NATO UNCLASSIFIED NATO Undersea Research Centre Partnering for Maritime Innovation NRL Stennis November 2006 Michel Rixen Multi-model Super-Ensembles Applied to Dynamics of the Adriatic

NATO UNCLASSIFIED 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]

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

NATO UNCLASSIFIED 4 MREA04: sound velocity (100m)

NATO UNCLASSIFIED 5 SE Weights

NATO UNCLASSIFIED 6 SE Single models Analysis Forecast errors on sound velocity HOPS IHPOHOPS HRVNCOM COARSENCOM FINE HOPS HRVFINE NCOM2 HOPS2 NCOM4 models

NATO UNCLASSIFIED 7 SE Sound speed profile errors

NATO UNCLASSIFIED 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)

NATO UNCLASSIFIED 9 MREA04: DRIFTERS

NATO UNCLASSIFIED 10 Hyper-ens.OceanMeteo Hyper-ensembles HOPS NCOM ALADIN FR COAMPS Linear HE Non-linear HE

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

NATO UNCLASSIFIED 12 Hyper-ensemble statistics Julian day

NATO UNCLASSIFIED 13 Strong Wind Event (Bora) R. Signell

NATO UNCLASSIFIED 14 Standard vs refined turbulence scheme R. Signell

NATO UNCLASSIFIED 15 ADRIA02-03 drifters (Jan-Feb)

NATO UNCLASSIFIED 16 Analysis: 14 Feb 2003

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

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

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

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NATO UNCLASSIFIED 21 Drifter tracks Ocean advection Ocean+Stokes SE True Stokes Unbiased single models 24 h forecast

NATO UNCLASSIFIED 22 ADV WIND RoT ADV+WIND RoT ADV+WIND+STOKES Indiv. mod. uncertainty: 14 Feb 2003 (cross-validation)

NATO UNCLASSIFIED 23 ADV WIND RoT ADV+WIND RoT ADV+WIND+STOKES SEs uncertainty on 14 Feb 2003 (cross-validation)

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NATO UNCLASSIFIED 26 INDIV SEs

NATO UNCLASSIFIED 27 MS-EVA (JRP Harvard) multi-scale interactive nonlinear intermittent in space episodic in time E.g. wavelet Selecting the right processes at the right time… New methodology utilizing multiple scale window decomposition in space and time of a model

NATO UNCLASSIFIED 28 Note: Energy/vorticity/mass conservation issues SE and MS-EVA=MSSE Model 1 Model 2 Model N MSSE combines optimally the strengths of all models at any time at different scales Selecting the right processes from the right models at the right time…

NATO UNCLASSIFIED 29 Lorenz equations

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NATO UNCLASSIFIED 32 SEs MSSEs SEs MSSEs SEs MSSEs

NATO UNCLASSIFIED 33 Gulf of Manfredonia & Gargano Peninsula Mid-Adriatic Whole Adriatic Critical mass of research and ressources Dynamics of the Adriatic in Real-Time

NATO UNCLASSIFIED 34 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)

NATO UNCLASSIFIED 35 PARTNERS 33 institutions (on board+home institutions): 10 USA, 15 ITA, 1 GRC, 1 DEU, 1 BEL, 2 FRA PfP : 4 HRV, (1 ALB)

NATO UNCLASSIFIED 36 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

NATO UNCLASSIFIED 37 SEPTR

NATO UNCLASSIFIED 38 SEPTR data in NRT on the web High bandwidth Ship-NURC satellite link NUR C GEOS II Mirror GEOS II Time based scheduled synchronizations

NATO UNCLASSIFIED 39 Common box

NATO UNCLASSIFIED 40 Data and models: sound velocity

NATO UNCLASSIFIED 41 Multi-scale super-ensemble (MSSE) NCOM TEMP ROMS TEMP ‘Standard’ Super-ensemble (SE) Multi-scale Super-ensemble (MSSE) Optimal combination of processes instead of models SEPTR TEMP S-transform, multiple filter, wavelet Errors on sound velocity profile 4-5 m/s 1-2 m/s Courtesy Paul Martin (NRLSSC) Courtesy Jacopo Chiggiato (ARPA)

NATO UNCLASSIFIED 42 S-TRANSFORM (SVP, 20m depth) SEPTR ADRICOSMHOPS NCOMROMS

NATO UNCLASSIFIED 43 Sound velocity at 20m SE MSSE

NATO UNCLASSIFIED 44 Hindcast skills: SE vs MSSE SEPTR OBS. MSSE SE Skill 0.1 Skill 0.9 STD Correlation

NATO UNCLASSIFIED 45 Forecast skills: SE vs MSSE SEPTR OBS. MSSE SE Skill 0.1 Skill 0.9

NATO UNCLASSIFIED 46 Forecast: error on sound velocity SE MSSE

NATO UNCLASSIFIED 47 Forecast: dynamic SE = KF+DLM KF+uncertainty Forecast Indiv models KF+uncertainty Sound velocity anomaly (m/s)

NATO UNCLASSIFIED 48 Forecast: error on sound velocity ENSMEAN UNBIASED ENSMEAN SE Kalman filter DLM+error evolution

NATO UNCLASSIFIED 49 A priori forecast uncertainties ENSMEAN UNBIASED ENSMEAN Kalman filter DLM+error evolution

NATO UNCLASSIFIED 50 Forecast skill on sound velocity Whole period and water column KF SE UEM EM Best indiv. model

NATO UNCLASSIFIED 51 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] 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 52 Questions ? At the risk of repeating myself, WRT DART Thanks to NRL ! Thanks Jeff !

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NATO UNCLASSIFIED 55 Operational Models - no CTD data ass. - two grids (coarse, fine) Analysis Forecast errors COARSE NCOM SE FINE NCOM SE COARSE+FINE NCOM

NATO UNCLASSIFIED 56 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 Forecast errors