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. 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). 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. |      
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Artificial 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 was undertaken. 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:Access Artificial Neural Network Water Temperature Forecasts
Access Report and Model Development Information
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 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. Access more information about the thunderstorm 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 or have suggestions for improving our present models let us know.
For more information on DNR forecasting projects, E-mail Philippe Tissot : ptissot@lighthouse.tamucc.edu
Interested in learning more about our forecasting models and methodology? Read it all here.
Texas A&M University-Corpus Christi
