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Gulf of Maine Circulation Modeling: Prospects for Skill 6 July 2005. Daniel R. Lynch Dartmouth College Hanover NH. Points of Departure. Science People Data Problems. Science. Well-Established: Physical Quantities Equations Algorithms for solutions. Distributed across ‘Academe’. - PowerPoint PPT Presentation
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Gulf of Maine Circulation Modeling: Prospects for Skill
6 July 2005
Daniel R. LynchDartmouth College
Hanover NH
Points of Departure
• Science
• People
• Data
• Problems
Science
• Well-Established:– Physical Quantities– Equations – Algorithms for solutions
• Distributed across ‘Academe’
People
• At least 3 different communities– Theory – Observation– Simulation “the third science”
• Algorithms• Systems
• Non-Local• Incentives: Advancement of Learning
Data
• Unprecedented new abundance• Sampling in (x, y, z, t) necessarily sparse• Real Data is site-specific, event-specific• Necessary to relate theory to facts• By itself, hopelessly incomplete
– Interpolation
– Extrapolation
– Interpretation
– Relation to other information
Problems
• “Local”• Not alligned with political boundaries• Distributed across agencies• Regulatory context
Status Quo
• Science: generic, roaming
• Data: local, incomplete
• People: distributed across ‘academe’– Advancent of knowledge, not application
• Problems: regulatory context, local, political boundaries
The Key Challenge is Organizational
Regional Progress
• 1993 RARGOM Workshop <-------------
• RMRP
• MWRA Mass. Bays
• GLOBEC
• EcoHAB
• Sea Grant (x4)
• GoMOOS
• Multiple NOAA programs
• Canadian Companions
Data and State Estimation
Time of Occurrence(Ocean)
Time of Availability(Information)
Future
(Now)
Past
State Estimation
Time of Occurrence(Ocean)
Time of Availability(Information)
Forecast
Nowcast
Hindcast
Time of Occurrence(Ocean)
Time of Availability(Information)
Forecast
Nowcast
Hindcast
All Data
Time of Occurrence(Ocean)
Time of Availability(Information)
Forecast
Nowcast
Hindcast
All Data
Time of Occurrence(Ocean)
Time of Availability(Information)
Forecast
Nowcast
HindcastAll Data
Model‘Data Product’
Time of Occurrence(Ocean)
Time of Availability(Information)
Forecast
Nowcast
Hindcast
Data Used
Bell
Time of Occurrence(Ocean)
Time of Availability(Information)
Forecast
Nowcast
Hindcast
Data Used
Bell Publication
The Well-Posed Problem(The Mathematical Standard)
Theory
• “The Data” • Necessary and Sufficient
– initial state, simultaneous– boundary conditions (deep
ocean, cross-shelf transports)– forcing (atmospheric fluxes,
rivers)– Parameters (bottom, surface
roughness)
• All roads lead to Rome – (small X)
The Well-Posed Problem(The Mathematical Standard)
Theory
• “The Data” • Necessary and Sufficient
– initial state, simultaneous– boundary conditions (deep
ocean, cross-shelf transports)– forcing (atmospheric fluxes,
rivers)– Parameters (bottom, surface
roughness)
• All roads lead to Rome – (small X)
Actual
Nonnecessary
Insufficient
X is finite
The Well-Posed Problem(The Mathematical Standard)
Theory
• “The Data” • Necessary and Sufficient
– initial state, simultaneous– boundary conditions (deep
ocean, cross-shelf transports)– forcing (atmospheric fluxes,
rivers)– Parameters (bottom, surface
roughness)
• All roads lead to Rome – (small X)
Actual
Nonnecessary
Insufficient
X is finite
There is never a well-posed problem in nature
– Must make up what is not known but necessary
– Use the data you have, deduce what you need
– Criterion: credibility
– Credibility implies a Prior Estimate• mean and variance
Poorly Posed Problems
What is Truth?
What is Truth?
Data Model
d m
Misfit
What is Truth?
Data Model
Truth real but unknowable
Errors unknowable
Prediction a credible blend:
Data + Model
Blend: Invokes statistics of d , m
Prediction Error: blend of statistics of d , m,
d m
Misfit
Prediction
p
What is Truth?
Data Model
Truth real but unknowable
Errors unknowable
Prediction a credible blend:
Data + Model
Blend: Invokes statistics of d , m
d m
Misfit
Prediction
p
Skill:Misfits
Small, NoisyUnknown Inputs
Small, Smooth
p : grows with time
Examples
• SAB: resolve the data or burn it
• Great Bay: Hi-resolution Lagrangian exchange
• ECOHAB Results: hindcast trajectories
• Georges Bank: Real-time Wind Forecast Error
A Data-Assimilative System
Resolution
The difference between high-resolution and low-resolution forward simulations
Inverse Error with Low Resolution-- DA cannot make up for Inadequate Resolution --
Estuarine Resolution
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Boundary deduced from Interior Data
Data Assimilative Hindcast
Mean Separation Rate: 1.78 km/day
The 2005 Prior
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The 2005 Hindcast
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The 2005 Hindcast
• Data-Assimilative
• Real Time
• At-Sea
•Limited-area
• Hindcast of complete cruise
• May 9- 18
The 2005 Hindcast
The 2005 Hindcast
The 2005 Hindcast
The 2005 Hindcast
The 2005 Hindcast
Who Painted the Bays Red?
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Who Painted the Bays Red?
Frontal Dispersion - Forecast
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Frontal Dispersion - Forecast
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Frontal Dispersion - Hindcast
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Frontal Dispersion - Hindcast
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Key Challenges
Organizational
Recommendations
• Accept
– Organizational progress must occur
– Scientific progress must continue in parallel
– Modeling is its own ‘science’
• Focus on
– the modelers, not the tools
– energizing the science community
• do not try to change the scientific culture
• organize the use of the Gulf of Maine as a laboratory
– enabling scientific progress on practical problems
– Circulation Modeling as initial baseline
• Invent
– no new organizations
– one new task: “Gulf of Maine Modeling Roundtable”
– Insist on its ‘standing’ in science and regulatory communities
• Do not distort the University Mission - Announce a new one
• Expect to Pay and Get
Interagency Agreement• Establish a GoM Modeling Roundtable within a standing organization • Service Populations: Science, Engineering, Management
• Spread the cost among agencies
• Govenance– full time staff– board of overseers– regular users’ group
• ‘Outreach’ workshops on model products and strategies
• Focused publications on Gulf of Maine
• Archive– software– data– simulation results– report series– publication series
• Insist on PI participation as precondition to participation in program
How to Recognize Success?• Honor thy precursors
• Broad Participation
• Data Standards
• Accumulation of– Data– Software– Reports– Papers
• Progress is enabled
• People are enabled
People are your Investment
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