Providing Wavelength Resolved Irradiance Measurements by Using Machine Learning

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dc.contributor.advisorBiewer, Michael C.
dc.contributor.advisorLary, David J.
dc.contributor.committeeMemberStoneback, Russell
dc.contributor.committeeMemberLou, Xinchou
dc.contributor.committeeMemberAnderson, Phillip C.
dc.contributor.committeeMemberDa Silveira Rodrigues, Fabiano
dc.creatorZhang, Yichao
dc.date.accessioned2023-02-21T20:58:08Z
dc.date.available2023-02-21T20:58:08Z
dc.date.created2021-12
dc.date.issued2021-12-01T06:00:00.000Z
dc.date.submittedDecember 2021
dc.date.updated2023-02-21T20:58:09Z
dc.description.abstractSunlight incident on the Earth’s atmosphere is essential for life and is the driving force for atmospheric photo-chemistry. Atmospheric photo-chemistry is central to understanding urban air quality and the host of associated human health impacts. In this dissertation, two solutions were proposed to address the current lack of real-time wavelength-resolved solar irradiance data across cities. Our first solution is based on the machine learning calibration of low-cost light sensors. These calibrated sensors have a strong performance and can be readily deployed at scale across dense urban environments to measure the wavelength resolved irradiance on a neighborhood scale. This work has been published in MDPI (Zhang et al., 2021). Our second solution is based on the comprehensive dataset from public environmental sensors. We developed another machine learning model to estimate the wavelength resolved solar irradiance from solar zenith angle, earth distance, and multiple environmental dataset, such as relative humidity, total column ozone, earth surface reflectance, and radar reflectivities in the sky. All these factors can be accessed from the public datasets of weather stations and remote sensing systems. Using this solution, wavelength resolved solar irradiance can be estimated in a neighborhood scale, without implementing any additional sensors.
dc.format.mimetypeapplication/pdf
dc.identifier.uri
dc.identifier.urihttps://hdl.handle.net/10735.1/9598
dc.language.isoen
dc.subjectPhysics, Atmospheric Science
dc.titleProviding Wavelength Resolved Irradiance Measurements by Using Machine Learning
dc.typeThesis
dc.type.materialtext
thesis.degree.collegeSchool of Natural Sciences and Mathematics
thesis.degree.departmentPhysics
thesis.degree.grantorThe University of Texas at Dallas
thesis.degree.namePHD

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