Earth’s Tides Objectives: Define tides What causes tides How often tides occur Types of tides.
Division of Nearshore Research TCOON Tides and Tide Forecasting
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Transcript of Division of Nearshore Research TCOON Tides and Tide Forecasting
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
0.2
0.3
0.4
0.5
0.6
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
0.06
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), 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
0.06
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
0.6
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
0.6
0.8
1
1.2
Wate
r L
ev
els
(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