DNR Forecasts

http://lighthouse.tamucc.edu/dnrpub/nn-channel-3.wmv^?

A set of models were implemented/developed to predict water levels as an aid to navigation in the coastal waters of the Gulf of Mexico and as a tool for emergency management. In Texas tidal predictions do not meet National Ocean Service acceptance criteria as other factors than tidal forcings often dominate water level changes (for example wind stress related forcings and wave climate). The graphs show comparisons of tidal predictions, persistence model predictions (simple but working much better than tides in Texas) and Artificial Neural Network models (perform best overall). Thank you to the sponsors who have made this model possible, including the Texas General Land Office, the NOAA Seangrant and Seagrant Technology programs, and the Texas Research Development Fund. Click to access the real-time water level forecasts.

Water Level Model Development Sponsored By:

A set of models were implemented/developed to predict water levels as an aid to navigation in the coastal waters of the Gulf of Mexico and as a tool for emergency management. In Texas tidal predictions do not meet National Ocean Service acceptance criteria as other factors than tidal forcings often dominate water level changes (for example wind stress related forcings and wave climate). The graphs show comparisons of tidal predictions, persistence model predictions (simple but working much better than tides in Texas) and Artificial Neural Network models (perform best overall). Thank you to the sponsors who have made this model possible, including the Texas General Land Office, the NOAA Seangrant and Seagrant Technology programs, and the Texas Research Development Fund. Click to access the real-time water level forecasts.

(:table width=100%:) (:cell:)Artificial Neural Network and Persistence Water Level Forecasts

Water Level Predictions in the Coastal Bend for Corpus Christi Caller Times Readers

Models' Performance (:cell width=35% align=center:) (:tableend:)

(:cell valign=center:)Artificial Neural Network and Persistence Water Level Forecasts

Water Level Predictions in the Coastal Bend for Corpus Christi Caller Times Readers

Models' Performance (:cell width=350px align=center:)

(:cell:)Artificial Neural Network and Persistence Water Level Forecasts

Water Level Predictions in the Coastal Bend for Corpus Christi Caller Times Readers

Models' Performance (:cell width=45% align=right:)

(:cell width=45% align=right:)

(:table width=100%:)

(:cell width=45% align=center:)

(:cell width=35% align=right:)

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(:cell:) A set of models were implemented/developed to predict water levels as an aid to navigation in the coastal waters of the Gulf of Mexico and as a tool for emergency management. In Texas tidal predictions do not meet National Ocean Service acceptance criteria as other factors than tidal forcings often dominate water level changes (for example wind stress related forcings and wave climate). The graphs show comparisons of tidal predictions, persistence model predictions (simple but working much better than tides in Texas) and Artificial Neural Network models (perform best overall). Thank you to the sponsors who have made this model possible, including the Texas General Land Office, the NOAA Seangrant and Seagrant Technology programs, and the Texas Research Development Fund. Click to access the real-time water level forecasts. (:cell width=35% align=center:)

Artificial Neural Network and Persistence Water Level Forecasts

Water Level Predictions in the Coastal Bend for Corpus Christi Caller Times Readers

Models' Performance

Forecasts

See the old version of this homepage.

Forecasts

Water Level Forecasts

Artificial Neural Network and Persistence Water Level Forecasts

Water Level Predictions in the Coastal Bend for Corpus Christi Caller Times Readers

Models' Performance

Water Temperature Forecasts

Access Artificial Neural Network Water Temperature Forecasts
Access Report and Model Development Information

Access to Selected Atmospheric Predictions from the North American Model (NAM)

Thunderstorm Predictions

Access more information about the thunderstorm model

Other DNR Forecasting Projects

Forecasting projects from other Researchers

Model Performance

Comparison of Performance of Artificial Neural Network model with Tide Charts and Persistence Forecasts

Texas General Land Office Princeton Ocean Model (POM) maps of forecasted surface currents and National Center for Environmental wind surface prediction maps.

Animated versions for the Texas Shelf (http://seawater.tamu.edu/tglo/animation.gif) and for the all Gulf (http://seawater.tamu.edu/tglo/RXanimation.gif).

Texas General Land Office Regional Ocean Modeling System (ROMS) forecasted surface currents maps for the Texas Shelf.

Water level forecasts (Tidal) for select TCOON stations from the Texas Water Development Board.

Texas Water Development Board Hydrodynamic & Oil Spill Modeling.

Texas General Land Office Princeton Ocean Model (POM) maps of forecasted surface currents and National Center for Environmental wind surface prediction maps: http://seawater.tamu.edu/tglo/

Animated versions accessible at "seawater.tamu.edu/tglo/animation.gif" for the Texas Shelf and "seawater.tamu.edu/tglo/RXanimation^?.gif" for the all Gulf

Texas General Land Office Regional Ocean Modeling System (ROMS) forecasted surface currents maps for the TX Shelf: http://seawater.tamu.edu/tglo/rindex.html

Water level forecasts (Tidal) for select TCOON stations from the Texas Water Development Board: http://midgewater.twdb.state.tx.us/bays_estuaries/tides.html

Texas Water Development Board Hydrodynamic & Oil Spill Modeling: http://midgewater.twdb.state.tx.us/bays_estuaries/bhydpage.html

Animated: "http://seawater.tamu.edu/tglo/animation.gif" All Gulf animation: "http://seawater.tamu.edu/tglo/RXanimation.gif"

Animated: http://seawater.tamu.edu/tglo/animation.gif All Gulf animation: http://seawater.tamu.edu/tglo/RXanimation.gif

Other Forecasting Projects

Access more information about the model

DNR is collaborating with Waylon Collins, a senior forecaster at the Corpus Christi Weather Forecasting Office (CCWFO) to develop and implement a model that will predict the timing and location of thunderstorms. The model uses an artificial neural network with input from the North American Model (NAM) for the predicted atmospheric state, Average Optical Depth (AOD) measurements and data derived from high resolution precipitation maps. The model was presented at several AMS conferences and work is ongoing to develop an operational prototype.

