1 / 28

Skill Assessment for Coupled Physical-Biological Models of Marine Systems

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.

callum
Download Presentation

Skill Assessment for Coupled Physical-Biological Models of Marine Systems

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. 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

  2. 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

  3. Topical Organization Scientific Carbon Cycle Harmful Algal Blooms Ecosystem Dynamics and Fisheries Estuarine/Coastal Water Quality Cross -Cutting Themes Skill Vocabulary Metrics Data Assimilation

  4. Participation Apex Contributions, Invited GLOBEC ECOHAB SAB JGOFS European Shelf Seas Contributions 18 -- 30 papers 42 et al -- 55 et al people

  5. 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

  6. 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/

  7. Vocabulary

  8. VocabularyThe first Bloom!

  9. 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 ,

  10. 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

  11. Skill Assessment • Judgement about Skill • Future, Past The realm of Mistake

  12. What is Truth?

  13. What is Truth? ed em Data Model Misfit d

  14. 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

  15. 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

  16. 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!

  17. 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”

  18. 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

  19. Post-Optimality Judgement • Beyond Misfit • Model - Truth • Criterion?

  20. Causality Prior / Posterior Logical Previous / Subsequent Temporal Forward / Inverse Influence in Classic Initial/Boundary Value Problem

  21. 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”

  22. Data and State Estimation

  23. Time of Occurrence (Ocean) Time Future (Now) Past Time of Availability (Information)

  24. Time of Occurrence (Ocean) Forecast Nowcast Hindcast All Data Time of Availability (Information)

  25. Time of Occurrence (Ocean) Forecast Nowcast Hindcast All Data Time of Availability (Information)

  26. Time of Occurrence (Ocean) Forecast Model ‘Data Product’ Nowcast All Data Hindcast Time of Availability (Information)

  27. Time of Occurrence (Ocean) Forecast Nowcast Hindcast Data Used Bell Time of Availability (Information)

  28. Time of Occurrence (Ocean) Forecast Nowcast Hindcast Data Used Bell Publication Time of Availability (Information)

More Related