1. MARACOOS High Frequency Radar Network Operations
Josh Kohut1, Hugh Roarty1, Erick Rivera1, Mike Smith1, Ethan Handel1, Colin Evans1, John Kerfoot1, Scott Glenn1
1Rutgers University Institute of Marine and Coastal Sciences
INTRODUCTION
REALTIME SURFACE CURRENT VISUALIZATION
APPLICATIONS
MODELS
Hourly Surface Current Fields
25 Hr Average Current Fields
The continued operation and maintenance of the MARACOOS regional HF Radar network, one of the highest observatory priorities, is essential for delivering quality controlled data to MARACOOS modeling groups, National HF Radar Network and the U.S. Coast Guard SAROPS. This goal requires us to maintain efficient regional coordination of technical support, track resiliency statistics so as to prioritize gap-filling needs, implement gap-filling measures as resources allow and incorporate new sites into the regional network.
Our objective is to advance our HF Radar network coverage statistics closer to the USCG target of 80% coverage, 80% of the time.
ROMS
Rutgers University
NYHOPS
Stevens Institute of Technology
HOPS
U. Massachusetts, Dartmouth
Hourly HF Radar data from the long-range systems are made available to regional partners developing real-time physical forecasts. These data are assimilated into one statistical model (Left) and three dynamical models (Above). Each model has slightly different techniques to incorporate the HF radar data giving an ensemble of forecasts available to product development throughout the region.
PARTNER INSTITUTIONS
25 Hour Current Field-Sea Surface Temperature (AVHRR) Overlays
Rutgers University
University of Massachusetts – Dartmouth
University of Rhode Island
Stevens Institute of Technology
University of Delaware
Center for Innovative Technology
Applied Science Associates
Old Dominion University
Short Term Prediction System
U. Connecticut
SAROPS
One of the products that benefit from this coupled observation modeling system is the U.S. Coast Guard’s Search and Rescue Optimal Planning System (SAROPS). Since May of 2009 real-time observations from the long-range HF radar network and associated statistical forecasts from STPS are fed into the operational decision tool SAROPS. A current project funded by IOOS will extend these products to all regions with HF radar coverage around the US coast.
NETWORK UPTIME SATISTICS
Site Code
Operating Time
NAUS 93
NANT 92
BLCK 90
MRCH 97
HOOK 98
LOVE
BRIG 99
WILD 69
ASSA
CEDR 59
LISL 71
DUCK
100
HATY
Network Average 89
All HF radar sites in the Mid-Atlantic were set up to report their data to the NOAA National Network Server at Rutgers.  HF Radar operations were sustained at a rate consistent with Phase 2 of the Mid Atlantic High Frequency Radar Consortium’s 3-Phase implementation plan.  Phase 2 includes 3 full time HF Radar technicians distributed across the northern, central and southern sub-regions of the Mid-Atlantic with a part time regional coordinator managing the technicians and network.  A week long advanced training session was held in February 2008.  The three full time technicians as well as technicians from seven of the eight operators in the region attended this training.  At this meeting it was decided that the regional HF radar network would adopt a distributed technician approach with one operator responsible for the systems in each of the three regions (north, central and south).  This work force was able to achieve an 89% operating time for the long range systems from December 1, 2008 to November 30, 2009 (Left).
Regional Observatory Data Fusion Products/Displays
OIL SPILLS
The oil spill that resulted from the Deepwater Horizon Incident in the Gulf of Mexico is the largest ecological disaster in U.S. History. An event of this magnitude warrants close monitoring and planning. The uncertainties in their scope of impact at the time of the event required regions along the eastern seaboard of the United States to monitor the situation and react. Along the Mid-Atlantic Bight, we ran scenarios of drift based on the available HF Radar current maps in the event the oil made it into the Gulfstream. The map to the right shows the initial location (green dots) and fate (blue tracks ending at the red dots) of simulated oil particles on the surface. These were intended to mimic tarballs, the most likely form of oil to approach the region.
August, 2009
FISHERIES
Fisheries in the Mid-Atlantic bight are highly migratory and tightly coupled to the surrounding physical habitat. Using a long time series of surface current maps, we developed a product to predict the location of key species in the food web. One parameter that proved very important to loligo squid, was the trend in divergence. These maps show where the surface is divergent (red) and convergent (blue) over specified time intervals. For this study the squid preferred regions in which divergence was likely over time scales of weeks to months.