National Science Foundation Ocean Observing Initiative Cyber Infrastructure Implementing...

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

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 WFOsStd Radar SitesMesonet StationsLR HF Radar SitesGlider AUV TracksUSCG SLDMB TracksNDBC Offshore PlatformsCODAR Daily Average Currents

MARCOOS

MACOORA Workshop

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

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

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ion

Por

tal:

http

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roce

an.jp

l.nas

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v/C

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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

10

EnsembleModel

SST Obs.

MACOORA Workshop

Science Community Workshop 1 11

Science Community Workshop 1 12

Model A Model B

Model C Model D

13

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)

17

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