimensional labels markedly reduce variability. CCN activity and volume scattering are well constrained even under the low-dimensional setup, whereas dust- and BC-sensitive diagnostics (frozen fraction, absorption) benefit substantially from the additional information in the high-dimensional case. This framework clarifies which observational inputs most effectively constrain different diagnostics and demonstrates how generative machine learning can provide uncertainty-aware estimates from incomplete aerosol information.