Gulf of Maine Circulation Modeling: Prospects for Skill 6 July 2005

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



Forecast

Nowcast

Hindcast



Forecast

Nowcast

Hindcast

All Data



Forecast

Nowcast

Hindcast

All Data



Forecast

Nowcast

HindcastAll Data

Model‘Data Product’



Forecast

Nowcast

Hindcast

Data Used

Bell



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)


Theory





roughness)


Actual

Nonnecessary

Insufficient

X is finite


Theory





roughness)


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

QuickTime™ and aCinepak decompressor

are needed to see this picture.

Boundary deduced from Interior Data

Data Assimilative Hindcast

Mean Separation Rate: 1.78 km/day

The 2005 Prior

QuickTime™ and a decompressor


The 2005 Hindcast



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?

QuickTime™ and a decompressor


Who Painted the Bays Red?

Frontal Dispersion - Forecast



Frontal Dispersion - Forecast



Frontal Dispersion - Hindcast



Frontal Dispersion - Hindcast



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

Documents

Gulf of Maine Circulation Modeling: Prospects for Skill 6 July 2005