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Dual-satellite cloud product generation using temporally updated canonical coordinate features

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dc.contributor.authorKankiewicz, J. Adam, author
dc.contributor.authorAzimi-Sadjadi, Mahmood R., author
dc.contributor.authorFalcone, Amanda K., author
dc.contributor.authorIEEE, publisher
dc.date.accessioned2007-01-03T04:43:35Z
dc.date.available2007-01-03T04:43:35Z
dc.date.issued2007
dc.description.abstractState-of-the-art cloud products are typically generated using scientific polar orbiting satellites such as the Moderate Resolution Imaging Spectroradiometer (MODIS). However, they do not allow for observation of the same region at a regular temporal frequency, rendering them ineffectual for nowcasting problems. Operational satellites such as Meteosat-8 SEVIRI, in contrast, are geostationary and provide continual data at a regular temporal frequency over a much larger region. MODIS-like cloud products cannot be directly generated from operational satellites as they typically have a smaller number of spectral bands and different wavelengths and spatial resolution. This paper applies the canonical coordinate decomposition method to estimate scientific cloud products using imagery from operational satellites. Using the proposed method features of the Meteosat-8 imagery data that are maximally coherent with the data from the MODIS are generated. These features are temporally updated at times and locations where MODIS data are unavailable using the alternating block power method. A subset of the canonical coordinates of Meteosat-8 SEVIRI is then used to create MODIS-like cloud products using several neural networks. The quality of the generated cloud products and their temporal consistency have been demonstrated on several data sets from July 2004. A benchmarking with an independent Meteosat-8-based algorithm is also provided, which shows the promise of our approach in generating MODIS-like cloud products.
dc.description.sponsorshipThis work was supported by the Department of Defense Center for Geosciences/Atmospheric Research, Colorado State University, via Cooperative Agreement DAAD19-02-2-0005 with the Army Research Laboratory.
dc.format.mediumborn digital
dc.format.mediumarticles
dc.identifier.bibliographicCitationFalcone, Amanda, K., Mahmood R. Azimi-Sadjadi, and J. Adam Kankiewicz, Dual-Satellite Cloud Product Generation Using Temporally Updated Canonical Coordinate Features, IEEE Transactions on Geoscience and Remote Sensing 45, no. 4 (April 2007): 1046-1060.
dc.identifier.urihttp://hdl.handle.net/10217/846
dc.languageEnglish
dc.language.isoeng
dc.publisherColorado State University. Libraries
dc.relation.ispartofFaculty Publications
dc.rights©2007 IEEE.
dc.rightsCopyright and other restrictions may apply. User is responsible for compliance with all applicable laws. For information about copyright law, please see https://libguides.colostate.edu/copyright.
dc.subjectpower methods
dc.subjectcloud mask and phase
dc.subjectcanonical correlation analysis
dc.subjectbackpropagation neural networks (BPNNs)
dc.subjectremote sensing
dc.subjecttemporal updating
dc.titleDual-satellite cloud product generation using temporally updated canonical coordinate features
dc.typeText

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