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NATO Undersea Research Centre Partnering for Maritime Innovation
NRL Stennis15-17 November 2006
Michel Rixen
Multi-model Super-Ensembles Applied to
Dynamics of the Adriatic
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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]
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Models DataWeights
• 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
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MREA04: sound velocity (100m)
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SE Weights
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SE
Single models
Analysis
Forecast errors on sound velocity
HOPS IHPO HOPS HRV NCOM COARSE NCOM FINE
HOPS HRV FINE NCOM 2 HOPS 2 NCOM 4 models
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SE Sound speed profile errors
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PS
-IH
PO
(1)
HO
PS
-Har
v. (
2)C
oars
e N
CO
M (
3)
Fin
e N
CO
M (
4)S
E (
2)S
E (
4)S
E (
1, 2
)S
E (
3, 4
)S
E (
1 to
4)
HO
PS
-IH
PO
(1)
HO
PS
-Har
v. (
2)C
oars
e N
CO
M (
3)
Fin
e N
CO
M (
4)S
E (
2)S
E (
4)S
E (
1, 2
)S
E (
3, 4
)S
E (
1 to
4)
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MREA04: DRIFTERS
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Hyper-ens.Ocean Meteo
Hyper-ensembles
HOPS
NCOM
ALADIN FR
COAMPS
Linear HE
Non-linear HE
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Drifter tracks
Ocean advection
Rule of thumb
Hyper-ensembles
True drifter
48 h forecast
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Hyper-ensemble statistics
Julian day
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Strong Wind Event (Bora)
R. Signell
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Standard vs refined turbulence scheme
R. Signell
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ADRIA02-03 drifters (Jan-Feb)
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Analysis: 14 Feb 2003
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ADV WIND RoT
ADV+WIND RoT ADV+WIND+STOKES
Indiv. Forecast err.: 14 Feb 2003 (12 Feb 2003+ 48h)
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ADV WIND RoT
ADV+WIND RoT ADV+WIND+STOKES
SEs forecast err: 14 Feb 2003 (12 Feb 2003+ 48h)
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SE 5, 10, 25 and 50 daysIndiv. Mod.
ADVWINDADV+WINDADV+WIND+STK
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Drifter tracks
Ocean advection
Ocean+Stokes
SE
True
Stokes
Unbiased single models
24 h forecast
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ADV WIND RoT
ADV+WIND RoT ADV+WIND+STOKES
Indiv. mod. uncertainty: 14 Feb 2003 (cross-validation)
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ADV WIND RoT
ADV+WIND RoT ADV+WIND+STOKES
SEs uncertainty on 14 Feb 2003 (cross-validation)
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INDIV SEs
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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
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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…
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Lorenz equations
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SEs MSSEs SEs MSSEs SEs MSSEs
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• Gulf of Manfredonia & Gargano Peninsula
• Mid-Adriatic• Whole Adriatic
• Critical mass of research and ressources
Dynamics of the Adriatic in Real-Time
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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)
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PARTNERS• 33 institutions (on board+home institutions): • 10 USA, 15 ITA, 1 GRC, 1 DEU, 1 BEL, 2 FRA PfP : 4 HRV, (1 ALB)
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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
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SEPTR
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SEPTR data in NRT on the webHigh bandwidth Ship-NURC satellite link
NURNURCC
GEOS II Mirror
GEOS II
Time based scheduled synchronizations
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Common box
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Data and models: sound velocity
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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
SEPTRTEMP
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)
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S-TRANSFORM (SVP, 20m depth)
SEPTR
ADRICOSM HOPS
NCOM ROMS
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Sound velocity at 20m
SE
MSSE
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Hindcast skills: SE vs MSSE
SEPTR OBS.
MSSE
SE
Skill 0.1 Skill 0.9
STD
Correlation
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Forecast skills: SE vs MSSE
SEPTR OBS.
MSSE
SE
Skill 0.1Skill 0.9
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Forecast: error on sound velocity
SE
MSSE
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Forecast: dynamic SE = KF+DLM
KF+uncertaintyForecast
Indiv models
KF+uncertainty
Sound velocity anomaly (m/s)
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Forecast: error on sound velocity
ENSMEAN UNBIASEDENSMEAN
SE Kalman filterDLM+error evolution
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A priori forecast uncertainties
ENSMEAN UNBIASEDENSMEAN
Kalman filterDLM+error evolution
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Forecast skill on sound velocityWhole period and water column
KF
SE
UEM
EMBest indiv.model
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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
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Questions ?
At the risk of repeating myself, WRT DART
Thanks to NRL !Thanks Jeff !
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Operational Models - no CTD data ass. - two grids (coarse, fine)
Analysis
Forecast errors
COARSE NCOM
SEFINE NCOM
FINE NCOM
SE COARSE+FINE
NCOM
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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