Select this text to learn more about the model

We are always working on interesting predictive models to help decision making. Other models we have worked on include the prediction of salinity, recreational water quality (enterococci colony forming units) and spring flows in a karst aquifer. If you would like more information on these models or have suggestions for improving our present models or new models let us know.

Access to Selected Atmospheric Predictions from the North American Model

joint project between DNR and the Corpus Christi Weather Forecasting Office. Since early 2002 NAM and GFS predictions are being stored for about 50 locations along the Texas coast, in the Gulf of Mexico and inland Texas. Most DNR/TCOON stations are covered. Both real-time and archived predictions are available for these models. Accessing the data is a little different then for the rest of the DNR/TCOON data but explained at the following link: http://lighthouse.tamucc.edu/DataQuery/NotSixMinuteDataRetrieval

Access to Selected Atmospheric Predictions from the North American Model^?

A joint project between DNR and the Corpus Christi Weather Forecasting Office. Since early 2002 NAM and GFS predictions are being stored for about 50 locations along the Texas coast, in the Gulf of Mexico and inland Texas. Most DNR/TCOON stations are covered. Both real-time and archived predictions are available for these models. Accessing the data is a little different then for the rest of the DNR/TCOON data but explained at the following link: http://lighthouse.tamucc.edu/DataQuery/NotSixMinuteDataRetrieval

A set of models were implemented/developed to predict water levels as an aid to navigation in the coastal waters of the Gulf of Mexico and as a tool for emergency management. In Texas tidal predictions do not meet National Ocean Service acceptance criteria as other factors than tidal forcings often dominate water level changes (for example wind stress related forcings and wave climate). The graphs show comparisons of tidal predictions, persistence model predictions (simple but working much better than tides in Texas) and Artificial Neural Network models (perform best overall). Thank you to the sponsors who have made this model possible, including the Texas General Land Office, the NOAA Seangrant and Seagrant Technology programs, and the Texas Research Development Fund. Click to access the real-time water level forecasts.

We typically focus on predictive models to help decision makers and anyone looking for coastal forecasts. The models are developed in collaboration with faculty members of the TAMUCC College of Science and Technology, the TAMUCC Center for Water Supply Studies (CWSS) and the Corpus Christi Weather Forecasting Office. Thank you to the sponsors who have made these models possible including the Texas General Land Office, the Texas Parks and Wildlife Department,the Coastal Conservation Association and the NOAA Seangrant and Seagrant Technology programs. If you have suggestions about existing or new models e-mail us or pass by our offices, we thoroughly enjoy the research and the related discussions.

A set of models were implemented/developed to predict water levels as an aid to navigation in the coastal waters of the Gulf of Mexico and as a tool for emergency management. In Texas tidal predictions do not meet National Ocean Service acceptance criteria as other factors than tidal forcings often dominate water level changes (for example wind stress related forcings and wave climate). The graphs show comparisons of tidal predictions, persistence model predictions (simple but working much better than tides in Texas) and Artificial Neural Network models (perform best overall). Click to access the real-time water level forecasts.

Access Artificial Neural Network Water Temperature Forecasts

Access Artificial Neural Network Water Temperature Forecasts Access Report and Model Development Information

Artificial Neural Network Water Temperature Forecasts

A joint project with the Coastal Conservation Association (CCA), Texas Parks and Wildlife and the Gulf Intracoastal Canal Association (GICA) to predict water temperatures in the Laguna Madre. Unusually low temperatures can result in substantial fish kills. A model predicting low water temperatures below about 45 F with about 24 hours notice can allow coastal stakeholders to take some measures to try to minimize the fish kills. The prediction model was developed to predict water temperatures in the middle and upper Laguna Madre. The Model can be accessed at the following link:

A set of models were implemented/developed to predict water levels as an aid to navigation in the coastal waters of the Gulf of Mexico and as a tool for emergency management. For the Texas coast Tidal predictions do not meet NOS acceptance criteria as other factors than tidal forcings dominate (for example wind stress related forcings and wave climate). The graphs show comparisons of tidal predictions, persistence model predictions (simple but working much better than tides in Texas) and Artificial Neural Network models (perform best overall). Click to access the real-time water level forecasts.

DNR/TCOON as well other observation networks provide an increasing amount of real-time data. As part of its research, DNR takes advantage of this information to develop and implement real-time predictive models. We have focused on using machine learning techniques such as artificial neural networks. These techniques are well suited for the modeling of non-linear processes when large data sets are available and also provide predictions virtually instantly once the models are trained. We typically focus on predictive models to help decision makers and anyone looking for coastal forecasts. The models are developed in collaboration with faculty members of the TAMUCC College of Science and Technology, the TAMUCC Center for Water Supply Studies (CWSS) and the Corpus Christi Weather Forecasting Office. Thank you to the sponsors who have made these models possible including the Texas General Land Office, the Texas Parks and Wildlife Department,the Coastal Conservation Association and the NOAA Seangrant and Seagrant Technology programs. If you have suggestions about existing or new models e-mail us or pass by our offices, we thoroughly enjoy the research and the related discussions.

