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Skill Assessment for Coupled Physical-Biological Models of Marine Systems. Daniel R. Lynch Dennis J. McGillicuddy, Jr. Francisco E. Werner Sponsors: NOAA - CSCOR NSF - CMG Prepared for: U.S. GLOBEC Pan-Regional Synthesis Workshop 27 November - 1 December 2006 NCAR, Boulder CO.

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Skill assessment for coupled physical biological models of marine systems

Skill Assessment for Coupled Physical-Biological Modelsof Marine Systems

Daniel R. Lynch

Dennis J. McGillicuddy, Jr.

Francisco E. Werner

Sponsors:

NOAA - CSCOR

NSF - CMG

Prepared for:

U.S. GLOBEC Pan-Regional Synthesis Workshop

27 November - 1 December 2006

NCAR, Boulder CO


Overview
Overview

Goals

Assess the state-of-the-art

Provide recommendations in support of Agency programs

Deliverables

Special volume of peer-reviewed contributions

Report to NOAA summarizing progress


Topical organization
Topical Organization

Scientific

Carbon Cycle

Harmful Algal Blooms

Ecosystem Dynamics and Fisheries

Estuarine/Coastal Water Quality

Cross -Cutting Themes

Skill Vocabulary

Metrics

Data Assimilation


Participation
Participation

Apex Contributions, Invited

GLOBEC

ECOHAB

SAB

JGOFS

European Shelf Seas

Contributions

18 -- 30 papers

42 et al -- 55 et al people


Timeline
Timeline

January '06 Invitations out

July '06 Authors' Workshop 1

Vocabulary Rev. 1

Working Groups: DA, Metrics

Dec ‘06 Working Group Reports to Editors

Feb ‘07 Vocabulary Rev. 2 + Working Group Report Distribution

March '07Authors’ Workshop 2

April ‘07 MS Submission; Peer Review Start

April ‘08 Final Copy to Printer

Report goes to NOAA


Peer-Reviewed Publication

Journal of Marine Systems

Coordination

3 Community Pieces

Vocabulary

Metrics

Data Assimilation

http://www-nml.dartmouth. edu/

Publications/internal_reports/

NML-06-Skill/



Vocabulary the first bloom
VocabularyThe first Bloom!


55 GLOBEC Contributions

Dartmouth

WHOI

UNH

UNC

Dalhousie

Rutgers

NMFS - WH

NMFS - Narragansett

NMFS - Sandy Hook

DFO - Halifax

DFO - St Andrew’s

DFO - Victoria

Reused in ECOHAB, SAB, EIRE, SWVI, NERRS, CICEET, RMRP, SeaGrant

,


Skill conformance to truth
Skill: Conformance to Truth

  • State of Model and Truth

  • Processes - Internal Dynamics

  • Modes of Expression - Properties, Features

    • Equilibria

    • Instabilities

    • Spectra

    • Covariance

    • Population Structure

      The Realm of Error


Skill assessment
Skill Assessment

  • Judgement about Skill

  • Future, Past

    The realm of Mistake



What is truth1
What is Truth?

ed

em

Data

Model

Misfit d


What is truth2
What is Truth?

ep

ed

em

Prediction

Data

Model

Misfit d

Truth real but unknowable

Errors unknowable

Prediction a credible blend:

Data + Model

Blend: Invokes statistics of ed , em

Prediction Error: blend of ed , em

Misfit: d = ed - em


What is truth3
What is Truth?

ep

ed

em

Prediction

Data

Model

Misfit d

Truth real but unknowable

Errors unknowable

Prediction a credible blend:

Data + Model

Invokes statistics of ed , em

Prediction Error: blend of ed , em

Misfit: d = ed - em

Skill:

Misfits

Small, Noisy

Deduced Inputs

Small, Smooth

Features

Credible


Features ex a retentive gyre

Physical Features

Is there a gyre?

Size?

Location?

Timing?

Residence Time?

Entrance Paths?

Exit Paths?

Relative to Organism

Cohort

Density

Scale

Age / Stage

Onset / Demise

Vital Rates

FeaturesEx: a Retentive Gyre

Bloom!


Misfit metrics
Misfit Metrics

  • Quadratic Form

  • = W

    • W = Cov-1()

    • d= ed+ em

  • Importance of

    • Data Error

    • Model Error (Unmodeled part of Truth)

  • “Dictatorship of Measurement”


  • Regularization
    Regularization

    • Data Sparse --> Indeterminacy

    • = W  p* Wp p

    • Importance of Prior

    • = W p* Wpp

    • Joint estimation of and p

      • Regularization adds bias toward prior

      • BPE - Best Prior Estimate

      • BPE is PDG -->  small, p small


    Post optimality judgement
    Post-Optimality Judgement

    • Beyond Misfit

    • Model - Truth

    • Criterion?


    Causality
    Causality

    Prior / Posterior

    Logical

    Previous / Subsequent

    Temporal

    Forward / Inverse

    Influence in Classic Initial/Boundary Value Problem


    Statistics
    Statistics

    • Distributions by Moments

    • Value of Moments: mean, variance, …

    • Ensemble within which Moments occur

    • Ex: 3 different ensembles

    • all previous realizations of a field

      • “Field variability”

  • all possible observations of this field

  • “Instrument Error”, “Noise”

  • all possible estimates of this field

  • “Inverse Noise”



  • Time of Occurrence

    (Ocean)

    Time

    Future

    (Now)

    Past

    Time of Availability

    (Information)


    Time of Occurrence

    (Ocean)

    Forecast

    Nowcast

    Hindcast

    All Data

    Time of Availability

    (Information)


    Time of Occurrence

    (Ocean)

    Forecast

    Nowcast

    Hindcast

    All Data

    Time of Availability

    (Information)


    Time of Occurrence

    (Ocean)

    Forecast

    Model

    ‘Data Product’

    Nowcast

    All Data

    Hindcast

    Time of Availability

    (Information)


    Time of Occurrence

    (Ocean)

    Forecast

    Nowcast

    Hindcast

    Data Used

    Bell

    Time of Availability

    (Information)


    Time of Occurrence

    (Ocean)

    Forecast

    Nowcast

    Hindcast

    Data Used

    Bell

    Publication

    Time of Availability

    (Information)


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