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An Integrated Framework for Hybrid and Adaptive Modeling of Sea Surface Temperature

Sea surface temperature (SST) fields are among the most broadly used observational data sets related to the ocean, and constitute critical information for informing a broad range of analyses and models, ranging from estimates of near-surface currents and water body masses, to application in biodiversity models, support of search and rescue missions, as well as the investigation of air-sea interaction at many scales. There is a bewildering array of SST products available, many deriving from satellite-borne instruments, as well as ship-board and other in situ instruments. Quantitative comparison and integration of these various SST data sources is currently extremely difficult and time-consuming. This use case will focus on developing the Kepler workflow application to facilitate the quantitative evaluation of SST data sets. Initial areas for investigation and development include facilitating the researcherís ability to find SST data sets falling in a given space- time window, without the need for specialized queries for discovery of relevant information. The workflow application will enable the researcher to link these input SST data streams to extraction filters that will build match-up data sets from various comparison products. The output of the extraction filters could then be incorporated into analytical workflows to perform a variety of user-selected statistical comparisons and visualizations on the matchups. A range of additional display/report capabilities would also be available as workflow output.


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This particular version was published on 09-Jul-2007 11:30:23 PDT by uid=barseghian,o=NCEAS.