Mineral Dust Detection Using Satellite Data

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dc.contributor.authorShi, Peichang
dc.contributor.authorSong, Qianqian
dc.contributor.authorPatwardhan, Janita
dc.contributor.authorZhang, Zhibo
dc.contributor.authorWang, Jianwu
dc.date.accessioned2020-07-29T17:46:08Z
dc.date.available2020-07-29T17:46:08Z
dc.description.abstractMineral dust, defined as aerosol originating from the soil, can have various harmful effects to the environment and human health. The detection of dust, and particularly incoming dust storms, may help prevent some of these negative impacts. We investigated both physical and machine learning algorithms of dust aerosols detection over the Atlantic Ocean using satellite observations from Moderate Resolution Imaging Spectroradiometer (MODIS) and the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation Observation (CALIPSO). We found that the machine learning algorithms achieved a higher accuracy rate compared to those of the physical algorithms. Through combining a logistic regression algorithm with our physical understanding of dust aerosols, we were able to reach the highest detection accuracy.en_US
dc.description.sponsorshipTeam 3 members gratefully acknowledge the NSF-funded CyberTraining program and all instructors for providing this chance for us to learn more about parallel computing. The hardware used in the computational studies is part of the UMBC High Performance Computing Facility (HPCF). The facility is supported by the U.S. National Science Foundation through the MRI program (CNS–0821258, CNS–1228778, and OAC–1726023) and the SCREMS program (DMS–0821311), with additional substantial support from the University of Maryland, Baltimore County (UMBC). See hpcf.umbc.edu for more information on HPCF and the projects using its resources.en_US
dc.description.urihttp://hpcf-files.umbc.edu/research/papers/CT2018Team3.pdfen_US
dc.format.extent11 pagesen_US
dc.genretechnical reportsen_US
dc.identifierdoi:10.13016/m2hpzu-r0sn
dc.identifier.citationPeichang Shi et al., Mineral Dust Detection Using Satellite Data, http://hpcf-files.umbc.edu/research/papers/CT2018Team3.pdfen_US
dc.identifier.urihttp://hdl.handle.net/11603/19274
dc.language.isoen_USen_US
dc.publisherUMBCen_US
dc.relation.isAvailableAtThe University of Maryland, Baltimore County (UMBC)
dc.relation.ispartofUMBC Information Systems Department Collection
dc.relation.ispartofUMBC Faculty Collection
dc.relation.ispartofUMBC Student Collection
dc.relation.ispartofUMBC Joint Center for Earth Systems Technology (JCET)
dc.relation.ispartofUMBC Mathematics and Statistics Department
dc.relation.ispartofUMBC Physics Department
dc.relation.ispartofseriesHPCF–2018–13;
dc.rightsThis item is likely protected under Title 17 of the U.S. Copyright Law. Unless on a Creative Commons license, for uses protected by Copyright Law, contact the copyright holder or the author.
dc.subjectUMBC High Performance Computing Facility (HPCF)en_US
dc.titleMineral Dust Detection Using Satellite Dataen_US
dc.typeTexten_US

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