Three-dimensional radiative transfer with machine learning: emulation and insights from aerosol observations

Radiation plays a central role in the Earth system, governing the distribution of energy and driving key processes in weather and climate. Atmospheric radiative transfer (RT), which describes the physical processes governing the propagation and interaction of radiation in Earth's atmosphere, can be modeled with high accuracy using sophisticated mathematical formulations under well-defined assumptions. However, such modeling is computationally intensive due to the multidimensional nature of the problem (e.g., spatial and angular dependencies) and the complexity of the underlying physics, including multiple scattering, spectral absorption, and emission. Consequently, RT calculations remain a major computational bottleneck in atmospheric modeling, limiting the use of more advanced and physically realistic radiative schemes. Machine learning, which can efficiently approximate complex, nonlinear relationships in high-dimensional spaces without explicitly solving the computationally intensive governing equations, offers a promising pathway to overcoming this limitation. In this dissertation, we leverage machine learning techniques to advance research topics related to three-dimensional (3D) radiative transfer (RT). In the first part, we present newly developed 3D RT emulators for shallow cumulus cloud fields. These emulators provide downwelling surface radiation and full 3D atmospheric heating rates at a horizontal resolution of 100 m and a vertical resolution of 30 m. Their design is physically guided, and performance is evaluated in terms of both accuracy and computational efficiency. In the second part, we apply a machine learning–based aerosol retrieval method developed by Yang et al. (2022), which accounts for 3D cloud radiative effects to enable accurate near-cloud retrievals, to investigate aerosol–cloud–radiation interactions using passive satellite observations. Specifically, we examine aerosol properties and their shortwave (SW) direct radiative effects (DRE) under four distinct cloud organizations—Sugar, Gravel, Fish, and Flowers—over the trade-wind regimes. Differences in hydration-induced enhancement of the aerosol DRE among these organizations are quantified and interpreted in terms of the moisture contrast between cloudy and clear skies, as well as the spatial distribution of clear-sky regions relative to the nearest clouds.

Name thesis:degree_name

Doctor of Philosophy (Ph.D.)

Level thesis:degree_level

Doctoral

Discipline thesis:degree_discipline

Atmospheric Science

Grantor dc:publisher

Colorado State University. Libraries

Year dc:date.issued

2025

Authors dc:creator

• Yang, Chen-Kuang, author
• Chiu, Christine, advisor
• Kummerow, Christian D., committee member
• Miller, Steven D., committee member
• Ebert-Uphoff, Imme, committee member

• machine learning
• remote sensing
• radiative transfer
• aerosol-cloud-radiation interactions

Statement dc:rights

• Copyright and other restrictions may apply. User is responsible for compliance with all applicable laws. For information about copyright law, please see https://libguides.colostate.edu/copyright.

Language dc:language.iso

eng, English

Identifier URI

https://doi.org/10.25675/3.025683

OAI record oai:identifier

https://api.mountainscholar.org/server/oai/request?verb=GetRecord&metadataPrefix=dim&identifier=oai%3Amountainscholar.org%3A10217%2F242791