DNR/TCOON as well other observation networks provide an increasing amount of real-time data. As part of its research, DNR takes advantage of this information to develop and implement real-time predictive models. We have focused on using machine learning techniques such as artificial neural networks. These techniques are well suited for the modeling of non-linear processes when large data sets are available and also provide predictions virtually instantly once the models are trained. Our focus is typically on predictive models which are automatically updated at least every hour and that help decision makers and anyone looking for coastal forecasts. The models are developed in collaboration with faculty members of the TAMUCC College of Science and Technology, the TAMUCC Center for Water Supply Studies (CWSS) and the Corpus Christi Weather Forecasting Office. Thank you to the sponsors who have made these models possible including the Texas General Land Office (Coastal Management Program), the Texas Parks and Wildlife Department,the Coastal Conservation Association and the NOAA Seangrant and Seagrant Technology programs.

A set of models predicting water levels as an aid to navigation in the coastal waters of the Gulf of Mexico and as a tool for emergency management. Several models have been developed including an Artifical Neural Network model which provides real-time water level forecasts and improves substantially over harmonic forecasts (tide tables).

A joint project with the Coastal Conservation Association (CCA), Texas Parks and Wildlife and the Gulf Intracoastal Canal Association (GICA) to predict water temperatures in the Laguna Madre. Unusually low temperatures can result in substantial fish kills. A model predicting low water temperatures below about 45 F with about 24 hours notice would allow coastal stakeholders to take some measures to try to minimize the fish kills. A prediction model was developed to predict water temperatures in the middle and upper Laguna Madre. The Model can be accessed at the following link:

We are always working on interesting models based on machine learning techniques such as Artificial Neural Network, Random Forest modeling and Rule Based systems to predict coastal and environmental variables. Models developed but yet to be implemented include the prediction of recreational water quality (enterococci colony forming units) and spring flows in a karst aquifer. If you would like more information on these models we would be glad to discuss our research.

DNR/TCOON as well other observation networks provide an increasing amount of real-time data. As part of its research, DNR aims at taking advantage of this growing information to develop and implement real-time predictive models. We have focused on using machine learning techniques such as artificial neural networks. These techniques are well suited for the modeling of non-linear processes when large data sets are available and also provide predictions virtually instantly once the models are trained. Our focus is typically on predictive models which are automatically updated at least every hour and that help decision makers and anyone looking for coastal forecasts. The models are developed in collaboration with faculty members of the TAMUCC College of Science and Technology, the TAMUCC Center for Water Supply Studies (CWSS) and the Corpus Christi Weather Forecasting Office. Thank you to the sponsors who have made these models possible including the Texas General Land Office (Coastal Management Program), the Texas Parks and Wildlife Department,the Coastal Conservation Association and the NOAA Seangrant and Seagrant Technology programs.

In collaboration with faculty members of the College of Science and Technology, the TAMUCC Center for Water Supply Studies (CWSS) and the Weather Forecasting Offices of Corpus Christi and Brownsville, DNR develops and implements forecasting techniques for coastal variables. The forecasting methodologies are based on Artificial Neural Networks (ANNs), statistical techniques, harmonic analysis and other techniques such as rule based decision systems. The main projects are listed below.

A main focus of DNR is the prediction of water levels as an aid to navigation in the coastal waters of the Gulf of Mexico and as a tool for emergency management. Several models have been developed including an Artifical Neural Network model which provides real-time water level forecasts and improves substantially over harmonic forecasts (tide tables). The different water level projects are listed below.

DNR and its partners have worked on prediction models using Artificial Neural Network and Rule Based systems for other coastal and environmental variables such as prediction of recreational water quality (enterococci colony forming units) and spring flows in a karst aquifer. If you would like more information on these models we woudl be glad to discuss our research.

About Our Forecasts

Interested in learning more about our forecasting models and methodology? Read it all here.

In collaboration with faculty members of the College of Science and Technology, the TAMUCC Center for Water Supply Studies (CWSS) and the Weather Forecasting Offices of Corpus Christi and Brownsville, DNR develops and implements forecasting techniques for coastal variables. The forecasting methodologies are based on Artificial Neural Networks (ANNs^?), statistical techniques, harmonic analysis and other techniques such as rule based decision systems. The main projects are listed below.

Water Level Forecasts

Water Levels & Wind Predictions at Bob Hall Pier

Artificial Neural Network and Persistence Water Level Forecasts

Test page for ANN water level forecasts at Bird Island Basin

For present water levels and historical water levels during hurricane, consult this other DNR webpage: http://lighthouse.tamucc.edu/Main/HurricaneAwareness

Statistically based water level forecasts and gap filling

Harmonic analysis and prediction of water levels

Wiki testing page for ANN models

Atmospheric Forecasts

As part of a collaboration with the Weather Forecasting Offices of Corpus Christi (CCWFO) and Brownsville (BWFO) DNR is archiving historical atmospheric predictions for various locations along the coast of Texas. The predictions are extracted from several NCEP models and are sent four times a day to DNR from CCWFO. These atmospheric predictions are used to compare the different model predictions with measurements and as input for other prediction models such as the computation of water level predictions.

Water Temperature Forecasts

http://lighthouse.tamucc.edu/Forecasts/WTPTests

Other Forecasting Projects

For more information on DNR forecasting projects, E-mail Philippe Tissot : ptissot@lighthouse.tamucc.edu

A joint project with the Coastal Conservation Association (CCA), Texas Parks and Wildlife and the Gulf Intracoastal Canal Association (GICA) to predict water temperatures in the Laguna Madre. Unusually low temperatures can result in substantial fish kills. A model predicting low water temperatures below about 45 F with about 24 hours notice would allow coastal stakeholders to take some measures to try to minimize the fish kills. A prediction model was developed to predict water temperatures in the middle and upper Laguna Madre. The Model can be accessed at the following link: http://lighthouse.tamucc.edu/Forecasts/WTPTests

A joint project with the Coastal Conservation Association (CCA), Texas Parks and Wildlife and the Gulf Intracoastal Canal Association (GICA) to predict water temperatures in the Laguna Madre. Unusually low temperatures can result in substantial fish kills. A model predicting low water temperatures below about 45 F with about 24 hours notice would allow coastal stakeholders to take some measures to try to minimize the fish kills. A prototype prediction model is being developed during the spring of 2006.

