pilot topaz reanalysis (2003-2008)

Pilot TOPAZ reanalysis (2003-2008)

Pavel Sakov, Francois Counillon and Laurent Bertino

Nansen Environmental and Remote Sensing Center, Norway

9 September 2010, Bergen

NERSCNERSC

OutlineAbout this talk

Data assimilation system design

Observations

Analysis diagnostics: DFS and SRF

Innovation statistics

ValidationMoviesDriftersIce concentrationSome intercomparisons

ICECSSHSSSSST

The Arctic

Parameter estimation

Conclusions

About this talk

This talk concentrates on:

◮ General DA system design and performance

◮ Some outcomes of the pilot (2003-2008) MyOcean TOPAZreanalysis

◮ In the context of what can be expected from the main(1990-2010) reanalysis

We will not talk about...

◮ TOPAZ3 → TOPAZ4 development (model + DA)

◮ Ensemble generation

◮ System spin-up

◮ Numerical and operational issues

Data assimilation system design

◮ 1. Rank issues

Ensemble size = 100 → localisation radius = 300 km.

(Effective radius ∼ 90 km → ∼ 13 × 13 × 28 local domain.)

◮ 2. Assimilation window

1 week

◮ 3. Scheme

DEnKF, asynchronous

◮ 4. Localisation

Distance-based (non-adaptive) local analysis, with G&C tapering

◮ 5. Moderation

Adaptive observation pre-screening, inflation 1%, R-factor = 2.

◮ 6. Parameter estimation

MSSH, SST, π∗.

Observations

Type Number After SO Asynchronous

Track SLA 9 · 104 4 · 104 Yes

SST (Reynolds) 6 · 103 ” No

SST (OSTIA) 2 · 106 2.2 · 105 No

In-situ T 2 · 104 + 1.5 · 104 6 · 103 No

In-situ S 2 · 104 + 1.5 · 104 6 · 103 No

Ice conc. (AMSR) 1.6 · 105 105 No

Ice drift (CERSAT) 6 · 103 ” Yes

Total 2.3 · 106 4 · 105

Analysis diagnostics: DFS and SRF

DFS = “Degrees of Freedomof Signal”

DFS = tr(KH)

0 5 10 15 20 25 30 35 400

1

2

3

4

5

6

7SVD spectrum of ensemble anomalies

mode

mag

nitu

de

DFS = 0.82

forecastanalysis

SRF = “Spread ReductionFactor”

SRF =√

tr(HPf HTR−1)tr(HPaHTR−1)

− 1

0 1 2 3 4 5 6 7 8 9 10 110

0.05

0.1

0.15

0.2

0.25

0.3

Ensemble spread

step

spre

ad

<SRF> = 0.172

forecastanalysis

DFS = 0.27 + 0.23 + . . .

− 0.02 − 0.02 = 0.82

Total DFS and SRF (for 24 April 2008)

