A sophisticated monitoring system using weather stations, satellites, coastal radar, and a fleet of robotic underwater vehicles is producing a three-dimensional view of real-time ocean conditions in the coastal Mid-Atlantic. This component of the U.S. Integrated Ocean Observing System (IOOS) aims to provide decision makers with highly accurate predictions of ocean and atmospheric conditions useful for a range of management issues, from search and rescue operations, to predictions of hurricane tracks, coastal flooding, energy demand and the fate of pollutant spills, to fisheries management.
The Mid-Atlantic Regional Coastal Ocean Observing System (MARCOOS) covers the coastal ocean from Massachusetts to North Carolina and brings together a group of 20 institutions representing academia, government, and industry led by Rutgers University. ASA is the lead partner for the Data Management and Communications (DMAC) group and is responsible for DMAC strategies as well as the specific integration of data for search and rescue operations.
Four technologies are being used for the actual “observing” or data collection: a network of automated coastal weather stations, satellite imagery, coastal ocean radar, and a fleet of undersea robotic vehicles. The weather network provides high resolution wind observations at the land-sea interface. Satellites provide a wide spectrum of ocean conditions, from surface temperature to biological activity. Coastal Ocean Radar provides real-time ocean surface conditions (current and wave height) that dictate the fate of objects or materials on the surface. Undersea robotic “gliders” provide a variety of subsurface data throughout the water column as they glide downwards and then upwards along a programmed pathway that may be hundreds of kilometers long.
These data streams are processed into a near real-time four-dimensional model of surface and subsurface ocean conditions. This allows much more accurate predictions of the track of major weather events like hurricanes and nor-easter storms; conditions affecting sea breezes, navigation, and rip-tides; and biological features like harmful algal blooms or distribution of shellfish larva. The system is intended to augment emergency management, search and rescue, and pollution response efforts, as well as enhance management of natural resources through better prediction and management of energy consumption, fisheries stocks, beach and coastal sediments, and storm water.
NOAA is funding this three-year effort. In the first year, ASA is focusing on integration of meteorological data from Weatherflow’s observation network and surface currents from the extensive high-frequency radar network managed by Rutgers University and the Universities of Connecticut, Massachusetts, and Rhode Island. Forecast data from numerical models run at Rutgers, Stevens Institute, and UMass Dartmouth will be integrated in the second year and will provide high resolution forecast data for search and rescue operations and predicting the fate of oil and chemical spills.