Artifical Neural Network and Persistence Water Level Forecasts

A joint project with the Coastal Conservation Association (CCA), Texas Parks and Wildlife and the Gulf Intracoastal Canal Association (GICA) to predict water temperatures in the Laguna Madre. Unusually low temperatures can result in substantial fish kills. A model predicting low water temperatures below about 45 F with about 24 hours notice would allow coastal stakeholders to take some measures to try to minimize the fish kills. A prototype prediction model is being developed during the spring of 2005.

Atmospheric Forecasts

Atmospheric Forecasts

DNR and its parners have worked on prediction models using Artifician Neural Network and Rule Based systems for other coastal and environmental variables such as prediction of recreational water quality (enterococci colony forming units) and spring flows in a karst aquifer. If you would like more information on these models we woudl be glad to discuss our research.

Harmonic analysis and prediction of water levels

[[ Artifical Neural Network and Persistence Water Level Forecasts

table width=100%^? Welcome to the DNR Water Level Forecasting Site

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UnderstandingWaterLevelObservationsAndForecastingMethods Are Water Level And Tide The Same Thing?^?

UnderstandingWaterLevelObservationsAndForecastingMethods Why Are The Present Forecasting Methods Inadequate For The Texas Coast?^?

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UnderstandingWaterLevelObservationsAndForecastingMethods Why Not Simply look at the NOAA Tides Information?^?

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table width=100%^?

MainWaterLevelForecastsPublications Main published references on ANN and persistent model forecasts methodology and performance^?

WhoMeasuresWaterLevelsAlongTheTexasCoast Who measures water levels along the Texas coast?^?

table width=100%^? cell width=45%^? How Do Advanced Forecasting Methods Work?

* IntroductionToNeuralNetworkForecasting Introduction to Neural Network forecasting^?

* NeuralApplied Neural Network as Applied to Water Level Forecasting^?

* NeuralCCBay Application of ANN to predict Water Levels in Corpus Christi Bay^?

* PersistenceModel Persistence Model^?

* ModelPerformance Performance Analysis^?

* SkillAssessment Skill Assessment Indices^?

* StormPrediction ANN Potential Application for Storm Prediction^?

Supplemental Information

http://dnr.cbi.tamucc.edu/wiki/Main/DNRPresentations DNR Presentations

http://dnr.cbi.tamucc.edu/wiki/Main/DNRPublications DNR Publications

NNTrailPage Trail Page^?

ForecastsSiteMap Forecasts Site Map^?

MainWaterLevelPubs Main published references on ANN and persistent model forecasts methodology and performance^?

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WhoMeasuresWaterLevelsAlongTheTexasCoast?

UnderstandingWaterLevelObservationsAndForecastingMethods Is Water Level And Tide The Same Thing?^?

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http://dnr.cbi.tamucc.edu/wiki/Main/DNRPublications DNR Publications

!Welcome to the DNR Water Level Forecasting Site

Welcome to the DNR Water Level Forecasting Site

* Supplemental Information http://dnr.cbi.tamucc.edu/wiki/Main/DNRPresentations DNR Presentations http://dnr.cbi.tamucc.edu/wiki/Main/DNRPublications DNR Publications

Supplemental Information

• http://dnr.cbi.tamucc.edu/wiki/Main/DNRPresentations DNR Presentations
• http://dnr.cbi.tamucc.edu/wiki/Main/DNRPublications DNR Publications

WhoMeasuresWaterLevelsAlongTheTexasCoast?

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* NeuralNetwork Introduction to Neural Network forecasting^?

WhoMeasuresWaterLevelsAlongtheTexasCoast?

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Who Measures Water Levels Along the Texas Coast?

AcessWaterLevelForecastsNow

AccessWaterLevelForecastsNow

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[[UnderstandingWaterLevelObservationsAndForecastingMethods Why Are The Present Forecasting Methods Inadequate For The Texas Coast?]

[[UnderstandingWaterLevelObservationsAndForecastingMethods Why Not Simply look at the NOAA Tides Information?]

[[UnderstandingWaterLevelObservationsAndForecastingMethods What are the Consequences of Inaccurate Forecasts?]

Why Are The Present Forecasting Methods Inadequate For The Texas Coast?

Why Not Simply look at the NOAA Tides Information?

What are the Consequences of Inaccurate Forecasts?

''' UnderstandingWaterLevelObservationsAndForecastingMethods Is Water Level And Tide The Same Thing?

''' UnderstandingWaterLevelObservationsAndForecastingMethods Is Water Level And Tide The Same Thing?^?

UnderstandingWaterLevelObservationsAndForecastingMethods Is Water Level And Tide The Same Thing?^?

Is Water Level And Tide The Same Thing?

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This site provides water level forecasting for SelectSites select sites^? along the Texas Coast through the application of a new Artificial Neural Network (ANN) forecasting system and a persistence model forecast.

Development of this website was sponsored in part by project partners the http://www.glo.state.tx.us/Texas General Land Office and http://www.noaa.gov/ National Oceanic and Atmospheric Administration through a http://www.glo.state.tx.us/coastal/cmp.html Texas Coastal Management Program grant. cell width=25%^? http://www.glo.state.tx.us/coastal/cmp.html DNRPub:/forecasts/cmpweb_3.png http://www.twdb.state.tx.us/ DNRPub:/forecasts/twdbweb_3.png http://www.glo.state.tx.us/ DNRPub:/forecasts/tgloweb_3.png cell width=25%^?

http://www.noaa.gov/ DNRPub:/forecasts/noaaweb_3.png http://www.nws.noaa.gov/ DNRPub:/forecasts/nwsweb_3.png http://www.usace.army.mil/ DNRPub:/forecasts/usaceweb_4.png tableend^?

