News: Enhancing Storm Surge Visualization

2017/10/04 – Science Node
The aim of storm surge models: When a storm approaches, emergency managers want fast and accurate forecasts

Although our model provides water level predictions from the deep ocean all the way to the coastal floodplains, the system is limited by the model’s resolution. Topographic features at scales smaller than 500 feet, such as roadways or narrow stream channels, are often not included in the models because of the computer time needed to produce such high-resolution outputs. Because of this limitation, the extent of flooding can be underpredicted by the model.

2017/08/31 – National Consortium for Data Science
Data Fellows project aims to make storm surge predictions faster and more accurate

Continuing our North Carolina Sea Grant project and with new support from the National Consortium of Data Science, we are developing a method to improve prediction of the true flooding extent by combining the results of our model with more accurate elevation datasets.

To perform this prediction of the flooding extent, we use a Geographic Information System (GIS) called GRASS GIS that specializes in processing very large amounts of data. The project has two major objectives. The first is to process the modeled water levels and the elevation data set together, producing a map showing the extent of predicted flooding. When the modeled water levels are greater than the land elevation, flooding extends outward into neighboring, unflooded areas in the data set. By mapping the model results to the higher resolution data sets on elevation, we can create more accurate surge forecasts of overland flooding.

2017/08/08 – NC Sea Grant Coastwatch Currents
Fast, Accurate Forecasts of Coastal Flooding: Enhancing Visualization of Storm Surge Guidance to Support Emergency Managers

ncsgStorm surge models must be both fast and accurate to give coastal communities the guidance they need to prepare for and respond to a storm. Perhaps just as important is the need for these forecasts to be visualized in a way that is meaningful and useable by emergency managers.

ADCIRC forecasts are currently visualized using Kalpana, a Python script that converts the model output into formats compatible with commonly-used visualization applications such as ArcGIS and Google Earth. With support from the National Consortium of Data Science (NCDS) and in partnership with North Carolina Emergency Management (NCEM), our team has developed a new visualization method that makes use of enhanced topographic resolution along the flooding boundary. This results in modeled storm surge extending farther into estuaries and floodplains, increasing the accuracy of the forecast.

News: Hurricane Matthew

2016/10/17 – DHS CRCoE
Coastal Resilience Center researchers, partners aid in Hurricane Matthew preparation and recovery


Dr. Casey Dietrich of NCSU, whose CRC project focuses on improving the speed of ADCIRC modeling, visited the North Carolina Emergency Management (NCEM) State Emergency Operations Center to see NCEM’s operation and workflow during storm response. Dietrich said emergency managers were excited about the data provided by ADCIRC predictions.

“They are using both the CERA site and the shapefiles we are generating,” Dietrich said. “The shapefiles are being combined manually with other datasets to determine the potential flood damages, in terms of both number and cost of buildings and infrastructure.”

Dietrich said that ADCIRC predictions have compared favorably to post-storm high-water marks and U.S. Geological Survey measurements of storm surge.

“Their comparisons after Hermine showed matches within a foot to the peak water levels,” Dietrich said. “They described ADCIRC as their eyes on the coast.”

Dietrich’s work with ADCIRC to provide more accurate storm surge estimates for North Carolina is also partially funded by the North Carolina Sea Grant and the National Consortium for Data Science.

The CERA website is used during Hurricane Matthew preparations at the NCEM Emergency Operations Center.

The CERA website is used during Hurricane Matthew preparations at the NCEM Emergency Operations Center.

2016/10/06 – CCEE
Dietrich Aiding Efforts to Forecast Flooding during Hurricane Matthew


As Hurricane Matthew approaches Florida and prepares to move up the U.S. east coast, researchers in North Carolina are running models to forecast the storm surge and coastal flooding. Dr. Casey Dietrich is working with collaborators at the University of North Carolina, the Renaissance Computing Institute, and Seahorse Coastal Consulting to generate and share guidance during the storm. The models are run every 6 hours, and they provide high-resolution forecasts of possible flooding throughout the NC coast. The forecasts can be found at: Dietrich is providing forecast guidance to NC Emergency Management, for use in decisions about evacuation and resource deployment. This real-time forecasting is part of a research project to downscale the model results and provide them in formats tailored to the needs of emergency managers.

Forecast of coastal flooding due to Hurricane Matthew (2016).

Forecast of coastal flooding due to Hurricane Matthew (2016).

