1. Division of Nearshore Research TCOON Tides and Tide Forecasting Dr. Patrick Michaud October 27, 2003

2. Texas A&M Univ-Corpus Christi Located in Corpus Christi, Texas Focused on regional and environmental issues, especially for the Gulf of Mexico Blucher Institute focuses on measuring the earth and its processes

3. 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

4. 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

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

6. Typical TCOON Station Wind anemometer Radio Antenna Satellite Transmitter Solar Panels Data Collector Water Level Sensor Water Quality Sensor Current Meter

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

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

13. Data Collection Paths

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

15. Data Management Design Principles Preserve source data Annotate instead of modify Automate as much as possible Maintain a standard interchange format Avoid complex or proprietary components Emphasize long-term reliability over short- term costs

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

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

18. Tidal Datum Schematic

21. 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

22. New Data Collection Hardware Linux operating system 2.4.9 kernel 16MB RAM, 32MB HDD 486 or Pentium processor Concurrent processes GNU shell/tools cron bash gcc

23. 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

24. Water-level graph

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

26. 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”.

27. 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) = H 0 +  H c f y,c cos(a c t + e y,c – k c ) h(t) = elevation of water at time t H 0 = datum offset a c = frequency (speed) of constituent t f y,c e y,c = node factors/equilibrium args H c = amplitude of constituent c k c = phase offset for constituent c

28. Harmonic tide predictions Obtain amplitudes and phases of harmonic constituents from trusted sources (e.g., NOS) or Perform a least-squares analysis on observations to determine amplitudes and phases of harmonic constituents To predict tides using harmonic analysis:

29. Harmonic prediction Apply the amplitudes/phases to get:

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

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

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

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

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

35. Uses of harmonic prediction

37. landfall

38. Isidore & JFK Causeway Effect of Isidore at JFK causeway

39. 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

40. Opportunity Problem: The tide charts do not work for most of the Texas coast Opportunity: We have extensive time series of water level and weather measurements for most of the Texas coast

41. Data Intensive Modeling Real time data availability is rapidly increasing Cost of weather sensors and telecommunication equipment is steadily decreasing while performance is improving How to use these new streams of data / can new modeling techniques be developed

42. Study Area: Corpus Christi Estuary Bob Hall Pier Packery Channel Naval Air Station Aquarium Ingleside Port Aransas Nueces Bay Corpus Christi Bay Gulf of Mexico Oso Bay Port of Corpus Christi

43. Bob Hall Pier Packery Channel Naval Air Station AquariumIngleside Port Aransas Nueces Bay Corpus Christi Bay Gulf of Mexico Oso Bay Port of Corpus Christi 6 TCOON Stations Measuring: Water levels (6) Wind speeds (4) Wind directions (4)  10 x 8760 hourly measurements per year Barometric pressure Air temperature Water temperature TCOON Data in CC Bay

44. Data Intensive Modeling Classic models (large computer codes - finite elements based) need boundary conditions and forcing functions which are difficult to provide during storm events Neural Network modeling can take advantage of high data density and does not require the explicit input of boundary conditions and forcing functions The modeling is focused on forecasting water levels at specific locations

45. Neural Network Modeling Started in the 60’s Key innovation in the late 80’s: Backpropagation learning algorithms Number of applications has grown rapidly in the 90’s especially financial applications Growing number of publications presenting environmental applications

46. Neural Network Features Non linear modeling capability Generic modeling capability Robustness to noisy data Ability for dynamic learning Requires availability of high density of data

47. 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)

48. BHP Performance Analysis harmonic forecasts (blue/squares), Persistence model (green/diamonds), ANN model without wind forecasts (red dashed/triangles) and ANN model with wind forecasts (red/circles)

49. CCNAS Performance Analysis Harmonic forecasts (blue/squares), Persistent model (green/diamonds), ANN model with only NAS data (red dashed/triangles) and ANN model with additional BHP data (red/circles)

50. Tropical Storms and Hurricanes Need for short to medium term water level forecasts during tropical storms and hurricanes Tropical storms and hurricanes are relatively infrequent and have each their own characteristics. ANN model performance?

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

52. CCNAS ANN 24-hour Forecasts During 1998 Tropical Storm Frances (ANN trained over 2001 Data Set)

53. 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