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INVOLVING UNDERGRADUATE STUDENTS IN FUNDED INTERDISCIPLINARY RESEARCH

Carl Steidley, Alex Sadovski, Kelly Torres, Aimee Mostella, Ray Bachnak

Department of Computing and Mathematical Sciences Texas A&M University Corpus Christi 6300 Ocean Blvd. Corpus Christi, TX 78412

Abstract

The goal of our on-going research is to develop effective and reliable tools for modeling the environmental systems of the Gulf of Mexico. For example, our on-going research into methodologies for the prediction of water levels in the shallow waters of the bays and estuaries along the Texas gulf coast. Our modeling approaches are based on the real-time data collected by the Texas Coastal Ocean Observation Network (TCOON). TCOON is managed by the Division of Nearshore Research (DNR) in cooperation with the Department of Computing and Mathematical Sciences (CAMS) both of Texas A&M University-Corpus Christi. TCOON consists of approximately 50 data gathering stations located along the Texas Gulf coast from the Louisiana to Mexico borders.

TCOON, started in 1988, serves as the major environmental data acquisition system for our modeling efforts. TCOON stations automatically measure and archive various measurements such as water levels, wind speed and direction, temperature, salinity, and barometric pressure. TCOON follows U.S. federal standards for the installation of its stations and has a very useful real-time, online database.

Tide charts, based on harmonic analysis, are generally the method of choice for the forecast of water levels. However there are limitations to the use of tide charts. Tide charts are mostly based on astronomical forcing or the influence on water levels of the respective motions of the earth, the moon, and the sun. There are locations around the world, including the Gulf of Mexico, where other factors such meteorological forcing often dominate tidal forcing and limit significantly the application of tide charts. In such cases other models must be developed to accurately forecast water levels.

Different schemes that we are using for the prediction of water levels include harmonic analysis, multivariate statistical models, and neural networks. In addition to a short description of the major data acquisition system for our research efforts, this paper summarizes our interdisciplinary, NASA-funded, modeling efforts, which have had a great deal of student involvement.

Proceedings of the 2005 American Society for Engineering Education Annual Conference & Exposition Copyright © 2005, American Society for Engineering Education

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Torres, K., & Mostella, A., & Sadovski, A. L., & Steidley, C. (2005, June), Involving Students In Funded Interdisciplinary Research Paper presented at 2005 Annual Conference, Portland, Oregon. 10.18260/1-2--15162