News: Developing Storm Surge Visualization

2015/03/10 – CCEE
Developing Storm Surge Visualization


When tropical storms approach, local, state and federal emergency managers seek predictions of storm surge and coastal flooding. In a project supported by NC Sea Grant, Dr. Casey Dietrich and Ph.D. student Rosemary Cyriac are improving the dissemination of flooding predictions to end-users by producing predictions in popular file formats. The Coastal Emergency Risk Assessment (CERA, provides a Web-based interface for visualizing surge predictions from computer models. Dr. Dietrich’s team is working with emergency managers in North Carolina’s coastal counties and with other decision makers. Results from daily model simulations are sent to these individuals, and they are widely used to predict inundation and flooding levels. Such predictions are also needed for engineering design and evacuation decisions. Model outputs are converted into formats compatible with commonly used visualization software, such as ArcGIS and Google Earth. By providing predictions to local emergency managers in a useful format, the information can be more easily integrated with other data, thereby making the information more accessible to those who most need it.

News: GoMRI Renews CARTHE

2014/11/15 – GoMRI
Gulf of Mexico Research Initiative Awards $140 Million to Support Research


The Gulf of Mexico Research Initiative (GoMRI) has selected 12 research consortia to conduct scientific studies of the impacts of oil, dispersed oil, and dispersant on the Gulf of Mexico ecosystem and public health. These research investments focus on improving our fundamental understanding of the implications of events such as the Macondo well blowout, and on developing improved spill mitigation, oil and gas detection, characterization and remediation technologies. The consortia were chosen following a competitive, merit review process that evaluated research applications submitted to GoMRI in response to its RFP-IV program solicitation.

Through the RFP-IV program, GoMRI is awarding $140 million to support research to be carried out from 2015 through 2017.

2014/11/18 – UM RSMAS
UM Rosenstiel School Scientists Receive Over $29 Million to Study Effects of Crude Oil Spills


Under the leadership of UM Rosenstiel School Professor Tamay Özgökmen, the CARTHE research consortium received over $20 million to continue the research necessary to predict the fate of oil released into the environment to help inform and guide response teams in the event of future oil spills. This second phase of CARTHE, which supports research through 2017, will help scientists develop and improve computational tools to accurately predict the fate of hydrocarbons found in crude oil that are released into the environment, and help to guide risk management and response efforts in mitigation and restoration of the economy and the ecosystem in situations like the Deepwater Horizon oil spill.

“An integral part of any informed response to a future event like the 2010 Deepwater Horizon oil spill requires knowledge of the distribution of pollutants in the water column and the ability to predict where and how fast the pollutants will spread,” said Özgökmen, lead investigator of CARTHE. “This information is also crucial to estimate the pollutants, impact on the local ecosystem and coastal communities.”

News: Developing Storm Surge Visualization

2014/10/29 – NC Sea Grant Coastwatch Currents
Picture This: Developing Storm Surge Visualization


When a tropical storm or hurricane develops in the open ocean, the National Hurricane Center, known as NHC, issues advisories that anticipate the track and intensity of the wind field. These advisories predict when and where the hurricane is expected to make landfall, even when the storm is far away from the coast.

This information serves as an input for the ocean model, which then predicts the water levels or storm surges, and wave heights created by these winds at various locations along our coastline for the coming days. These results will convey a greater meaning to the end user when visualized properly. The chief objective for our project is to improve the communication of these model outputs to the end-user by producing them in popular file formats like that of GIS based shapefiles and KMZ files used in Google Earth.

News: Sea Change

2014/05/28 – NC State Engineering Magazine
Working with one of North Carolina’s most valuable resources


Dietrich came to NC State with a wealth of experience in coastal modeling, most of it done along the Gulf Coast of the United States. He will soon begin a project with the National Oceanic and Atmospheric Administration to model North Carolina’s coast. Dietrich is part of a research community using a computer model called ADCIRC to predict everything from storm surge and flooding to the feasibility of dredging, or where material floating in the ocean might end up.

When a hurricane is bearing down on the coastline, running the models quickly is of the essence. But the faster the model runs, the less accurate it is. Improving the models so that the time and accuracy trade-off isn’t as sharp is part of Dietrich’s work.

So is figuring out the best ways to visualize the results and get the modeling information to local emergency management officials along the coast so that they can use it.

“It doesn’t help anybody if we are doing this in an empty room somewhere and not sharing the results with the community and sharing them in a way that will maximize their use and maximize their impact,” he said.

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