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Ocean Modeling Network & the Virtual OceanOcean Modeling Network & the Virtual Ocean
YI CHAO([email protected]; 818-354-8168)
Jet Propulsion Laboratory, California Institute of TechnologyPasadena, California
October 18, 2007
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Ocean Models defined in my talk
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Modeling should be an essential component of OOI
• Model can be used to
– Fill the data gap: ocean will be always under-sampled (much more so than the atmosphere)
• Model can be used to – Forecast into the future: observation
can only tell what happens today– Weather forecast as a success story for
the atmospheric sciences
• Model can also be used to test hypothesis, diagnostic analysis, close the budget, dT/dt = A ● t + B – C * D / E
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Linking Physics, Biology, Marine Ecosystem with Marine Mammals
Enabled by CI
Natural Variability(e.g., El Nino)
Climate Change(e.g., Global Warming)
Local Wind ChangeOcean Circulation
Upwelling
ConvergenceDivergence
Nutrient Supply:Nitrate
PhosphateSilicate
Iron
PhytoplanktonZooplankton
Population ChangeFishery Collapse
Endangered Species
Migration Behavior
Diving Patterns
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DataInput
Ocean Modeling Services
Synthesis Products
User CommunityEducation
Public Awareness
Observatories(global, regional
& coastal)
ObservationNetworkDesign(OSSEs)
Integrating Data and Model for Science/Applications
Feedback
Forecasting
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Virtual Ocean Observatory/Simulator
Click to see the movie
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Ocean has multiple scales: From Global to Regional and Coastal
100-km; years
Modeling network needs to integrate all three observatories
10-km; hours/days
100-km; years
1000-km, decades
Global
Coastal
Regional
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What is the problem size for the Virtual Ocean?Linking the 3-Observatory at the highest resolution
possible
360 (longitude) x 100 (1km/grid) x180 (latitude) x 100 (1km/grid) x 100 (depth) x 5 (para.) x 8 (byte) = 2.6 TB/snapshot
=(360x180x100x5x8 job/cpu) x 10000 cpu
Dedicated computers
Computers on Grids
Distributed computers
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Real-Time Ocean Forecast for Southern California
http://ourocean.jpl.nasa.gov/SCB/index.jsp
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Core: Ocean Model & Data Assimilation SystemInitialization for Forecasting
Xt+1nowcast/analysis = Xt
forecast + δx
Min(Data-Model) δx
Time
Xtnowcast
Xt+1forecast
Xt+1nowcast
00Z 06Z(Assimilation window)
Xt+1forecast = M ● Xt
nowcast
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On-Demand Modeling: Users select their own modeling configurations, submit runs,
and analyze results all through a web portal
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Pre-deployment: Observing System Simulation Experiments (OSSEs)
Post-deployment: Observing System Experiments (OSEs) &
J = 0.5 (x-xf)T B-1 (x-xf) + 0.5 (h x-y)T R-1 (h x-y)
Time
03Z 21Z15Z09Z
Initialcondition
24-hour forecast
03Z
Xa = xf + xf
Xa
xf
48-hour forecast y: observation
x: model
6-hour assimilation cycle
6-hour real-time data processing modeling and data assimilation workflow
Observatory data
Virtual Ocean
Design, Testing and Deploy
Models
Data Assimilation
DataAnalysis
Science Questions & Drivers
~ 500 m
~3 km
Sensor &Platform
Data Synthesis: Nowcast & Data Impact
End-to-end traceability from science objectives to sensors and infrastructure
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The Virtual Ocean: from Global to Regional and Coastal