Access Forecast Now

Click on a station in the map below to view harmonic and persistent model water level forecasts at that location. In some browsers, you can hover over a flag to determine which station you are pointing at.

Why Predict Water Levels Along the Texas Coast?

John F Kennedy Causeway, Corpus Christi, Post Fay Conditions (September 2002) Photograph Courtesy of TGLO

The knowledge of future water levels is important to coastal communities for a number of reasons including emergency management, engineering activities, safety, economic factors, and recreation. The prediction of the magnitude of anticipated coastal flooding can assist communities in avoiding dangerous low-lying regions or evacuating such regions in a timely fashion.

Gulf Intracoastal Waterway at Galveston Photo Courtesy of USACE

The ShippingIndustry shipping industry^? is particularly impacted by changes in water levels, since the amount of cargo carried into Texas ports depends on the depth of the waterways. For example, tankers potentially must unload a portion of their cargo before entering ship channels. Mariners in general and specifically commercial fishermen and anglers rely on water level forecasts for safe navigation in the shallow nearshore waters.

PageTwo More About Water Level Forecasting^?

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Development of this website was sponsored in part by project partners the http://www.glo.state.tx.us/Texas General Land Office and http://www.noaa.gov/ National Oceanic and Atmospheric Administration through a http://www.glo.state.tx.us/coastal/cmp.html Texas Coastal Management Program grant.

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http://www.noaa.gov/ DNRPub:/forecasts/noaaweb_3.png http://www.nws.noaa.gov/ DNRPub:/forecasts/nwsweb_3.png http://www.usace.army.mil/ DNRPub:/forecasts/usaceweb_4.png

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Access Forecast Now ForecastsAccess ^?

Access Water Level Forecasts Now

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'Why Predict Water Levels Along the Texas Coast?'

Project Partners include the http://www.glo.state.tx.us/Texas General Land Office and http://www.noaa.gov/ National Oceanic and Atmospheric Administration through a TGLO administered http://www.glo.state.tx.us/coastal/cmp.html Texas Coastal Management Program grant. || http://www.glo.state.tx.us/coastal/cmp.html DNRPub:/forecasts/cmpweb_3.png http://www.twdb.state.tx.us/ DNRPub:/forecasts/twdbweb_3.png http://www.glo.state.tx.us/ DNRPub:/forecasts/tgloweb_3.png ||http://www.noaa.gov/ DNRPub:/forecasts/noaaweb_3.png http://www.nws.noaa.gov/ DNRPub:/forecasts/nwsweb_3.png http://www.usace.army.mil/ DNRPub:/forecasts/usaceweb_4.png ||

Project Partners include the http://www.glo.state.tx.us/Texas General Land Office and http://www.noaa.gov/ National Oceanic and Atmospheric Administration through a TGLO administered http://www.glo.state.tx.us/coastal/cmp.html Texas Coastal Management Program grant. || http://www.glo.state.tx.us/coastal/cmp.html DNRPub:/forecasts/cmpweb_3.png http://www.twdb.state.tx.us/ DNRPub:/forecasts/twdbweb_3.png http://www.glo.state.tx.us/ DNRPub:/forecasts/tgloweb_3.png ||http://www.noaa.gov/ DNRPub:/forecasts/noaaweb_3.png http://www.nws.noaa.gov/ DNRPub:/forecasts/nwsweb_3.png http://www.usace.army.mil/ DNRPub:/forecasts/usaceweb_4.png ||

This site provides water level forecasting for SelectSites select sites^? along the Texas Coast through the application of a new Artificial Neural Network (ANN) forecasting system and a persistence model forecast.

Project sponsors include:

Is Water Level the Same Thing as Tide?

Yes and No. NeuralNetworkTidePredictions Tides^? are defined as the alternating rise and fall of sea level with respect to land, as influenced by the gravitational attraction of the moon and sun. In certain locations where water elevation is predominately dictated by AstronomicalFactors astronomical factors^? water level and tide are often used interchangeably. In other regions like south Texas where the average tidal range is minimal (< 1 ft) other natural forces such as wind, shoreline orientation, topography, and bathymetry dominate over the astronomical forces then the observed water level is not the same as the predicted tide influenced water level. The combination of the tide and other influences can be referred to as the total water level fluctuation.

Why Not Simply look at the NOAA Tides Information?

http://co-ops.nos.noaa.gov/restles1.html#Intro National Oceanic and Atmospheric Administration tide forecasts are predicted based on the periodicity of astronomical factors (the respective motion of the moon and the sun). NOAA has stated that "presently published predictions do not meet working standards" when assessing the performance of current tidal predictions, tides being closely related to water level predictions, for regular weather conditions in Aransas Pass and Corpus Christi Bay [cite]. Water level forecasting is complicated by meteorological influences (in particular wind forcing) and is often further complicated by the unique shoreline orientation and shallow water depth associated with the bays and lagoon system that exists along the Texas Gulf Coast. For example, strong winds have been observed to drive water out of the shallow lower Laguna Madre resulting in a lower than predicted water level or in contrast pile water up along the shore resulting in an increased observed water level diagram^?. Differences in predicted verses observed water levels could be on the order of 1 to 2 feet along Texas shorelines. Such dramatic differences are typically observed under the action of two opposing wind regimes; a) winds directed out of the southeast - dominating greater than 50% of the time or b) winds directed out of the north - strong pulses of energy associated with frontal passage.

Why Are The Present Forecasting Methods Inadequate For the Texas Coast?

