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Dataset associated with "Near-Cloud Aerosol Retrieval Using Machine Learning Techniques, and Implied Direct Radiative Effects"

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dc.contributor.authorYang, C. Kevin
dc.contributor.authorChiu, Christine
dc.contributor.authorMarshak, Alexander
dc.contributor.authorFeingold, Graham
dc.contributor.authorVárnai, Tamás
dc.contributor.authorWen, Guoyong
dc.contributor.authorYamaguchi, Takanobu
dc.contributor.authorvan Leeuwen, Peter Jan
dc.date.accessioned2022-09-19T18:23:06Z
dc.date.available2022-09-19T18:23:06Z
dc.date.issued2022
dc.descriptionThe dataset contains two parts: 1) Large eddy simulation (LES) output 2) a module that performs all-clear-sky aerosol retrieval using a machine-learning-based model. There are also example data and a script to demonstrate how to use the module.en_US
dc.descriptionDepartment of Atmospheric Science
dc.description.abstractThere is a lack of satellite-based aerosol retrievals in the vicinity of low-topped clouds, mainly because reflectance from aerosols is overwhelmed by three-dimensional cloud radiative effects. To account for cloud radiative effects on reflectance observations, we develop a Convolutional Neural Network and retrieve aerosol optical depth (AOD) with 100–500 m horizontal resolution for all cloud-free regions regardless of their distances to clouds. The retrieval uncertainty is 0.01+5%AOD, and the mean bias is approximately –2%. In an application to satellite observations, aerosol hygroscopic growth due to humidification near clouds enhances AOD by 100% in regions within 1 km of cloud edges. The humidification effect leads to an overall 55% increase in the clear-sky aerosol direct radiative effect. Although this increase is based on a case study, it highlights the importance of aerosol retrievals in near-cloud regions, and the need to incorporate the humidification effect in radiative forcing estimates.en_US
dc.description.sponsorshipNASA Projects 80NSSC20K0596.en_US
dc.description.sponsorshipNASA Projects 80NSSC20K1719.en_US
dc.description.sponsorshipCooperative Institute for Research in the Atmosphere (CIRA).en_US
dc.description.sponsorshipU.S. Department of Energy, Office of Science, Atmospheric System Research Program Interagency Award 89243020SSC000055.en_US
dc.description.sponsorshipEuropean Research Council under the CUNDA project 694509.en_US
dc.format.mediumTXT
dc.format.mediumMATLAB
dc.format.mediumHDF5
dc.format.mediumPY
dc.format.mediumNetCDF
dc.identifier.urihttps://hdl.handle.net/10217/235755
dc.identifier.urihttp://dx.doi.org/10.25675/10217/235755
dc.languageEnglishen_US
dc.language.isoengen_US
dc.publisherColorado State University. Librariesen_US
dc.relation.ispartofResearch Data
dc.relation.isreferencedbyYang, C. K., Chiu, J. C., Marshak, A., Feingold, G., Várnai, T., Wen, G., et al. (2022). Near-cloud aerosol retrieval using machine learning techniques, and implied direct radiative effects. Geophysical Research Letters, 49, e2022GL098274. https://doi.org/10.1029/2022GL098274
dc.rightsPython code is distributed under an MIT License (see LICENSE.txt file).
dc.rights.licenseData are distributed under the terms and conditions of the Creative Commons CC BY: Attribution 4.0 International license (https://creativecommons.org/licenses/by/4.0/).
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectAerosol direct radiative effecten_US
dc.subjectAerosol remote sensingen_US
dc.subjectMachine Learningen_US
dc.titleDataset associated with "Near-Cloud Aerosol Retrieval Using Machine Learning Techniques, and Implied Direct Radiative Effects"en_US
dc.typeDataseten_US

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