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DART-related modeling activities

DART-related modeling activities. DART-ITHACA coordination meeting 30 Nov - 1 Dec 2006 Michel Rixen rixen@nurc.nato.int. Ensembles…. 2 particular research lines relevant to MILOC/EOS/NURC/NATO Acoustic properties Surface drift. Ensemble (single model) Initial conditions

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DART-related modeling activities

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  1. DART-related modeling activities DART-ITHACA coordination meeting 30 Nov - 1 Dec 2006 Michel Rixenrixen@nurc.nato.int

  2. Ensembles… 2 particular research lines relevant to MILOC/EOS/NURC/NATO • Acoustic properties • Surface drift • Ensemble (single model) • Initial conditions • Boundary conditions • Statistics/parameterization • Super-ensemble (multi-model of the same kind) • Least-squares: weather+climate (Krishnamurti 2000, Kumar 2003) • Max likelihood+ regularization by climatology : tropical cyclones (Rajagopalan 2002) • Kalman filters: precipitation (Shin 2003) • Probabilistic: precipitation (Shin 2003) • ‘Hyper’-ensemble (multi-model of different kinds) • e.g. combination of ocean+atmospheric+wave models? • General aim: forecast + [uncertainty/error/confidence estimation]

  3. Models Weights Data Super-Ensembles (SE)… • Simple ensemble-mean • Individually bias-corrected ens.-mean • Linear regression (least-squares) • Non-linear regression (least-squares) • Neural networks (+regularisation) • Genetic algorithms Compute optimal combination from past model-data regression, then use in forecast-mode

  4. MREA04: sound velocity (100m)

  5. Errors on sound velocity Analysis Single models SE

  6. SE Sound speed profile errors

  7. SE Weights

  8. HOPS-IHPO (1) HOPS-Harv. (2) Coarse NCOM (3) Fine NCOM (4) SE (2) SE (4) SE (1, 2) SE (3, 4) SE (1 to 4) HOPS-IHPO (1) HOPS-Harv. (2) Coarse NCOM (3) Fine NCOM (4) SE (2) SE (4) SE (1, 2) SE (3, 4) SE (1 to 4)

  9. ADRIA02-03 drifters (Jan-Feb)

  10. Analysis: 14 Feb 2003

  11. ADV WIND Indiv. Forecast err.: 14 Feb 2003 (12 Feb 2003+ 48h) ADV+WIND ADV+WIND+STOKES

  12. ADV WIND SEs forecast err: 14 Feb 2003 (12 Feb 2003+ 48h) ADV+WIND ADV+WIND+STOKES

  13. Drifter tracks Ocean advection Ocean+Stokes Stokes SE True Unbiased single models 24 h forecast

  14. SEs INDIV

  15. MS-EVA (JRP Harvard) New methodology utilizing multiple scale window decomposition in space and time of a model • multi-scale interactive • nonlinear • intermittent in space • episodic in time • E.g. wavelet Selecting the right processes at the right time…

  16. SE and MS-EVA=MSSE Note: Energy/vorticity/mass conservation issues Model 1 MSSE combines optimally the strengths of all models at any time at different scales Model 2 Model N Selecting the right processes from the right models at the right time…

  17. Dynamics of the Adriatic in Real-Time • Gulf of Manfredonia & Gargano Peninsula • Mid-Adriatic • Whole Adriatic • Critical mass of research and ressources

  18. NURC-NRLSSC JRP GOALS • Assess real-time capabilities of monitoring (data) and prediction (models) of small-scale instabilities in a controlled environment (operational framework) • Produce a comprehensive data-model set of ocean and atmosphere properties (validation of fusion methods) • 1A5: ensemble modeling+uncertainty • 1A2: air-sea interaction, coupling/turbulence • 1D1: data fusion & remote sensing • 1D3: geospatial data services • ONR projects: • NRL-HRV on internal tides • NICOP program on turbulence • EOREA ESA (SatObSys/Flyby/ITN/NURC)

  19. PARTNERS • 33 institutions (on board+home institutions): • 10 USA, 15 ITA, 1 GRC, 1 DEU, 1 BEL, 2 FRA PfP : 4 HRV, (1 ALB)

  20. Highlights IN-SITU • SEPTR (1 NURC, 3 NRL) • BARNY (2 NURC, 13 NRL, 2HRV) • Wave rider, meteo stations • CTD chain • +Aquashuttle (NRL, Universitatis) MODELS • Ocean (6+3 to come) • Atmospheric (7) • Wave (4) REMOTE SENSING • NURC: HRPT, Ground station • NRL: MODIS • SatObSys: SLA

