Division of Nearshore ResearchTCOONTides and Tide Forecasting

Dr. Patrick MichaudOctober 27, 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 ofMexico

TCOON Overview

Measurements 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

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 Management

Automated Acquisition, Archive, Processing, Retrieval

10-year Historical Database

Most processing takes place via Internet

Infrastructure for other observation systems

Data ManagementDesign 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

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

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

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 graph

Water level forecasting

…what will happen next?

Isidore begins to (re-)enter the Gulf…

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 tH0 = datum offsetac = frequency (speed) of constituent tfy,c ey,c = node factors/equilibrium args

Hc = amplitude of constituent ckc = phase offset for constituent c

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:

Harmonic prediction

Apply the amplitudes/phases to get:

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…

…what will happen next?

Isidore begins to (re-)enter the Gulf…

Uses of harmonic predictions

If we add harmonic prediction…

…what will happen next?

Uses of harmonic prediction

landfall

Isidore & JFK Causeway

Effect of Isidore at JFK causeway

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

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

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

Bob Hall Pier

Packery Channel

Naval Air Station

Aquarium InglesidePort Aransas

Nueces Bay

Corpus Christi Bay

Gulf of Mexico

Oso BayPort 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

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

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

Neural Network Forecasting

Use neural network to model non-tidal component of water level

Reliable short-term predictions

75 80 85 90 95 100 105 110 115 120 125

0

0.1

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0.3

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0.7

Wat

er L

evel

s (m

)

Julian Day,1997

CCNAS ANN 24-hour Forecasts for 1997 (ANN trained over 2001 Data Set)

BHP Performance Analysis

0.00

0.02

0.04

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0.10

0 hr 6 hr 12 hr 18 hr 24 hr 30 hr 36 hr 42 hr 48 hr 54 hr

Forecasting Period

harmonic forecasts (blue/squares), Persistence model (green/diamonds), ANN model without wind forecasts (red dashed/triangles) and ANN model with wind forecasts

(red/circles)

CCNAS Performance Analysis

0.00

0.02

0.04

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0.08

0.10

0 hr 6 hr 12 hr 18 hr 24 hr 30 hr 36 hr 42 hr 48 hr 54 hr

Forecasting Period

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)

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?

Forecasts in storm events

230 235 240 245 250 255 260 265 270 2750

0.2

0.4

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0.8

1

1.2

Wat

er L

evel

s (m

)

Julian Day,1998

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

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

230 240 250 260 270 280

0

0.2

0.4

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0.8

1

1.2

Wate

r L

ev

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(m

)

Julian Day,1998

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