{"id":{"repo_id":"colostate","oai_identifier":"oai:mountainscholar.org:10217/242791"},"canonical_url":"https://search.dev.ndltd.org/etd/colostate/oai:mountainscholar.org:10217/242791","repository":{"repo_id":"colostate","name":"Colorado State University","base_url":"https://api.mountainscholar.org/server/oai/request"},"display":{"title":"Three-dimensional radiative transfer with machine learning: emulation and insights from aerosol observations","abstract":"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.","abstract_html":"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.","abstract_has_math":false,"creators":["Yang, Chen-Kuang, author","Chiu, Christine, advisor","Kummerow, Christian D., committee member","Miller, Steven D., committee member","Ebert-Uphoff, Imme, committee member"],"institution":"Colorado State University. 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For information about copyright law, please see https://libguides.colostate.edu/copyright."],"rights_urls":[],"identifier_entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://doi.org/10.25675/3.025683"],"render_values":[{"text":"https://doi.org/10.25675/3.025683","href":"https://doi.org/10.25675/3.025683","code":true}]}]},"links":{"outbound_url":"https://hdl.handle.net/10217/242791","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"source_record":{"url":"https://api.mountainscholar.org/server/oai/request?verb=GetRecord&metadataPrefix=dim&identifier=oai%3Amountainscholar.org%3A10217%2F242791","prefix":"dim"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:creator","label":"Author","values":["Yang, Chen-Kuang, author","Chiu, Christine, advisor","Kummerow, Christian D., committee member","Miller, Steven D., committee member","Ebert-Uphoff, Imme, committee member"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2026-01-12T11:29:42Z"]},{"key":"dc:date.issued","label":"Date","values":["2025"]},{"key":"dc:publisher","label":"Institution","values":["Colorado State University. 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User is responsible for compliance with all applicable laws. For information about copyright law, please see https://libguides.colostate.edu/copyright."]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["Yang_colostate_0053A_19376.pdf"]},{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://hdl.handle.net/10217/242791","https://doi.org/10.25675/3.025683"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["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."]},{"key":"dc:format.medium","label":"Dc Format Medium","values":["born digital","doctoral dissertations"]},{"key":"dc:title","label":"Title","values":["Three-dimensional radiative transfer with machine learning: emulation and insights from aerosol observations"]}]}],"canonical_facts":{"dc:creator":["Yang, Chen-Kuang, author","Chiu, Christine, advisor","Kummerow, Christian D., committee member","Miller, Steven D., committee member","Ebert-Uphoff, Imme, committee member"],"dc:date.accessioned":["2026-01-12T11:29:42Z"],"dc:date.issued":["2025"],"dc:description.abstract":["Radiation plays a central role in the Earth system, governing the distribution of energy and driving key processes in weather and climate. 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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."],"dc:format.medium":["born digital","doctoral dissertations"],"dc:identifier":["Yang_colostate_0053A_19376.pdf"],"dc:identifier.uri":["https://hdl.handle.net/10217/242791","https://doi.org/10.25675/3.025683"],"dc:language":["English"],"dc:language.iso":["eng"],"dc:publisher":["Colorado State University. Libraries"],"dc:rights":["Copyright and other restrictions may apply. User is responsible for compliance with all applicable laws. 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