NATO Undersea Research Centre Partnering for Maritime Innovation
- Slides: 56
NATO Undersea Research Centre Partnering for Maritime Innovation Multi-model Super-Ensembles Applied to Dynamics of the Adriatic NRL Stennis 15 -17 November 2006 Michel Rixen rixen@nurc. nato. int NATO UNCLASSIFIED
Ensembles… • • 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] 2 particular research lines relevant to MILOC/EOS/NURC/NATO • Acoustic properties • Surface drift 2 NATO UNCLASSIFIED
Super-Ensembles (SE)… Models • • Weights Data 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 3 NATO UNCLASSIFIED
MREA 04: sound velocity (100 m) 4 NATO UNCLASSIFIED
SE Weights 5 NATO UNCLASSIFIED
Forecast errors on sound velocity Analysis HOPS IHPO HOPS HRV 6 HOPS HRV FINE NCOM COARSE 2 HOPS NCOM FINE 2 NCOM Single models 4 models SE NATO UNCLASSIFIED
SE Sound speed profile errors 7 NATO UNCLASSIFIED
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) 8 NATO UNCLASSIFIED
MREA 04: DRIFTERS 9 NATO UNCLASSIFIED
Hyper-ensembles Ocean 10 Meteo HOPS ALADIN FR NCOM COAMPS Hyper-ens. Linear HE Non-linear HE NATO UNCLASSIFIED
Drifter tracks True drifter Ocean advection 48 h forecast Rule of thumb Hyper-ensembles 11 NATO UNCLASSIFIED
Hyper-ensemble statistics 12 Julian day NATO UNCLASSIFIED
Strong Wind Event (Bora) R. Signell 13 NATO UNCLASSIFIED
Standard vs refined turbulence scheme 14 R. Signell NATO UNCLASSIFIED
ADRIA 02 -03 drifters (Jan-Feb) 15 NATO UNCLASSIFIED
Analysis: 14 Feb 2003 16 NATO UNCLASSIFIED
ADV WIND Ro. T Indiv. Forecast err. : 14 Feb 2003 (12 Feb 2003+ 48 h) ADV+WIND Ro. T 17 ADV+WIND+STOKES NATO UNCLASSIFIED
ADV WIND Ro. T SEs forecast err: 14 Feb 2003 (12 Feb 2003+ 48 h) ADV+WIND Ro. T 18 ADV+WIND+STOKES NATO UNCLASSIFIED
ADV WIND ADV+WIND+STK Indiv. Mod. 19 SE 5, 10, 25 and 50 days NATO UNCLASSIFIED
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Drifter tracks Ocean advection Ocean+Stokes Unbiased single models 21 SE 24 h forecast True NATO UNCLASSIFIED
ADV WIND Ro. T Indiv. mod. uncertainty: 14 Feb 2003 (cross-validation) ADV+WIND Ro. T 22 ADV+WIND+STOKES NATO UNCLASSIFIED
ADV WIND Ro. T SEs uncertainty on 14 Feb 2003 (cross-validation) ADV+WIND Ro. T 23 ADV+WIND+STOKES NATO UNCLASSIFIED
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INDIV 26 SEs NATO UNCLASSIFIED
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… 27 NATO UNCLASSIFIED
SE and MS-EVA=MSSE Model 1 Model 2 Model N 28 MSSE combines optimally the strengths of all models at any time at different scales Note: Energy/vorticity/mass conservation issues Selecting the right processes from the right models at the right time… NATO UNCLASSIFIED
Lorenz equations 29 NATO UNCLASSIFIED
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SEs 32 MSSEs SEs MSSEs NATO UNCLASSIFIED
Dynamics of the Adriatic in Real-Time • Gulf of Manfredonia & Gargano Peninsula • Mid-Adriatic • Whole Adriatic • Critical mass of research and ressources 33 NATO UNCLASSIFIED