Water levels are typically predicted using tide tables computed based on the gravitational influence of celestial bodies PresentModels ^?. While this method of prediction is adequate for most coastlines, along the Gulf of Mexico meteorological effects can significantly influence the observed water level and make the tide tables ineffective. Although astronomical forcing is very predictable meteorological influences are not periodic and are difficult to predict for periods longer than 1 or 2 days. The influence of meteorological effects, however, is frequently stronger than the influence from the celestial bodies. For example, the Corpus Christi, Texas, airport is ranked by the National Weather Service as the third windiest in the U.S. based on a multiannual average wind speed of 23.5 kph [NeuralNetReferences(Smith 1978)].

Until recently it was not possible to make accurate water level predictions for the Texas coast. This is because the PresentModels do not include both meteorological and astronomical effects. For example, a fourteen-day comparison between measured water levels and tidal forecasts for the Port Aransas station is shown below. The tide station is located in the Corpus Christi ship channel near Port Aransas, Texas. The difference between tidal forecasts and actual water level can be larger than 1 foot (~31 cm) for several consecutive days. A predicted difference of 1 foot is particularly significant because of the small tidal range (< 1 ft) observed along the Texas Coast.

Predicted (red) vs. observed (blue) tides at Port Aransas, Texas

How Does the Artificial Neural Network (ANN) Produce a Better Water Level Forecast?

• Takes meteorological parameters into account
• Non linear modeling capability (Allows modeling of complex systems consisting of reciprocal relationships or feedback loops)
• Ability to learn dynamically
• Robustness to noisy data

What are the Consequences of Inaccurate Forecasts?

The inability of PresentModels to accurately predict water level fluctuations can result in severe consequences, such as ship groundings. In an effort to improve information required for safe navigation NOAA established the http://www.co-ops.nos.noaa.gov/d_ports.html Physical Oceanographic Real-Time System (PORTS). Although the PORTS system is beneficial to navigators in the Galveston area it is not available at other Texas ports, thus the ANN water level forecasts will fill this gap for navigators. In addition to navigation hazards present forecasting inaccuracies impair the management of roadways and low-lying coastal regions during presently unanticipated increases in water levels.

Who Measures Water Levels Along the Texas Coast?

The Texas Coastal Ocean Observation Network (http://dnr.cbi.tamucc.edu/wiki/TCOON/HomePage TCOON) has forty-two stations, including seven long-term stations established and operated by NOS as part of its National Water Level Observation Network. TCOON follows NOS guidance to obtain reliable water level data. Most stations provide additional data such as wind speed and direction, air temperature, and water temperature, and some stations provide water current, salinity, pH, and dissolved oxygen data.

The TCOON database was a source of input data for the ANN water level forecasts. The database provided water level observations, barometric pressure, as well as wind speed and direction.

How Advanced Forecasting Methods Work

• NeuralNetwork Introduction to Neural Network forecasting^?
• NeuralApplied Neural Network as Applied to Water Level Forecasting^?
• NeuralCCBay Application of ANN to predict Water Levels in Corpus Christi Bay^?
• PersistenceModel Persistence Model^?
• ModelPerformance Performance Analysis^?
• StormPrediction ANN Potential Application for Storm Prediction^?

Access DNR Harmonic Forecasts

• ForecastsHarmonic DNR Harmonic Forecasts^?

Access TCOON Water Level Observations

• NeuralWaterLevelObservations Water Level Observations^?

DNR Publications and Presentations

• NeuralNetworkPublications Publications and Presentations^?

Other Helpful Links

• MesoETA MesoETA Forecasts^?
• NeuralTerms Helpful Terms and Definitions^?
• NeuralNetworkResults Preliminary neural network results^?
• NeuralNetworkNotes Notes for this page...please comment on page progress here^?
• NeuralNetPhotos Photographs and Images^?
• NeuralNetForecast Access Forecast Now^?
• NeuralNetSponsors Project Sponsors^?
• NeuralNetReferences References^?

Related Links

• [[NeuralNetworkTidePredictions Gen

Is Water Level and Tide the Same Thing?

The knowledge of future water levels is important to coastal communities for a number of reasons including emergency management, engineering activities, safety, economic factors, and recreation. The prediction of the magnitude of anticipated coastal flooding can assist communities in avoiding dangerous low-lying regions or evacuating such regions in a timely fashion.

The ShippingIndustry shipping industry^? is particularly impacted by changes in water levels, since the amount of cargo carried into Texas ports depends on the depth of the waterways. For example, tankers potentially must unload a portion of their cargo before entering ship channels. Mariners in general and specifically commercial fishermen and anglers rely on water level forecasts for safe navigation in the shallow nearshore waters.

Yes and No. NeuralNetworkTidePredictions Tides^? are defined as the alternating rise and fall of sea level with respect to land, as influenced by the gravitational attraction of the moon and sun. In certain locations where water elevation is predominately dictated by AstronomicalFactors astronomical factors^? water level and tide are often used interchangeably. In other regions like south Texas where the average tidal range is minimal (< 1 ft) other natural forces such as wind, shoreline orientation,

Water levels are typically predicted using tide tables computed based on the gravitational influence of celestial bodies PresentModels ^?. While this method of prediction is adequate for most coastlines, along the Gulf of Mexico meteorological effects can significantly influence the observed water level and make the tide tables ineffective. Although astronomical forcing is very predictable meteorological influences are not periodic and are difficult to predict for periods longer than 1 or 2 days. The influence of meteorological effects, however, is frequently stronger than the influence from the celestial bodies. For example, the Corpus Christi, Texas, airport is ranked by the National Weather Service as the third windiest in the U.S. based on a multiannual average wind speed of 23.5 kph [NeuralNetReferences(Smith 1978)].

