Mineral Dust Detection Using Satellite Data

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CT2018Team3.pdf (3.54 MB)

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http://hpcf-files.umbc.edu/research/papers/CT2018Team3.pdf

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http://hdl.handle.net/11603/19274

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UMBC Joint Center for Earth Systems Technology (JCET)
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technical reports
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Peichang Shi et al., Mineral Dust Detection Using Satellite Data, http://hpcf-files.umbc.edu/research/papers/CT2018Team3.pdf

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UMBC High Performance Computing Facility(HPCF)

Abstract

Mineral 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.