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Multi-model Super-Ensembles Applied to Dynamics of the Adriatic

This study explores the use of multi-model super-ensembles for forecasting the dynamics of the Adriatic Sea. It focuses on acoustic properties, surface drift, initial and boundary conditions, and statistical parameterization to improve forecast accuracy and estimate uncertainty.

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Multi-model Super-Ensembles Applied to Dynamics of the Adriatic

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  1. Multi-model Super-Ensembles Applied to Dynamics of the Adriatic NRL Stennis 15-17 November 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. SE Weights

  6. Forecast errors on sound velocity Analysis Single models HOPS IHPO HOPS HRV NCOM COARSE NCOM FINE SE HOPS HRV FINE NCOM 2 HOPS 2 NCOM 4 models

  7. SE Sound speed profile errors

  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. MREA04: DRIFTERS

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

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

  12. Hyper-ensemble statistics Julian day

  13. Strong Wind Event (Bora) R. Signell

  14. Standard vs refined turbulence scheme R. Signell

  15. ADRIA02-03 drifters (Jan-Feb)

  16. Analysis: 14 Feb 2003

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

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

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

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

  21. ADV WIND RoT Indiv. mod. uncertainty: 14 Feb 2003 (cross-validation) ADV+WIND RoT ADV+WIND+STOKES

  22. ADV WIND RoT SEs uncertainty on 14 Feb 2003 (cross-validation) ADV+WIND RoT ADV+WIND+STOKES

  23. SEs INDIV

  24. 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…

  25. 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…

  26. Lorenz equations

  27. MSSEs SEs MSSEs MSSEs SEs SEs

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

  29. 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)

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

  31. 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

  32. SEPTR

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

  34. Common box

  35. Data and models: sound velocity

  36. 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)

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

  38. Sound velocity at 20m SE MSSE

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

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

  41. Forecast: error on sound velocity SE MSSE

  42. Forecast: dynamic SE = KF+DLM Indiv models KF+uncertainty Forecast KF+uncertainty Sound velocity anomaly (m/s)

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

  44. A priori forecast uncertainties UNBIASED ENSMEAN ENSMEAN Kalman filter DLM+error evolution

  45. Forecast skill on sound velocityWhole period and water column UEM Best indiv. model EM SE KF

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