NATO UNCLASSIFIED NATO Undersea Research Centre Partnering for Maritime Innovation NRL Stennis 15-17 November 2006 Michel Rixen [email protected] Multi-model

NATO UNCLASSIFIED

NATO Undersea Research Centre Partnering for Maritime Innovation

NRL Stennis15-17 November 2006

Michel Rixen

[email protected]

Multi-model Super-Ensembles Applied to

Dynamics of the Adriatic

2 NATO UNCLASSIFIED

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 NATO UNCLASSIFIED

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

4 NATO UNCLASSIFIED

MREA04: sound velocity (100m)

5 NATO UNCLASSIFIED

SE Weights

6 NATO UNCLASSIFIED

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

7 NATO UNCLASSIFIED

SE Sound speed profile errors

8 NATO UNCLASSIFIEDHO

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)

9 NATO UNCLASSIFIED

MREA04: DRIFTERS

10 NATO UNCLASSIFIED

Hyper-ens.Ocean Meteo

Hyper-ensembles

HOPS

NCOM

ALADIN FR

COAMPS

Linear HE

Non-linear HE


Drifter tracks

Ocean advection

Rule of thumb

Hyper-ensembles

True drifter

48 h forecast


Hyper-ensemble statistics

Julian day


Strong Wind Event (Bora)

R. Signell


Standard vs refined turbulence scheme

R. Signell


ADRIA02-03 drifters (Jan-Feb)


Analysis: 14 Feb 2003


ADV WIND RoT

ADV+WIND RoT ADV+WIND+STOKES

Indiv. Forecast err.: 14 Feb 2003 (12 Feb 2003+ 48h)


ADV WIND RoT


SEs forecast err: 14 Feb 2003 (12 Feb 2003+ 48h)


SE 5, 10, 25 and 50 daysIndiv. Mod.

ADVWINDADV+WINDADV+WIND+STK



Drifter tracks

Ocean advection

Ocean+Stokes

SE

True

Stokes

Unbiased single models

24 h forecast


ADV WIND RoT


Indiv. mod. uncertainty: 14 Feb 2003 (cross-validation)


ADV WIND RoT


SEs uncertainty on 14 Feb 2003 (cross-validation)




INDIV SEs


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


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…


Lorenz equations




SEs MSSEs SEs MSSEs SEs MSSEs


• Gulf of Manfredonia & Gargano Peninsula

• Mid-Adriatic• Whole Adriatic

• Critical mass of research and ressources

Dynamics of the Adriatic in Real-Time


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)


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


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


SEPTR


SEPTR data in NRT on the webHigh bandwidth Ship-NURC satellite link

NURNURCC

GEOS II Mirror

GEOS II

Time based scheduled synchronizations


Common box


Data and models: sound velocity


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)


S-TRANSFORM (SVP, 20m depth)

SEPTR

ADRICOSM HOPS

NCOM ROMS


Sound velocity at 20m

SE

MSSE


Hindcast skills: SE vs MSSE

SEPTR OBS.

MSSE

SE

Skill 0.1 Skill 0.9

STD

Correlation


Forecast skills: SE vs MSSE

SEPTR OBS.

MSSE

SE

Skill 0.1Skill 0.9


Forecast: error on sound velocity

SE

MSSE


Forecast: dynamic SE = KF+DLM

KF+uncertaintyForecast

Indiv models

KF+uncertainty

Sound velocity anomaly (m/s)


Forecast: error on sound velocity

ENSMEAN UNBIASEDENSMEAN

SE Kalman filterDLM+error evolution


A priori forecast uncertainties

ENSMEAN UNBIASEDENSMEAN

Kalman filterDLM+error evolution


Forecast skill on sound velocityWhole period and water column

KF

SE

UEM

EMBest indiv.model


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


Questions ?

At the risk of repeating myself, WRT DART

Thanks to NRL !Thanks Jeff !




Operational Models - no CTD data ass. - two grids (coarse, fine)

Analysis

Forecast errors

COARSE NCOM

SEFINE NCOM

FINE NCOM

SE COARSE+FINE

NCOM


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

Documents

NATO UNCLASSIFIED NATO Undersea Research Centre Partnering for Maritime Innovation NRL Stennis 15-17 November 2006 Michel Rixen [email protected] Multi-model