Division of Nearshore Research TCOON Dr. Gary Jeffress Dr. Patrick Michaud February 3, 2003

Division of Nearshore Research Projects Texas Coastal Ocean Observation Network NOAA/NOS Natl Water Level Obs Network Houston/Galveston PORTS National/Global Ocean Observing System TWDB Intensive Surveys Nueces Bay Salinity Project Corpus Christi Real-Time Navigation System CMP - Neural-Network Forecasting CMP - Waves

TCOON Overview Started 1988 Over 50 stations Primary Sponsors General Land Office Water Devel. Board US Corps of Eng Nat'l Ocean Service Gulf of Mexico

TCOON Overview Measurements Follows NOAA/NOS standards Precise Water Levels Wind Temperature Barometric Pressure Follows NOAA/NOS standards Real-time, online database

Typical TCOON Station Wind anemometer Radio Antenna Satellite Transmitter Solar Panels Data Collector Water Level Sensor Water Quality Sensor Current Meter These are the typical sensors associated with a TCOON station.

Once the data are collected at the Blucher Institute, they are available via our web site. This slide shows an example of one of our “overview” pages describing the current conditions at a nearby platform. The top of the page identifies the platform (“Naval Air Station”), its location and other parameters. The latest observations are displayed next in both metric and English units. For many stations we are able to display the information in “real-time” (not older than 15 minutes).

Because of the importance of the data for boundary determination and navigation, we have an extensive quality control system in place. Every morning, an analyst reviews all of the data collected by all stations. This slide shows what an analyst would be looking at. Since 1996, all TCOON data processing has taken place via the World Wide Web; that is, not only do we publish data via the web, but we also use the web as the primary internal interface for updating and maintaining the network itself. Thus, the analyst visits a web page showing recently collected data. In this slide, the top graph represents the water elevations, the next graph displays wave activity, and the bottom graph shows air and water temperatures.

Moving further down the web page, you can see the interface the analyst uses to monitor the system. If any problems are observed in the operation of the station, the analyst enters a note using the form at the bottom of the web page. When all of the stations have been reviewed, the outstanding notes are then organized and sent via electronic mail to the entire staff. These notes then form the basis for repair and maintenance operations for the next few days.

Nueces Bay Salinity Project Started 1991 Informs data management officials of opportunities to avoid water releases Water quality data collected every 30 minutes

Other Real-Time Systems Real-time Navigation Port of Corpus Christi Port Freeport NOAA PORTS Offshore Weather

Data Collection Paths Data are transmitted from the stations to the Conrad Blucher Institute in a variety of paths, including satellite transmissions, packet radio, and telephone connections.

Data Management Automated Acquisition, Archive, Processing, Retrieval 10-year Historical Database Most processing takes place via Internet Infrastructure for other observation systems

Uses of DNR/TCOON Data Tidal Datums Littoral Boundaries Oil-Spill Response Navigation Storm Preparation/ Response Water Quality Studies Research

Tidal Datums Used for Coastal property boundaries Nautical charts Bridge and engineering structures

Tidal Datum Schematic

New Data Collection Hardware PC-104 based computer Linux operating system Solid-state Flash memory 10 serial ports, 16 A/D channels Low power consumption Rugged for harsh environments Because of difficulties in finding flexible data collection systems, the Blucher Institute has been designing its own data collectors using commercially available components. We are now using PC-104 based computer components to provide the sensor control and data collection for our individual stations. PC-104 provides a good, stable platform for software development and long-term availability of components.

Research Real-time Automated Data Processing Tidal Datum Processing Web-based Visualization and Manipulation of Coastal Data Neural-Network-based forecasts from real-time observations Specialized sensor and data acquisition system development Support for other research efforts

Water level forecasting Isidore begins to (re-)enter the Gulf… …what will happen next?

Tide predictions tide: The periodic rise and fall of a body of water resulting from gravitational interactions between Sun, Moon, and Earth. Tide and Current Glossary, National Ocean Service, 2000 According to NOS, changes in water level from non-gravitational forces are not “tides”.

Harmonic analysis Standard method for tide predictions Represented by constituent cosine waves with known frequencies based on gravitational (periodic) forces Elevation of water is modeled as h(t) = H0 +  Hc fy,c cos(act + ey,c – kc) h(t) = elevation of water at time t H0 = datum offset ac = frequency (speed) of constituent t fy,c ey,c = node factors/equilibrium args Hc = amplitude of constituent c kc = phase offset for constituent c

Prediction vs. observation It’s nice when it works…

Prediction vs. observation …but it often doesn’t work in Texas

Water level != tide In Texas, meteorological factors have a significant effect on water elevations

Uses of harmonic predictions However, harmonic predictions can still be useful! Consider… Isidore begins to (re-)enter the Gulf… …what will happen next?

Uses of harmonic predictions If we add harmonic prediction… …what will happen next?

Uses of harmonic prediction landfall

Harmonic WL prediction - present capabilities Automated system for computing harmonic constituent values from observations database Harmonic predictions available via query page for many TCOON stations

Water-level prediction – (near) future capabilities Persistent model forecast Apply difference between latest observation and harmonic prediction to future predictions Forecasts page on DNR web site Obtain forecasts from different models Harmonic predictions Persistent model Neural-network model Linear-regression/statistical model Hybrid models Information about water-level forecasting methods Statistics on previous forecasts

Neural Network Forecasting Use neural network to model non-tidal component of water level Reliable short-term predictions CCNAS ANN 24-hour Forecasts for 1997 (ANN trained over 2001 Data Set)

Forecasts in storm events CCNAS ANN 12-hour Forecasts During 1998 Tropical Storm Frances (ANN trained over 2001 Data Set)

Conclusions Long-term, data-rich observation network Web-based infrastructure for automated collection and processing of marine data Research in datum computation and coastal forecasting