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December 2014 Issue

GeoSpectrum Acquires Akoostix
GeoSpectrum Technologies Inc. (GTI), based in Dartmouth, Canada, has acquired Akoostix Inc., also based in Dartmouth.

Akoostix provides underwater acoustic and sonar processing software. Their acoustic processing capability complements GTI’s transducer expertise and is expected to position GTI as a strong contender for system sales in the Asia-Pacific region, the unmanned vehicle sector and other burgeoning markets.

Akoostix will continue to operate in the short term under its current organizational structure.


ROPOS Completes RSN Installation
The ROPOS ROV operated by the Canadian Scientific Submersible Facility (CSSF) has successfully completed the installation of the U.S. Regional Scale Nodes (RSN) Cabled Observatory, located off the coast of Washington and Oregon. The RSN is funded by the US National Science Foundation as part of the Ocean Observatories Initiative.

The cabled observatory will provide interactive real-time data from the seafloor and throughout the water column with state-of-the-art moorings that reach nearly 2,900 meters above the seafloor. Data flowing from this system will dramatically increase discovery and understanding of oceanic behavior.


Optical Sensors Track Nitrate Pulse to US Gulf
A new U.S. Geological Survey (USGS) report describes how advanced optical sensor technology is being used in the Mississippi River basin to accurately track the nitrate pulse to the Gulf of Mexico.

Excessive springtime nitrate runoff from agricultural land and other sources in the Mississippi drainage flows into the Mississippi River and downstream to the Gulf of Mexico. This excess nitrate contributes to the Gulf of Mexico hypoxic zone, an area with low oxygen known as the “dead zone.” NOAA-supported researchers reported that the summer 2014 dead zone covered about 5,052 square miles, an area the size of Connecticut.

The USGS is using the new sensor technology to collect nitrate concentration data every hour to improve the accuracy of nitrate load estimates to the Gulf of Mexico. The data can also be used to make it easier to detect changes in nitrate levels related to basin management and to track progress toward the goal of reducing the size of the dead zone.

This high-frequency data also provides new insights into timing and magnitude of nitrate flushing from soils during wet and dry conditions.


Lake Level Viewer Aids Great Lakes Coastal Planning
A new NOAA online visualization and mapping tool, the Lake Level Viewer, will help communities along the U.S. Great Lakes plan for, and adapt to, climate change and changes in lake water levels. The easy-to-use, interactive tool was developed by the National Ocean Service’s Office for Coastal Management as part of its Digital Coast initiative. The Lake Level Viewer provides planners and decision makers with visual lake level scenarios for rise and drop information before it happens. Lake level scenarios can be incorporated into land use decisions, along with economic, social and environmental considerations, to make wise investments in public infrastructure and develop livable, resilient communities. The viewer uses high-resolution elevation data, enabling users to display and visualize water levels associated with different lake level scenarios with a high degree of accuracy—ranging from 0 to 6 feet above and below average lake level. Users can view elevation models, determine lake water depths at specific locations, examine data confidence, and view societal and economic impacts. Rising or decreasing lake levels can affect commercial interests and shoreline habitats and structures. More than 4,900 miles of U.S. shoreline ring the Great Lakes, of which 3,800 miles are currently mapped on the Lake Level Viewer.


NSF Grant to Advance Storm Surge Simulations
The University of Texas at Austin Institute for Computational Engineering and Sciences (ICES) has received a $3 million National Science Foundation (NSF) grant to advance the storm surge predictive simulations that have helped Texas emergency managers develop some of the country’s most successful hurricane evacuation plans.

Speed is essential for disaster planning, and the new simulations will be designed to take advantage of advances in supercomputing to convert large databases of weather and topographical data into storm surge predictions within an hour, half the current time for processing the changing data.

The current storm surge prediction process relies on feeding hurricane data into a computational program called ADCIRC, which uses high-performance computers to generate data about potential outcomes. With the NSF grant, ADCIRC will be overhauled into a version 2.0 dubbed “STORM” that’s designed to perform more efficiently across a variety of computer hardware architectures.

Since first being developed in the mid-1990s, ADCIRC has been widely used by NOAA, the U.S. Army Corps of Engineers and academic researchers to simulate and predict water flow in coastal areas of the U.S. Storm surge prediction is a popular use for the program, but the governing equations describing fluid flow can be applied to investigate other research questions. During the Deepwater Horizon oil spill, for example, ADCIRC was used to predict oil dispersal paths up to three days in advance.

The STORM program will maintain the same ADCIRC functionalities but will get a code upgrade with a completely new foundation for its algorithms. The new code will be written in HPX and designed to be flexible and easily integratable with other code types and adaptable to diverse computer architectures. By rewriting the code using HPX, STORM will be able to run more efficiently on today’s supercomputing systems and will likely be well-equipped to handle inevitable changes to come. Turning ADCIRC into STORM will entail understanding the history and composition of the original code, which could help in constructing other programs in general.


2014:  JAN | FEB | MARCH | APRIL | MAY | JUNE | JULY | AUG | SEPT | OCT | NOV | DEC
2013:  JAN | FEB | MARCH | APRIL | MAY | JUNE | JULY | AUG | SEPT | OCT | NOV | DEC

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