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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. Core CI OSSE Teams.

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slide1

National Science FoundationOcean Observing InitiativeCyber Infrastructure Implementing OrganizationObserving System Simulation Experiment NSF OOI CI IO OSSE

Yi Chao, JPL

Oscar Schofield, Rutgers

Scott Glenn, Rutgers

(about 30 people)

MACOORA Workshop

slide2

Core CI OSSE Teams

  • 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

MACOORA Workshop

ci osse in the mid atlantic bight

NWS WFOs

Std Radar Sites

Mesonet Stations

LR HF Radar Sites

Glider AUV Tracks

USCG SLDMB Tracks

NDBC Offshore Platforms

CODAR Daily Average

Currents

CI OSSE in the Mid-Atlantic Bight
  • Five real-time forecasting models
  • Avijit Gangopadhyay, U. Mass-Dartmouth
  • Alan Blumberg, Stevens Institute of Technology
  • John Wilkin, Rutgers
  • John Warner, USGS/WHOI
  • Pierre Lermusiaux, MIT

MARCOOS

MACOORA Workshop

ci osse november 2 13 2009

Space, In-Situ

(Oceans)

Data

Assimilation

Predictive Models

Supercomputing

Virtual Space

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.

Adaptive

Sampling

Two-way interactions between the sensor web and predictive models.

MACOORA Workshop

5

data model integration portal http ourocean jpl nasa gov ci
Data/Model Integration Portal: http://ourocean.jpl.nasa.gov/CI

Science Community Workshop 1

nam 12 km weather forecast
NAM (12-km) Weather Forecast

Science Community Workshop 1

sst obs
SST Obs.

Science Community Workshop 1

slide9

Model A

Model B

Model C

Model D

Science Community Workshop 1

observation vs multi model ensemble
Observation vs Multi-Model Ensemble

SST Obs.

Ensemble

Model

MACOORA Workshop

slide12

Model A

Model B

Model C

Model D

Science Community Workshop 1

observation vs multi model ensemble1
Observation vs Multi-Model Ensemble

HF Radar Obs

Ensemble Model

MACOORA Workshop

hyperion on eo 1 7 5kmx100km 30 m
Hyperion on EO-1: 7.5kmx100km (30-m)

Science Community Workshop 1

ci osse accomplishments

Space, In-Situ

(Oceans)

Data

Assimilation

Predictive Models

Supercomputing

Virtual Space

CI OSSE Accomplishments
  • 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.

Adaptive

Sampling

Two-way interactions between the sensor web and predictive models.

MACOORA Workshop

slide18

Steering CommitteeTommy Dickey (co-chair) - University of California, Santa BarbaraScott Glenn (co-chair) - Rutgers UniversityJim Bellingham - Monterey Bay Aquarium Research InstituteYi Chao - Jet Propulsion Laboratory and California Institute of TechnologyFred Duennebier - University of HawaiiAnn Gargett - Old Dominion UniversityDave Karl - University of HawaiiLauren Mullineaux - Woods Hole Oceanographic InstitutionDave Musgrave - University of AlaskaClare Reimers - Oregon State UniversityBob Weller (ex officio) - Woods Hole Oceanographic InstitutionDon Wright - Virginia Institute of Marine SciencesMark 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.

slide19

“MARCOOS data increases the explanatory power of habitat models by as much as 50%” – NOAA Fisheries And The Environment

MACOORA Mid Atlantic Cold Pool

Sampling & Forecasting for Fisheries

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

Five X-Shelf Glider Endurance Lines

Subsurface Maps Fisheries Groups

DB

NYH

CB

LIS

Cold Pool (T < 8C)

Dominant Spring-Fall

Subsurface Feature

In the MAB

Data Assimilated

into Forecast Models: Spring-Fall

OOI CI Tools:

Model Feedback

to Glider Sampling

Combines Infrastructure & Expertise from

IOOS MARCOOS, NSF OOI, NOAA NMFS

MACOORA Workshop

slide20

MACOORA Themes – MARCOOS Products

Cross-cut

MACOORA Workshop