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National Science FoundationOcean Observing Initiative
Cyber Infrastructure Implementing Organization
Observing System Simulation Experiment
NSF OOI CI IO OSSE
Yi Chao, JPLOscar Schofield, Rutgers
Scott Glenn, Rutgers(about 30 people)
MACOORA Workshop
MACOORA Workshop 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
CI OSSE in the Mid-Atlantic Bight
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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 WFOsStd Radar SitesMesonet StationsLR HF Radar SitesGlider AUV TracksUSCG SLDMB TracksNDBC Offshore PlatformsCODAR Daily Average Currents
MARCOOS
MACOORA Workshop
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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.
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DataAssimilation
Predictive Models
Space, In-Situ (Oceans)
Virtual SpaceSupercomputing
AdaptiveSampling
Two-way interactions between the sensor web and predictive models.
MACOORA Workshop
Science Community Workshop 1 6
Dat
a/M
odel
Inte
grat
ion
Por
tal:
http
://ou
roce
an.jp
l.nas
a.go
v/C
I
NAM (12-km) Weather Forecast
Science Community Workshop 1 7
Science Community Workshop 1 8
SST Obs.
Science Community Workshop 1 9
Model A Model B
Model C Model D
Observation vs Multi-Model Ensemble
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EnsembleModel
SST Obs.
MACOORA Workshop
Science Community Workshop 1 11
Science Community Workshop 1 12
Model A Model B
Model C Model D
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Observation vs Multi-Model Ensemble
HF Radar Obs Ensemble Model
MACOORA Workshop
Science Community Workshop 1 14
Science Community Workshop 1 15
Science Community Workshop 1 16
Hyperion on EO-1: 7.5kmx100km (30-m)
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CI OSSE Accomplishments
DataAssimilation
Predictive Models
Space, In-Situ (Oceans)
Virtual SpaceSupercomputing
AdaptiveSampling
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
Steering CommitteeTommy 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 Observatoriesfor Time Series, NSF Ocean Observatories InitiativeWorkshop Report, Portsmouth, VA., 80 pp., www.geoprose.com/projects/scots_rpt.html.
Fisheries UsersFisheries CouncilsNMFSCommercialRecreational
Glider PortsU Mass DartmouthSUNY Stony BrookRutgersU DelawareU MarylandNaval AcademyU North Carolina
Forecast CentersU Mass DartmouthStevens Institute TechRutgersMITUSGS Woods Hole
Operations CentersRutgersNASA JPL
MACOORA Mid Atlantic Cold Pool Sampling & Forecasting for Fisheries
Combines Infrastructure & Expertise fromIOOS 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)Cold Pool (T < 8C)Dominant Spring-Fall Dominant Spring-Fall Subsurface FeatureSubsurface Feature
In the MABIn the MAB
CBDB NYH
LIS
“MARCOOS data increases the explanatory power of habitat models by as much as 50%” – NOAA Fisheries And The Environment
MACOORA Workshop
MACOORA Workshop
MACOORA Themes – MARCOOS Products Cross-cut
