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Coastal Visualization Tools. The Department of Computing and Mathematical Sciences at A&M-CC is developing a Scientific Visualization Laboratory to support CBI-DNR activities. This laboratory will provide facilities for interactive visualization and investigation of the multidimensional multivariate data gathered by the TCOON system.
Coastal Visualization Tools. The Department of Computing and Mathematical Sciences at A&M-CC is developing a Scientific Visualization Laboratory to support DNR? activities. This laboratory will provide facilities for interactive visualization and investigation of the multidimensional multivariate data gathered by the TCOON system.
Additional Sensor Packages. CBI is collaborating with many groups to expand the types of data collected from environmental platforms in the Gulf of Mexico. CBI is presently developing a mobile high-frequency (HF) radar unit capable of collecting real-time measurements of surface circulation patterns, wave height, wave direction, wave period, and wind direction over large areas of water. Observations from the HF radar unit are presently being integrated into the http://www.cbi.tamucc.edu/projects/hfradar online Web site, providing unprecedented capabilities for oil-spill response, marine safety, and environmental science. CBI is also working to add sensors based on new technologies for performing in situ measurement and analysis of directional wave profiles, particle size and distribution, turbidity, nutrients, and water quality.
Short-Term Prediction using Neural Networks. CBI-DNR is collaborating with the Department of Computing and Mathematical Sciences at A&M-CC and researchers at Texas A&M University (College Station) to produce neural-network-based models for short-term prediction of water level and currents along the Texas Gulf coast. The new models will use the real-time and historical observations from TCOON coupled with wind forecasts from the National Weather service to predict water elevations and currents in a 1 to 30-hour time horizon. Short-term predictions such as these are needed for navigation, oil spill response, and marine operations. Preliminary results have indicated that for Texas coastal waters a neural-network-based model can significantly outperform forecasts of water levels based on traditional harmonic analysis methods. CBI-DNR is also investigating the use of neural networks to make real-time predictions of storm surge and other impacts associated with tropical storms. Short-Term Prediction using Neural Networks. DNR? is collaborating with the Department of Computing and Mathematical Sciences at A&M-CC and researchers at Texas A&M University (College Station) to produce neural-network-based models for short-term prediction of water level and currents along the Texas Gulf coast. The new models will use the real-time and historical observations from TCOON coupled with wind forecasts from the National Weather service to predict water elevations and currents in a 1 to 30-hour time horizon. Short-term predictions such as these are needed for navigation, oil spill response, and marine operations. Preliminary results have indicated that for Texas coastal waters a neural-network-based model can significantly outperform forecasts of water levels based on traditional harmonic analysis methods. DNR? is also investigating the use of neural networks to make real-time predictions of storm surge and other impacts associated with tropical storms. Data from Other Observation Networks. CBI-DNR is presently looking to expand its data management system to include environmental observation networks from other states and organizations. Preliminary efforts to integrate data from the http://www.gerg.tamu.edu/tglo Texas Automated Buoy System and the http://comps.marine.usf.edu University of Southern Florida's Coastal Ocean Monitoring and Prediction System have been successful. A centralized data management system that can accommodate multiple observation networks will reduce the need to redevelop systems such as TCOON and allow better communication among the producers and consumers of environmental data.
Data from Other Observation Networks. DNR? is presently looking to expand its data management system to include environmental observation networks from other states and organizations. Preliminary efforts to integrate data from the http://www.gerg.tamu.edu/tglo Texas Automated Buoy System and the http://comps.marine.usf.edu University of Southern Florida's Coastal Ocean Monitoring and Prediction System have been successful. A centralized data management system that can accommodate multiple observation networks will reduce the need to redevelop systems such as TCOON and allow better communication among the producers and consumers of environmental data.
Short-Term Prediction using Neural Networks. CBI-DNR is collaborating with the Department of Computing and Mathematical Sciences at A&M-CC and researchers at Texas A&M University (College Station) to produce neural-network-based models for short-term prediction of water level and currents along the Texas Gulf coast. The new models will use the real-time and historical observations from TCOON coupled with wind forecasts from the National Weather service to predict water elevations and currents in a 1 to 30-hour time horizon. Short-term predictions such as these are needed for navigation, oil spill response, and marine operations. Preliminary results have indicated that for Texas coastal waters a neural-network-based model can significantly outperform forecasts of water levels based on traditional harmonic analysis methods. CBI-DNR is also investigating the use of neural networks to make real-time predictions of storm surge and other impacts associated with tropical storms.
Short-Term Prediction using Neural Networks. CBI-DNR is collaborating with the Department of Computing and Mathematical Sciences at A&M-CC and researchers at Texas A&M University (College Station) to produce neural-network-based models for short-term prediction of water level and currents along the Texas Gulf coast. The new models will use the real-time and historical observations from TCOON coupled with wind forecasts from the National Weather service to predict water elevations and currents in a 1 to 30-hour time horizon. Short-term predictions such as these are needed for navigation, oil spill response, and marine operations. Preliminary results have indicated that for Texas coastal waters a neural-network-based model can significantly outperform forecasts of water levels based on traditional harmonic analysis methods. CBI-DNR is also investigating the use of neural networks to make real-time predictions of storm surge and other impacts associated with tropical storms. Short-Term Prediction using Neural Networks. CBI-DNR is collaborating with the Department of Computing and Mathematical Sciences at A&M-CC and researchers at Texas A&M University (College Station) to produce neural-network-based models for short-term prediction of water level and currents along the Texas Gulf coast. The new models will use the real-time and historical observations from TCOON coupled with wind forecasts from the National Weather service to predict water elevations and currents in a 1 to 30-hour time horizon. Short-term predictions such as these are needed for navigation, oil spill response, and marine operations. Preliminary results have indicated that for Texas coastal waters a neural-network-based model can significantly outperform forecasts of water levels based on traditional harmonic analysis methods. CBI-DNR is also investigating the use of neural networks to make real-time predictions of storm surge and other impacts associated with tropical storms.
Describe FutureDirections here.
Additional Sensor Packages. CBI is collaborating with many groups to expand the types of data collected from environmental platforms in the Gulf of Mexico. CBI is presently developing a mobile high-frequency (HF) radar unit capable of collecting real-time measurements of surface circulation patterns, wave height, wave direction, wave period, and wind direction over large areas of water. Observations from the HF radar unit are presently being integrated into the http://www.cbi.tamucc.edu/projects/hfradar online Web site, providing unprecedented capabilities for oil-spill response, marine safety, and environmental science. CBI is also working to add sensors based on new technologies for performing in situ measurement and analysis of directional wave profiles, particle size and distribution, turbidity, nutrients, and water quality.
Coastal Visualization Tools. The Department of Computing and Mathematical Sciences at A&M-CC is developing a Scientific Visualization Laboratory to support CBI-DNR activities. This laboratory will provide facilities for interactive visualization and investigation of the multidimensional multivariate data gathered by the TCOON system.
Data from Other Observation Networks. CBI-DNR is presently looking to expand its data management system to include environmental observation networks from other states and organizations. Preliminary efforts to integrate data from the http://www.gerg.tamu.edu/tglo Texas Automated Buoy System and the http://comps.marine.usf.edu University of Southern Florida's Coastal Ocean Monitoring and Prediction System have been successful. A centralized data management system that can accommodate multiple observation networks will reduce the need to redevelop systems such as TCOON and allow better communication among the producers and consumers of environmental data.
Texas A&M University-Corpus Christi