1. National Science Foundation Ocean Observing Initiative Cyber Infrastructure Implementing Organization Observing System Simulation Experiment NSF OOI CI IO OSSE Yi Chao, JPL Oscar Schofield, Rutgers Scott Glenn, Rutgers (about 30 people) MACOORA Workshop

2. 2 OurOcean data and model integration portal Yi Chao and Peggy Li, JPL CASPER/ASPEN mission planning and control Steve Chien and David Thompson, JPL MOOSDB/MOOS-IvP autonomous vehicle control Arjuna Balasuriya, MIT Glider Simulator, Environment and Field Deployment in Mid-Atlantic Bight Oscar Schofield, Rutgers Core CI OSSE Teams

4. CI OSSE in the Mid-Atlantic Bight 4 Five real-time forecasting models (1)Avijit Gangopadhyay, U. Mass- Dartmouth (2)Alan Blumberg, Stevens Institute of Technology (3)John Wilkin, Rutgers (4)John Warner, USGS/WHOI (5)Pierre Lermusiaux, MIT NWS WFOs Std Radar Sites Mesonet Stations LR HF Radar Sites Glider AUV Tracks USCG SLDMB Tracks NDBC Offshore Platforms CODAR Daily Average Currents MARCOOS MACOORA Workshop

5. 5 CI OSSE: November 2-13, 2009 Objective: To provide a real oceanographic test bed in which the designed CI technologies will support field operations of ships and mobile platforms, aggregate data from fixed platforms, shore- based radars, and satellites and offer these data streams to data assimilative forecast models. Goal: To use multi-model forecasts to guide glider deployment and coordinate satellite observing. 5 Data Assimilation Predictive Models Space, In-Situ (Oceans) Virtual Space Supercomputing Adaptive Sampling Two-way interactions between the sensor web and predictive models. MACOORA Workshop

6. Science Community Workshop 16 Data/Model Integration Portal: http://ourocean.jpl.nasa.gov/CI

7. NAM (12-km) Weather Forecast Science Community Workshop 17

8. 8 SST Obs.

9. Science Community Workshop 19 Model AModel B Model C Model D

10. Observation vs Multi-Model Ensemble 10 Ensemble Model SST Obs. MACOORA Workshop

11. Science Community Workshop 111

12. Science Community Workshop 112 Model A Model B Model CModel D

13. 13 Observation vs Multi-Model Ensemble HF Radar ObsEnsemble Model MACOORA Workshop

14. Science Community Workshop 114

15. Science Community Workshop 115

16. Science Community Workshop 116 Hyperion on EO-1: 7.5kmx100km (30-m)

17. 17 CI OSSE Accomplishments Data Assimilation Predictive Models Space, In-Situ (Oceans) Virtual Space Supercomputing Adaptive Sampling Two-way interactions between the sensor web and predictive models. A Closed Loop OSSE/OSE –We integrated in-situ sensors with space-based Earth observation system. –Data gathered locally by a fleet of gliders is fed into a real-time assimilative ocean forecasting system. –Model forecasts are used by scientists to command the gliders and space craft to optimize the spatial coverage over the areas of interests. –Both data and model forecast are available in real-time to aid better decision making. MACOORA Workshop

18. Steering Committee Tommy Dickey (co-chair) - University of California, Santa Barbara Scott Glenn (co-chair) - Rutgers University Jim Bellingham - Monterey Bay Aquarium Research Institute Yi Chao - Jet Propulsion Laboratory and California Institute of Technology Fred Duennebier - University of Hawaii Ann Gargett - Old Dominion University Dave Karl - University of Hawaii Lauren Mullineaux - Woods Hole Oceanographic Institution Dave Musgrave - University of Alaska Clare Reimers - Oregon State University Bob Weller (ex officio) - Woods Hole Oceanographic Institution Don Wright - Virginia Institute of Marine Sciences Mark Zumberge - Scripps Institution of Oceanography Glenn, S.M. and T.D. Dickey, eds., 2003, SCOTS: Scientific Cabled Observatories for Time Series, NSF Ocean Observatories Initiative Workshop Report, Portsmouth, VA., 80 pp., www.geoprose.com/projects/scots_rpt.html.

19. Fisheries Users Fisheries Councils NMFS Commercial Recreational Glider Ports U Mass Dartmouth SUNY Stony Brook Rutgers U Delaware U Maryland Naval Academy U North Carolina Forecast Centers U Mass Dartmouth Stevens Institute Tech Rutgers MIT USGS Woods Hole Operations Centers Rutgers NASA JPL MACOORA Mid Atlantic Cold Pool Sampling & Forecasting for Fisheries Combines Infrastructure & Expertise from IOOS MARCOOS, NSF OOI, NOAA NMFS Five X-Shelf Glider Endurance Lines Data Assimilated into Forecast Models: Spring-Fall OOI CI Tools: Model Feedback to Glider Sampling Subsurface Maps Fisheries Groups Cold Pool (T < 8C) Dominant Spring-Fall Subsurface Feature In the MAB CB DBNYH LIS “MARCOOS data increases the explanatory power of habitat models by as much as 50%” – NOAA Fisheries And The Environment MACOORA Workshop

20. MACOORA Themes – MARCOOS Products Cross-cut