DFS and SRF for TSLA

DFS and SRF for SST

DFS and SRF for ICEC

DFS and SRF for T

DFS and SRF for S

DFS of UICE

DFS of VICE

Innovation statistics

innovation = d − Hxf = observations − forecast

120 oW

60oW

0o

60

o E

120 oE

180o W

0 o

20 oN

40 oN

60oN

80

o N

1

2

3

4

Innovation statistics for TSLA

2003 2004 2005 2006 2007 2008

0

0.05

0.1

0.15

0.2

0.25region 1

0

2000

4000

biasrmsσ

ensσ

tot

# obs.

2003 2004 2005 2006 2007 2008

0

0.05

0.1

0.15region 2

0

5000

2003 2004 2005 2006 2007 2008−0.04

−0.02

0

0.02

0.04

0.06

0.08

0.1

region 4

0

5000

Innovation statistics for SST

2003 2004 2005 2006 2007 2008

−1.5

−1

−0.5

0

0.5

1

1.5

region 1

0

2

x 104

biasrmsσ

ensσ

tot

# obs.

2003 2004 2005 2006 2007 2008

−1

−0.5

0

0.5

1

1.5region 2

0

2

x 104

2003 2004 2005 2006 2007 2008−1

−0.5

0

0.5

1

region 4

0

x 104

Innovation statistics for T, 0 − 10 m

2003 2004 2005 2006 2007 2008

−1

0

1

2

3

region 1

0

20

40

60

80

biasrmsσ

ensσ

tot

# obs.

2003 2004 2005 2006 2007 2008

−1

−0.5

0

0.5

1

1.5

2

region 2

0

50

100

150

2003 2004 2005 2006 2007 2008

−1.5

−1

−0.5

0

0.5

1

1.5

2

region 3

0

50

100

150

200

250

300

350

2003 2004 2005 2006 2007 2008

−1

−0.5

0

0.5

1

1.5

region 4

0

10

20

30

40

50

60


2003 2004 2005 2006 2007 2008

−1

0

1

2

3

4

5

region 1

0

10

20

30

40

50

60

70

biasrmsσ

ensσ

tot

# obs.

2003 2004 2005 2006 2007 2008

−1

0

1

2

3

region 2

0

20

40

60

80

100

120

140

2003 2004 2005 2006 2007 2008−1

−0.5

0

0.5

1

1.5

2

2.5

region 3

0

200

400

600

800

2003 2004 2005 2006 2007 2008−1

0

1

2

3

4

region 4

0

20

40

60

80


2003 2004 2005 2006 2007 2008

0

1

2

3

region 1

0

50

100

150

biasrmsσ

ensσ

tot

# obs.

2003 2004 2005 2006 2007 2008

−1

−0.5

0

0.5

1

1.5

2

2.5region 2

0

50

100

150

2003 2004 2005 2006 2007 2008

−2

−1

0

1

2

3

region 3

0

50

100

150

200

250

2003 2004 2005 2006 2007 2008

−0.5

0

0.5

1

1.5

region 4

0

20

40

60

80

Innovation statistics for S, 0 − 10 m

2003 2004 2005 2006 2007 2008−0.5

0

0.5

1

region 1

0

10

20

30

40

50

60

70

biasrmsσ

ensσ

tot

# obs.

2003 2004 2005 2006 2007 2008−0.1

0

0.1

0.2

0.3

0.4

0.5

0.6

region 2

0

50

100

150

2003 2004 2005 2006 2007 2008

−1

0

1

2

3

4

5

6

region 3

0

50

100

150

200

250

300

2003 2004 2005 2006 2007 2008

−0.5

0

0.5

1

region 4

0

10

20

30

40

50

60


2003 2004 2005 2006 2007 2008−0.2

0

0.2

0.4

0.6

0.8

region 1

0

10

20

30

40

50

60

70

biasrmsσ

ensσ

tot

# obs.

2003 2004 2005 2006 2007 2008

−0.1

0

0.1

0.2

0.3

0.4

region 2

0

20

40

60

80

100

120

140

2003 2004 2005 2006 2007 2008

0

0.2

0.4

0.6

0.8

1

1.2

region 3

0

200

400

600

800

2003 2004 2005 2006 2007 2008

0

0.1

0.2

0.3

0.4

0.5

0.6

region 4

0

20

40

60

80


2003 2004 2005 2006 2007 2008

0

0.1

0.2

0.3

0.4

0.5region 1

0

50

100

150

200

biasrmsσ

ensσ

tot

# obs.

2003 2004 2005 2006 2007 2008−0.1

0

0.1

0.2

0.3

0.4

region 2

0

50

100

150

2003 2004 2005 2006 2007 2008

−0.1

0

0.1

0.2

0.3region 3

0

50

100

150

200

250

2003 2004 2005 2006 2007 2008

−0.1

0

0.1

0.2

0.3

region 4

0

20

40

60

80

Innovation statistics for ICEC

2003 2004 2005 2006 2007 2008

−0.05

0

0.05

0.1

0.15

0.2

0.25

0.3

0

1

2

3

4

5

x 104

biasrmsσ

ensσ

tot

# obs.

SLA, SSS and SST movies for 2007

• SSH

• Salinity

• Temperature

SLA/SSH versus drifters - 9 January 2008 (± 4 days)

SSH versus drifters - 9 January 2008 (± 4 days)

Ice concentration - 17 August 2005

Ice concentration - 30 April 2008

ICEC on 9 January 2008: reanalysis versus observations

ICEC on 9 January 2008: reanalysis versus TOPAZ3

ICEC on 9 January 2008: reanalysis versus free run

SLA on 9 January 2008: reanalysis versus TOPAZ3

SLA on 9 January 2008: reanalysis versus free run

SSS on 9 January 2008: reanalysis versus GDEM

climatology

SSS on 9 January 2008: reanalysis versus PHC climatology

SSS on 9 January 2008: reanalysis versus TOPAZ3

SSS on 9 January 2008: reanalysis versus free run

SST on 9 January 2008: reanalysis versus GDEM

climatology

SST on 9 January 2008: reanalysis versus TOPAZ3

SST on 9 January 2008: reanalysis versus free run

Arctic: SSH and Ice thickness movies for 2007

• SSH

• Ice thickness

(Thanks to Alexander Korablev for providing in-situ observation data for North

Atlantic and Arctic)

SSS, January 2007 and 2008

S at 100m, January 2007 and 2008

Parameter estimation

◮ Started from 20 February 2008

◮ Results shown after 10 cycles

To be done

Decisions:

◮ Use of parameter estimation in the main reanalysis

Validations:

◮ Time/area average profiles

◮ Taylor diagrams

◮ Some fancy movies with drifters, perhaps

Conclusions

◮ The TOPAZ reanalysis system has been developed and isclose now to its final shape.

◮ It proved to be robust and computationally effective. Beinggranted 100 CPUs on Hexagon, it will be possible to completethe 20-year reanalysis in 1 year.

◮ The quality of the analysis seems to be good, both in terms ofdynamical balance and match to observations and climatology.There seem to be a substantial improvement compared toTOPAZ3, particularly in the Gulf Stream and Arctic regions.

◮ The final shape of the system is to be decided by outcomes ofparameter estimation experiment.

References

Bertino, L. and K. A. Lisæter, 2008: The TOPAZ monitoring and prediction system for the Atlantic and

Arctic Oceans. J. Oper. Oceanogr., 1, 15–19.

Oke, P. R., G. B. Brassington, D. A. Griffin, and A. Schiller, 2008: The Bluelink ocean data assimilation

system (BODAS). Ocean Model., 21, 46–70.

— 2010: Ocean data assimilation: a case for ensemble optimal interpolation. AMOJ, 59, 67–76.

Sakov, P., G. Evensen, and L. Bertino, 2010: Asynchronous data assimilation with the EnKF. Tellus, 62A,

24–29, doi:10.1111/j.1600-0870.2009.00417.x.

Sakov, P. and P. R. Oke, 2008: A deterministic formulation of the ensemble Kalman filter: an alternative to

ensemble square root filters. Tellus, 60A, 361–371.

Simmons, U. S. D. D., A. and S. Kobayashi, 2007: PHC: A global ocean hydrography with a high-quality

Arctic Ocean. J. Climate, 14, 25–35.

Teague, C. M. J., W. J. and P. J. Hogan, 1990: Generalized Digital Environmental Model and Levitus

climatologies. J. Geophys. Res., 95, 7167–7183.

pilot topaz reanalysis (2003-2008)

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