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 poster presents a case study to develop Kepler scientific workflows to facilitate the quantitative evaluation of SST data sets. The presented workflow is comprised of three main steps, namely, a user input sub-workflow, a match-up generation sub-workflow, and a statistical analysis sub-workflow. The user input sub-workflow provides the user with an interface to specify how the match-up database is to be constructed and which SST data sets are to be compared. The match-up generation sub-workflow produces a match-up database from the selected SST datasets. Finally, the analysis sub-workflow performs a suite of statistical analyses on the match-up database. The workflow generates a KML file based on the results of this analysis that can be displayed using Google Earth.

The presented work is part of a National Science Foundation (NSF) funded project called Realtime Environment for Analytical Processing. (REAP, http://reap.ecoinformatics.org)