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) • • • 1 A 5: ensemble modeling+uncertainty 1 A 2: air-sea interaction, coupling/turbulence 1 D 1: data fusion & remote sensing 1 D 3: geospatial data services • ONR projects: – – • 34 NRL-HRV on internal tides NICOP program on turbulence EOREA ESA (Sat. Ob. Sys/Flyby/ITN/NURC) NATO UNCLASSIFIED
PARTNERS • 33 institutions (on board+home institutions): • 10 USA, 15 ITA, 1 GRC, 1 DEU, 1 BEL, 2 FRA 35 Pf. P : 4 HRV, (1 ALB) NATO UNCLASSIFIED
Highlights IN-SITU • SEPTR (1 NURC, 3 NRL) • BARNY (2 NURC, 13 NRL, 2 HRV) • 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 • Sat. Ob. Sys: SLA 36 NATO UNCLASSIFIED
SEPTR 37 NATO UNCLASSIFIED
SEPTR data in NRT on the web High bandwidth Ship-NURC satellite link NUR C GEOS II Mirror GEOS II Time based scheduled synchronizations 38 NATO UNCLASSIFIED
Common box 39 NATO UNCLASSIFIED
Data and models: sound velocity 40 NATO UNCLASSIFIED
Multi-scale super-ensemble (MSSE) Optimal combination of processes instead of models SEPTR TEMP Courtesy Paul Martin (NRLSSC) NCOM TEMP ROMS TEMP 41 Courtesy Jacopo Chiggiato (ARPA) S-transform, multiple filter, wavelet Errors on sound velocity profile ‘Standard’ Super-ensemble (SE) Multi-scale Super-ensemble (MSSE) 4 -5 m/s 1 -2 m/s NATO UNCLASSIFIED
S-TRANSFORM (SVP, 20 m depth) ADRICOSM HOPS SEPTR NCOM 42 ROMS NATO UNCLASSIFIED
Sound velocity at 20 m SE MSSE 43 NATO UNCLASSIFIED
Hindcast skills: SE vs MSSE Co rre lat STD ion SE Skill 0. 1 Skill 0. 9 MSSE SEPTR OBS. 44 NATO UNCLASSIFIED
Forecast skills: SE vs MSSE SE MSSE Skill 0. 1 Skill 0. 9 SEPTR OBS. 45 NATO UNCLASSIFIED
Forecast: error on sound velocity SE MSSE 46 NATO UNCLASSIFIED
Forecast: dynamic SE = KF+DLM Indiv models KF+uncertainty Forecast Sound velocity anomaly (m/s) 47 NATO UNCLASSIFIED
Forecast: error on sound velocity ENSMEAN SE 48 UNBIASED ENSMEAN Kalman filter DLM+error evolution NATO UNCLASSIFIED
A priori forecast uncertainties ENSMEAN UNBIASED ENSMEAN Kalman filter DLM+error evolution 49 NATO UNCLASSIFIED
Forecast skill on sound velocity Whole period and water column UEM Best indiv. model EM SE KF 50 NATO UNCLASSIFIED
Conclusions • SE = paradigm for improved reliability and accuracy • NATO framework: cheap (i. e. marginal cost) because model forecasts are available • “Relocatable science”: [ocean, atmosphere, wave, surf], [shallow, deep], [in-situ, remote], [linear, non-linear] 51 • Information fusion per-se, Recognized environmental picture • Uncertainty as a direct by-product (e. g. std of models) • Interoperability, network enabled capability • Information and decision superiority NATO UNCLASSIFIED
At the risk of repeating myself, WRT DART Thanks to NRL ! Thanks Jeff ! Questions ? 52 NATO UNCLASSIFIED
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Forecast errors Analysis COARSE NCOM SE FINE NCOM 55 Operational Models - no CTD data ass. - two grids (coarse, fine) FINE NCOM SE COARSE+FINE NCOM NATO UNCLASSIFIED
Forecast errors Operational Models Single HOPS Model Runs - with CTD data ass. - two training options Data Ass. SE I (using 2 models) Overall SE II (using 4 models) +2 NCOM models 56 NATO UNCLASSIFIED
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