Signal Post-Processing as Applied to Texas Coastal Ocean Observation Network (TCOON) Data

C. Steidley, R. Rush, D. Thomas, P. Tissot, A. Sadovski, and R. Bachnak (USA)


Environmental monitoring and control, Filtering, Change detection


The Texas Coastal Ocean Observation Network (TCOON) consists of more than 50 data gathering stations located along the Texas Gulf coast from the Louisiana to Mexico borders. Data sampled at these stations include: precise water levels, wind speed and direction, atmospheric and water temperatures, barometric pressure, and water currents. The measurements collected at these stations are often used in legal proceedings such as littoral boundary determinations; therefore data are collected according to National Ocean Service standards. Some stations of TCOON collect parameters such as turbidity, salinity, and other water quality parameters. All data are transmitted back to Texas A&M University Corpus Christi (A&M-CC) at multiples of six minutes via line-of sight packet radio, cellular phone, or GOES satellite, where they are then processed and stored in a real-time, web-enabled database. TCOON has been in operation since 1988. This paper describes a software project based upon signal processing techniques to be utilized with the TCOON meteorological database to detect spikes in water level. Water level readings are frequently victim to abnormal water levels caused by ship wakes, affected equipment scrambled by thunder, or corrupted by transmission errors. Since these water levels are the bases for a number of research calculations, such as, oil-spill response, navigation safety, environmental research, and recreation, it is essential to be able to make these water level data as correct and spike free as possible.

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