December 2004
NASA/Goddard Space Flight Center--EOS Project Science Office
Exploring ocean life and color on the internetNew software enables students to do research on-line
Giovanni for Internet Users: Giovanni provides users with an easy-to-use, Web-based interface for visualization and analysis of ocean color data. Users can generate plots or ASCII file output for area-average, time-series, and latitude vs. time or longitude vs. time diagrams. Credit: NASA
A new NASA Internet tool called "Giovanni" allows high school and college students and researchers to access and analyze satellite-derived ocean color data. Ocean color data provides students with information about ocean biology by looking at phytoplankton through changes in the color of the ocean surface.
"Ocean color" refers primarily to the measurement of the green pigment called chlorophyll, which is contained in phytoplankton. Phytoplankton are free-floating plants that are the foundation of the ocean's food chain.
Giovanni stands for the "Goddard Earth Sciences Data and Information Services Center (GES DISC) Online Visualization and Analysis Infrastructure." NASA recently released three Giovanni tutorials demonstrating how students can conduct research with ocean color data. Use of such technical information was previously only possible for experienced scientists with advanced computer systems.
Scientists and software developers at NASA's Goddard Space Flight Center (GSFC), Greenbelt, Md., designed Giovanni. The initial release of this Web tool allows users to see ocean color data from the SeaWiFS satellite. Data from other ocean color missions will be added, including data from NASA's Aqua satellite. Giovanni development is part of the Ocean Color Time-Series Project, headed by Dr. Watson Gregg, a NASA GSFC oceanographer.
Dr. James Acker of the GES DISC Oceans Data Team created three tutorials geared for high school and college level students. These tutorials help students identify research questions that can be answered with ocean color data.
"In creating these tutorials, I discovered features in the data that were a complete surprise," Acker said. "The tutorials show how to use Giovanni, and how students can use it to make new discoveries, potentially contributing to ocean science."
In the first tutorial, Dr. Acker looked at the chlorophyll patterns in the Gulf of Panama to see if they were influenced by El Nino/La Nina events. The Gulf of Panama has a strong seasonal pattern caused by strong winds that blow through the Panama Canal Zone in winter. The winds mix nutrients from deeper waters to the surface, and the nutrients promote phytoplankton growth. The strong 1997-1998 El Nino reduced the productivity, or how much phytoplankton grow in this region, as expected.
In the summer of 2001, however, there were short bursts of higher productivity not seen in other years. This unusual pattern may have been an early indicator of how the Gulf of Panama changed before the moderate El Nino event that occurred in 2002-2003.
The second tutorial investigated seasonal patterns of productivity in the Red Sea. There were two seasonal patterns in the Red Sea, one in the north and another in the south. Though these patterns are familiar to oceanographers, Giovanni provided another surprise. "I saw a very small area of relatively high chlorophyll concentrations near the Egyptian coast," Acker said. "At first it looked like a small river was entering the Red Sea. But there aren't any rivers in this part of the desert." Further investigation indicated that this area was associated with a large coral reef complex on the Red Sea coast. A third tutorial examines the California coast near Monterey Bay, and discusses the influence of clouds on the data.
In the past, researchers had to download data files and analyze them on their own computing systems, a difficult and time-consuming process. Giovanni is one of the first demonstrations of new technology that will be improved in the future, making it much easier to use the data, including the multi-decade data sets that the Ocean Color Time-Series Project will create.
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