Show simple item record

dc.contributor.authorLiu, Yonggang  Concept link
dc.contributor.authorWeisberg, Robert H.  Concept link
dc.contributor.authorHe, Ruoying  Concept link
dc.date.accessioned2010-12-07T21:05:51Z
dc.date.available2010-12-07T21:05:51Z
dc.date.issued2006-02
dc.identifier.citationJournal of Atmospheric and Oceanic Technology 23 (2006): 325-338en_US
dc.identifier.urihttps://hdl.handle.net/1912/4186
dc.descriptionAuthor Posting. © American Meteorological Society 2006. This article is posted here by permission of American Meteorological Society for personal use, not for redistribution. The definitive version was published in Journal of Atmospheric and Oceanic Technology 23 (2006): 325–338, doi:10.1175/JTECH1848.1.en_US
dc.description.abstractNeural network analyses based on the self-organizing map (SOM) and the growing hierarchical self-organizing map (GHSOM) are used to examine patterns of the sea surface temperature (SST) variability on the West Florida Shelf from time series of daily SST maps from 1998 to 2002. Four characteristic SST patterns are extracted in the first-layer GHSOM array: winter and summer season patterns, and two transitional patterns. Three of them are further expanded in the second layer, yielding more detailed structures in these seasons. The winter pattern is one of low SST, with isotherms aligned approximately along isobaths. The summer pattern is one of high SST distributed in a horizontally uniform manner. The spring transition includes a midshelf cold tongue. Similar analyses performed on SST anomaly data provide further details of these seasonally varying patterns. It is demonstrated that the GHSOM analysis is more effective in extracting the inherent SST patterns than the widely used EOF method. The underlying patterns in a dataset can be visualized in the SOM array in the same form as the original data, while they can only be expressed in anomaly form in the EOF analysis. Some important features, such as asymmetric SST anomaly patterns of winter/summer and cold/warm tongues, can be revealed by the SOM array but cannot be identified in the lowest mode EOF patterns. Also, unlike the EOF or SOM techniques, the hierarchical structure in the input data can be extracted by the GHSOM analysis.en_US
dc.description.sponsorshipSupport was provided by the Office of Naval Research under Grant N00014-98-1-0158 for observations and modeling of the west Florida continental shelf circulation and Grant N00014-02-1-0972 for the Southeast Atlantic Coastal Ocean Observing System.en_US
dc.format.mimetypeapplication/pdf
dc.language.isoen_USen_US
dc.publisherAmerican Meteorological Societyen_US
dc.relation.urihttps://doi.org/10.1175/JTECH1848.1
dc.titleSea surface temperature patterns on the West Florida Shelf using growing hierarchical self-organizing mapsen_US
dc.typeArticleen_US
dc.identifier.doi10.1175/JTECH1848.1


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record