  21. SEPTR

  22. SEPTR data in NRT on the webHigh bandwidth Ship-NURC satellite link NURC GEOS II Mirror GEOS II Time based scheduled synchronizations

  23. Common box

  24. Data and models

  25. S-TRANSFORM (SVP, 20m depth) ADRICOSM HOPS SEPTR NCOM ROMS

  26. Sound velocity at 20m SE MSSE

  27. Hindcast skills: SE vs MSSE Correlation STD SE Skill 0.9 Skill 0.1 MSSE SEPTR OBS.

  28. Forecast skills: SE vs MSSE SE MSSE Skill 0.1 Skill 0.9 SEPTR OBS.

  29. Multi-scale super-ensemble (MSSE) Optimal combination of processes instead of models SEPTR TEMP S-transform, multiple filter, wavelet Courtesy Paul Martin (NRLSSC) NCOM TEMP Errors on sound velocity profile ‘Standard’ Super-ensemble (SE) Multi-scale Super-ensemble (MSSE) ROMS TEMP 4-5 m/s 1-2 m/s Courtesy Jacopo Chiggiato (ARPA)

  30. Forecast: error on sound velocity SE MSSE

  31. Forecast: dynamic SE = KF+DLM

  32. Forecast: error on sound velocity UNBIASED ENSMEAN ENSMEAN Kalman filter DLM+error evolution SE

  33. Forecast skill on sound velocityWhole period and water column

  34. REMARKS • SE = paradigm data fusion • NATO framework: cheap (i.e. marginal cost) because forecasts are available • “Relocatable science”: [ocean, atmosphere, wave, surf], [shallow, deep], [in-situ, remote], [linear, non-linear] • Uncertainty as a direct by-product (e.g. std of models) • Interoperability, networking • Reprints available • 2 special issues of Journal of Marine Systems on REA

  35. Publications, work (1/2) • RSMAS+NURC+…: Eddy in the Gulf of Manfredonia: when? how? Baroclinic instability, history upstream, conservation of potential vorticity, Wind regimes? Seasonal signature, preconditioning? Relaxation-upwelling? • NURC+NRL+..: SE, MSSE with 4 wave models (+ wave-current interaction) • NURC+: MSSE/DSE at SEPTR and Thermistor sites, • eddy resolving from eddy permitting models • NURC+RSMAS+…: Hyper-ensemble+drifters • RSMAS+NURC+…: OSSE in fine-scale area, inertial oscillations, hyperbolic point, track separation

  36. Publications, work (2/2) • CNR/UCOL+NURC+…: Turbulence, GOTM, ROMS • HR+…: Eastern Adriatic Current • NURC+NRLMRY+…: 2-way coupling, COAMPS, SAR • NRL+UNIAN+NURC+…: dense water, fluxes, HOPS • HR+NRL+…:internal waves • NURC+ICRAM+IOF+…:optics, 1D model • NURC+ARPA+NRL+…: MS-EVA on models

  37. CONCLUSIONS • IMPORTANT • Exploit DART model/data set and GEOS • Great inter-disciplinary potential • Available for the whole DART community • Also releasable elsewhere - on request • Data sharing agreement, check who are natural • co-authors, acknowledgments • EVENTS • DART workshop, NURC, 23/24 April • MREA/DART conference, Villa Marigola, • Lerici, 25-27 September, proceedings • Last but not least: thanks for this excellent collaboration!!!

  38. Forecast errors Operational Models - no CTD data ass. - two grids (coarse, fine) Analysis FINE NCOM COARSE NCOM SE FINE NCOM SE COARSE+FINE NCOM

  39. Forecast errors Operational Models - with CTD data ass. - two training options Single HOPS Model Runs Data Ass. SE I (using 2 models) Overall SE II (using 4 models) +2 NCOM models

  40. MREA04: DRIFTERS

  41. Hyper-ensembles Ocean Meteo Hyper-ens. HOPS ALADIN FR Linear HE Non-linear HE NCOM COAMPS

  42. Drifter tracks True drifter Ocean advection 48 h forecast Rule of thumb Hyper-ensembles

  43. Hyper-ensemble statistics Julian day

  44. Strong Wind Event (Bora) R. Signell

  45. Standard vs refined turbulence scheme R. Signell

  46. ADV WIND ADV+WIND ADV+WIND+STK Indiv. Mod. SE 5, 10, 25 and 50 days

  47. ADV WIND Indiv. mod. uncertainty: 14 Feb 2003 ADV+WIND ADV+WIND+STOKES

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