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dc.contributor.advisorDavid H. Staelin.en_US
dc.contributor.authorHancock, Jay Brian, 1976-en_US
dc.contributor.otherMassachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science.en_US
dc.date.accessioned2005-08-23T21:23:05Z
dc.date.available2005-08-23T21:23:05Z
dc.date.copyright2001en_US
dc.date.issued2001en_US
dc.identifier.urihttp://hdl.handle.net/1721.1/8573
dc.descriptionThesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2001.en_US
dc.descriptionIncludes bibliographical references (p. 231-234).en_US
dc.description.abstractTwo clear-air relative humidity profile estimators were designed and implemented using neural networks. The microwave estimator is the first to utilize 54-, 118-, and 183-GHz channels for simultaneously retrieving a relative humidity profile. It utilizes 2 separate instruments simultaneously. The first instrument is a medium-resolution dual-band radiometer with one set of 8 double-sideband 118-GHz channels and a second set of 8 single-sideband 54-GHz channels. The other instrument is a high-resolution double-sideband radiometer with a set of 3 183-GHz channels, and additional channels at 89, 220, and 150 GHz. The infrared estimator is among the first to utilize a hyperspectral infrared aircraft instrument for relative humidity profile retrievals. The infrared instrument is a 9000-channel interferometer operative over the wavelength range of 3.8-16.2 microns. Both estimators utilized neural networks of comparable topology and training methods. The training data was generated from the SATIGR set of 1761 RAOBs using a different implementation of the discrete radiative transfer equation for each estimator. The test data were from two clear-air ER-2 aircraft flights during the tropical CAMEX-3 mission near Andros Island. The retrievals were robust in the face of unknown instrument bias and noise, which introduced a difference between the training data and the flight data. A noise-averaging technique achieved robustness in exchange for a degradation in sensitivity of the retrieval algorithms. Robustness was demonstrated by the retrieval agreement between the microwave and infrared instruments. The theoretical average rms error in relative humidity for the various techniques on the training set was 12% for the microwave estimator, 11% for the infrared, and 10% for a linear regression of the two. In application to two flights, the rms error was 9.4% for the microwave, 7.7% for the infrared, and 7.7% for the combination, based on comparisons with nearby radiosondes.en_US
dc.description.statementofresponsibilityby Jay Brian Hancock.en_US
dc.format.extent234 p.en_US
dc.format.extent30268718 bytes
dc.format.extent30268475 bytes
dc.format.mimetypeapplication/pdf
dc.format.mimetypeapplication/pdf
dc.language.isoengen_US
dc.publisherMassachusetts Institute of Technologyen_US
dc.rightsM.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission.en_US
dc.rights.urihttp://dspace.mit.edu/handle/1721.1/7582
dc.subjectElectrical Engineering and Computer Science.en_US
dc.titlePassive microwave and hyperspectral infrared retrievals of atmospheric water vapor profilesen_US
dc.typeThesisen_US
dc.description.degreeS.M.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science.en_US
dc.identifier.oclc49223453en_US


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