Advanced signal processing techniques for the analysis of solar radiometer data in the presence of temporally varying aerosol optical depths

Persistent Link:
http://hdl.handle.net/10150/282644
Title:
Advanced signal processing techniques for the analysis of solar radiometer data in the presence of temporally varying aerosol optical depths
Author:
Erxleben, Wayne Henry, 1963-
Issue Date:
1998
Publisher:
The University of Arizona.
Rights:
Copyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.
Abstract:
Solar radiometers, which are used for remote sensing of atmospheric aerosols and absorbing gases, have traditionally been calibrated by the Langley method. Temporally variable conditions, however, can significantly bias the zero-airmass intercepts obtained by this method. In this dissertation, a number of new signal processing techniques are developed to better characterize aerosol variability and use it to obtain improved intercepts under a broad range of conditions. The techniques include (1) an extension of Forgan's method, using correlation between optical depths at different wavelengths to model temporal variations; (2) spectral/fractal analysis and filtering to identify systematic atmospheric variations and distinguish them from noise; and (3) error correction using correlation between results from different data sets. These techniques, along with some preliminary adjustments and an algorithm for estimating ozone content, are incorporated into an iterative processing scheme that both calibrates the instrument and provides improved estimates of each optically significant atmospheric constituent. Finally, the characterization of aerosol variability is further enhanced by analyzing data taken with a customized radiometer that measures diffuse skylight as well as direct sunlight.
Type:
text; Dissertation-Reproduction (electronic)
Keywords:
Engineering, Electronics and Electrical.; Physics, Atmospheric Science.; Remote Sensing.
Degree Name:
Ph.D.
Degree Level:
doctoral
Degree Program:
Graduate College; Electrical and Computer Engineering
Degree Grantor:
University of Arizona
Advisor:
Reagan, John A.

Full metadata record

DC FieldValue Language
dc.language.isoen_USen_US
dc.titleAdvanced signal processing techniques for the analysis of solar radiometer data in the presence of temporally varying aerosol optical depthsen_US
dc.creatorErxleben, Wayne Henry, 1963-en_US
dc.contributor.authorErxleben, Wayne Henry, 1963-en_US
dc.date.issued1998en_US
dc.publisherThe University of Arizona.en_US
dc.rightsCopyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.en_US
dc.description.abstractSolar radiometers, which are used for remote sensing of atmospheric aerosols and absorbing gases, have traditionally been calibrated by the Langley method. Temporally variable conditions, however, can significantly bias the zero-airmass intercepts obtained by this method. In this dissertation, a number of new signal processing techniques are developed to better characterize aerosol variability and use it to obtain improved intercepts under a broad range of conditions. The techniques include (1) an extension of Forgan's method, using correlation between optical depths at different wavelengths to model temporal variations; (2) spectral/fractal analysis and filtering to identify systematic atmospheric variations and distinguish them from noise; and (3) error correction using correlation between results from different data sets. These techniques, along with some preliminary adjustments and an algorithm for estimating ozone content, are incorporated into an iterative processing scheme that both calibrates the instrument and provides improved estimates of each optically significant atmospheric constituent. Finally, the characterization of aerosol variability is further enhanced by analyzing data taken with a customized radiometer that measures diffuse skylight as well as direct sunlight.en_US
dc.typetexten_US
dc.typeDissertation-Reproduction (electronic)en_US
dc.subjectEngineering, Electronics and Electrical.en_US
dc.subjectPhysics, Atmospheric Science.en_US
dc.subjectRemote Sensing.en_US
thesis.degree.namePh.D.en_US
thesis.degree.leveldoctoralen_US
thesis.degree.disciplineGraduate Collegeen_US
thesis.degree.disciplineElectrical and Computer Engineeringen_US
thesis.degree.grantorUniversity of Arizonaen_US
dc.contributor.advisorReagan, John A.en_US
dc.identifier.proquest9829398en_US
dc.identifier.bibrecord.b38563812en_US
All Items in UA Campus Repository are protected by copyright, with all rights reserved, unless otherwise indicated.