Until recently it was not possible to make accurate water level predictions for the Texas coast. This is because the PresentModels do not include both meteorological and astronomical effects. For example, a fourteen-day comparison between measured water levels and tidal forecasts for the Port Aransas station is shown below. The tide station is located in the Corpus Christi ship channel near Port Aransas, Texas. The difference between tidal forecasts and actual water level can be larger than 1 foot (~31 cm) for several consecutive days. A predicted difference of 1 foot is particularly significant because of the small tidal range (< 1 ft) observed along the Texas Coast.

The inability of PresentModels to accurately predict water level fluctuations can result in severe consequences, such as ship groundings. In an effort to improve information required for safe navigation NOAA established the http://www.co-ops.nos.noaa.gov/d_ports.html Physical Oceanographic Real-Time System (PORTS). Although the PORTS system is beneficial to navigators in the Galveston area it is not available at other Texas ports, thus the ANN water level forecasts will fill this gap for navigators. In addition to navigation hazards present forecasting inaccuracies impair the management of roadways and low-lying coastal regions during presently unanticipated increases in water levels.

Yes and No. NeuralNetworkTidePredictions Tides^? are defined as the alternating rise and fall of sea level with respect to land, as influenced by the gravitational attraction of the moon and sun. In certain locations where water elevation is predominately dictated by AstronomicalFactors astronomical factors^? water level and tide are often used interchangebly. In other regions like south Texas where the average tidal range is minimal (< 1 ft) other natural forces such as wind, shoreline orientation,

http://co-ops.nos.noaa.gov/restles1.html#Intro National Oceanic and Atmospheric Administration tide forecasts are predicted based on the periodicity of astronomical factors (the respective motion of the moon and the sun). NOAA has stated that "presently published predictions do not meet working standards" when assessing the performance of current tidal predictions, tides being closely related to water level predictions, for regular weather conditions in Aransas Pass and Corpus Christi Bay [cite]. Water level forecasting is complicated by meteorological influences (in particular wind forcing) and is often further complicated by the unique shoreline orientation and shallow water depth associated with the bays and lagoon system that exists along the Texas Gulf Coast. For example, strong winds have been observed to drive water out of the shallow lower Laguna Madre resulting in a lower than predicted water level or in contrast pile water up along the shore resulting in an increased observed water level diagram^?. Differences in predicted verses observed water levels can be on the order of 1 to 2 feet along Texas shorelines. Such dramatic differences are typically observed under the action of two opposing wind regimes; a) winds directed out of the southeast - dominating greater than 50% of the time or b) winds directed out of the north - strong pulses of energy associated with frontal passage.

Water levels are typically predicted using tide tables computed based on the gravitational influence of celestial bodies PresentModels ^?. While this method of prediction is adequate for most coastlines, along the Gulf of Mexico meteorological effects can significantly influence the observed water level and make the tide tables ineffective. Although astronomical forcing is very predictable meteorological influences are not periodic and are difficult to predict for periods longer than 1 or 2 days. The influence of meteorological effects, however, is frequently stronger than the influence from the celestial bodies. For example, the Corpus Christi,Texas, airport is ranked by the National Weather Service as the third windiest in the U.S. based on a multiannual average wind speed of 23.5 kph [NeuralNetReferences(Smith 1978)].

• Non linear modeling capablity (Allows modeling of complex systems consisting of reciprocal relationships or feedback loops)

Are Water Level and Tide the Same Thing?

Why Does the Artificial Neural Network (ANN) Produce a Better Water Level Forecast?

Predicted vs. observed tides at Port Aransas, Texas

Click on a station in the map below to view current water level at that location. In some browsers, you can hover over a flag to determine which station you are pointing at.

Welcome to the DNR Artificial Neural Network Water Level Forecasting Site

This site provides water level forecasting for SelectSites select sites^? along the Texas Coast through the application of a new Artificial Neural Network (ANN) forecasting system.

• NeuralNetworkTidePredictions General Information on Tides and Traditional Methods of Tide Prediction^?

HyperlinkNote A Note About Hyperlinks^?

The research and development of this ANN water level forecasting system was sponsored by the http://www.glo.state.tx.us/landoffice.html Texas General Land Office and the http://www.noaa.gov/ National Oceanic and Atmospheric Association through a http://www.glo.state.tx.us/coastal/cmp.html Texas Coastal Management Program grant.

Project Details

• ForecastsHarmonic DNR Harmonic Forecasts^?

• NeuralNetworkPublications Publications and Presentations^?

HyperlinkNote A Note About Hyperlinks^?

A Note About Hyperlinks

• NeuralNetworkTidePredictions General Information on Tides and Traditional Methods of Tide Prediction^?

Under construction.

Welcome to the DNR Water Level Forecasting Site

This site provides water level forecasting for SelectSites select sites^? along the Texas Coast through the application of a unique Neural Network forecasting system.

Access Forecast Now ForecastsAccess ^?

