OCEANOGRAPHY AND GOOGLE EARTH: OBSERVING OCEAN PROCESSES WITH TIME ANIMATIONS AND STUDENT BUILT OCEAN DRIFTERS

Google Earth provides an easily accessible platform for students to view oceanographic data from satellites, buoys, and student-built ocean drifters. Displaying these data as time animations allows students to observe and ask questions about how ocean circulation works. Understanding and predicting surface circulation has many widespread applications (e.g. upwelling, harmful algal blooms, oil spill mitigation, fishery science.) The power of Google Earth is that many oceanographic properties can be displayed simultaneously such as atmospheric pressure, wind speed and direction, sea surface height, surface currents, sea surface temperature, and biological productivity. This activity addresses several of the principal outcomes of an introductory oceanography course, including: the ability to observe ocean properties, hypothesize about the inter-relationship of ocean processes, and recognize how technology is used to observe and measure the state of the oceans. This effort resulted from the MATE (Marine Advanced Technology Education) Center’s ocean drifter project, in which students build and release GPS-equipped drifters and watch their movement via satellite communication over the Internet. Because students see, touch, and feel the drifter in the classroom as they build and decorate it, they develop an inherent interest in its fate. Following the motion of the drifter fosters student interest in related oceanographic properties, many of which can be displayed in Google Earth for the same time period providing easy comparisons. The satellite data used in these animations are accessed through the NOAA Southwest Fisheries Science Center’s ERDDAP data server and displayed in Google Earth using KML scripts. Sea surface temperature and sea surface height data are downloaded as .png image files and displayed as a series of image overlays to create a “flowing” time animation. Atmospheric pressure data are downloaded as a grid, converted to contours, and displayed as paths. Wind data, from the National Data Buoy Center buoys, are converted to vectors and plotted as arrow icons, the size and color of which correspond to wind speed. Python scripts automate the process of generating KML scripts. The MATE Center has developed a web-based GUI that allows easy generation of the animations by students.