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Adventures in Data Analysis: The TAO Array and the 1997–1998 El Niño

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Abstract

Traditional undergraduate education in earth sciences does not emphasize data acquisition, analysis, or assessment. However, arrival of the information age dictates that earth sciences graduates be imbued with fundamental skills to organize, evaluate and process large data sets. Fortunately, the proliferation of remotely sensed data and its availability via the Internet provides many opportunities for earth science educators to meet these needs. Exercises to introduce students to data analysis have been designed utilizing data from the Tropical Atmosphere–Ocean (TAO) Array and the 1997–1998 El Niño episode in the tropical Pacific Ocean. The TAO Array is a grid of 69 buoys moored across the equatorial Pacific Ocean (8°N to 8°S and 95°W to 143°E) recording environmental data relevant to El Niño—Southern Oscillation (ENSO) processes. Data from the TAO Array is available in near-real-time (http://www.pmel.noaa.gov/toga-tao/realtime.html) or as archived ASCII files (http://www.pmel.noaa.gov/toga-tao/data-delivery.html) providing daily (sometimes hourly) records of environmental parameters for each buoy in the grid. Student exercises in data analysis begin with downloading data from buoy locations, parsing the data into spreadsheets, and organizing data by environmental parameter into yearly and monthly data sets. Analyses of reconstructed data include calculations of long-term averages of environmental parameters, seasonal climatologies, monthly climatologies and calculation of long-term, seasonal, and monthly anomalies. Finally, monthly anomaly maps produced by students are loaded sequentially into GIF-animation software to create time-series images illustrating the progress and development of the 1997–1998 El Niño event.

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  1. Department of Geosciences, University of Arkansas, 113 Ozark Hall, Fayetteville, Arkansas, 72701

    Stephen K. Boss

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Boss, S.K. Adventures in Data Analysis: The TAO Array and the 1997–1998 El Niño. Mathematical Geology 32, 159–185 (2000). https://doi.org/10.1023/A:1007523322222

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