018: Port Isabel  (26°  3' 40" N, 97° 12' 55" W)
Primary Water Level: 1.45 ft (0.442 m) above Mean Sea Level  00h12m ago
Air Temperature: 69.3 degF (20.7 degC)  00h12m ago
Water Temperature: 71.1 degF (21.7 degC)  00h12m ago
Wind Speed: 7.4 mph (3.300 m/s)  00h12m ago
Wind Gusts: 9.4 mph (4.200 m/s)  00h12m ago
Wind Direction: 36 degfromN  00h12m ago
Barometric Pressure: 29.94 in Hg (1014.0 millibars)  00h12m ago 009: Port Aransas  (27° 50' 23" N, 97°  4' 21" W)
Primary Water Level: 1.62 ft (0.493 m) above Mean Sea Level  00h12m ago
Air Temperature: 62.4 degF (16.9 degC)  00h12m ago
Wind Speed: 7.6 mph (3.400 m/s)  00h12m ago
Wind Gusts: 11.4 mph (5.100 m/s)  00h12m ago
Wind Direction: 346 degfromN  00h12m ago 057: Port O'Connor  (28° 26' 45" N, 96° 23' 45" W)
Primary Water Level: 1.30 ft (0.396 m) above Mean Sea Level  00h12m ago
Water Temperature: 66.4 degF (19.1 degC)  00h12m ago
Wind Speed: 17.2 mph (7.700 m/s)  00h12m ago
Wind Gusts: 20.4 mph (9.100 m/s)  00h12m ago
Wind Direction: 349 degfromN  00h12m ago 021: Galveston Pleasure Pier  (29° 17'  6" N, 94° 47' 17" W)
Primary Water Level: 1.10 ft (0.335 m) above Mean Sea Level  00h12m ago
Air Temperature: 56.7 degF (13.7 degC)  00h12m ago
Water Temperature: 70.0 degF (21.1 degC)  00h12m ago
Wind Speed: 9.6 mph (4.300 m/s)  00h12m ago
Wind Gusts: 18.3 mph (8.200 m/s)  00h12m ago
Wind Direction: 325 degfromN  00h12m ago
Barometric Pressure: 29.87 in Hg (1011.4 millibars)  00h12m ago 016: Sabine Pass  (29° 43' 47" N, 93° 52' 15" W)
Primary Water Level: 1.16 ft (0.355 m) above Mean Sea Level  00h12m ago
Air Temperature: 54.7 degF (12.6 degC)  00h12m ago
Water Temperature: 61.3 degF (16.3 degC)  00h12m ago
Wind Speed: 17.2 mph (7.700 m/s)  00h12m ago
Wind Gusts: 21.5 mph (9.600 m/s)  00h12m ago
Wind Direction: 323 degfromN  00h12m ago
Barometric Pressure: 29.92 in Hg (1013.1 millibars)  00h12m ago

Who Uses Water Level Forecasts?

The knowledge of future water levels is important to coastal communities for a number of reasons including emergency management, safety, economic factors, and recreation. The ShippingIndustry shipping industry^? is particularly impacted by changes in water levels, since the amount of cargo carried into Texas ports depends on the depth of the waterways. Mariners in general and specifically commercial fishermen and anglers rely on water level forecasts for safe navigation in the shallow nearshore waters.

Why Not Simply look at the NOAA Tides Information?

http://co-ops.nos.noaa.gov/restles1.html#Intro National Oceanic and Atmospheric Administration tide forecasts are predicted based on the periodicity of astronomical factors. Water level forecasting is complicated by meteorological influences (in particular wind forcing) and is often further complicated by the unique shoreline orientation and shallow water depth associated with the bays and lagoon system that exists along the Texas Gulf Coast. For example, strong winds have been observed to drive water out of the shallow lower Laguna Madre resulting in a lower than predicted water level or in contrast pile water up along the shore resulting in an increased observed water level diagram^?. Differences in predicted verses observed water levels can be on the order of 1 to 2 feet along Texas shorelines. Such dramatic differences are typically observed under the action of two opposing wind regimes; a) winds directed out of the southeast - dominating greater than 50% of the time or b) winds directed out of the north - strong pulses of energy associated with frontal passage.

Why Are The Present Forecasting Methods Inadequate For the Texas Coast?

Water levels are typically predicted using tide tables computed based on the gravitational influence of celestial bodies. While this method of prediction is adequate for most coastlines, along the Gulf of Mexico meteorological effects can significantly influence the observed water level and make the tide tables ineffective. Although astronomical forcing is very predictable the meteorological influences are not periodic and are difficult to predict for periods longer than 1 or 2 days. The influence of meteorological effects, however, is frequently stronger than the influence from the celestial bodies. At this time it is not possible to make accurate water level predictions for the Texas coast. This is because the PresentModels Present Models^? do not include both meteorological and astronomical effects. For example, a fourteen-day comparison between measured water levels and tidal forecasts for the Port Aransas station is shown below. The tide station is located in the Corpus Christi ship channel near Port Aransas, Texas. The difference between tidal forecasts and actual water level can be larger than 1 foot (~31 cm) for several consecutive days. A predicted difference of 1 foot is particularly significant because of the small tidal range (< 1 ft) observed along the Texas Coast.

http://dnr.cbi.tamucc.edu/~dnrwiki/images/predvsobs_1.wmf

• NeuralNetwork Introduction to Neural Network forecasting^?
• NeuralApplied Neural Network as Applied to Water Level Forecasting^?
• NeuralNetworkResults Preliminary neural network results^?
• NeuralNetworkNotes Notes for this page...please comment on page progress here^?

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21 Nov 2009 23:18 UTC 2009325+2318 UTC

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009: Port Aransas  (27° 50' 23" N, 97°  4' 21" W)
Primary Water Level: 1.62 ft (0.493 m) above Mean Sea Level  00h12m ago
Air Temperature: 62.4 degF (16.9 degC)  00h12m ago
Wind Speed: 7.6 mph (3.400 m/s)  00h12m ago
Wind Gusts: 11.4 mph (5.100 m/s)  00h12m ago
Wind Direction: 346 degfromN  00h12m ago

Page last modified on May 15, 2009, at 10:09 AM

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009: Port Aransas  (27° 50' 23" N, 97°  4' 21" W)
Primary Water Level: 1.62 ft (0.493 m) above Mean Sea Level  00h12m ago
Air Temperature: 62.4 degF (16.9 degC)  00h12m ago
Wind Speed: 7.6 mph (3.400 m/s)  00h12m ago
Wind Gusts: 11.4 mph (5.100 m/s)  00h12m ago
Wind Direction: 346 degfromN  00h12m ago