Sea Ice Modellingand

Data Assimilation at CIS

Sea Ice Modellingand

Data Assimilation at CIS

Tom Carrieres

Sea Ice ModelsSea Ice Models

- Model developments began around 1987- First coupled ice-ocean models available in 1997- Currently models cover all CIS operational areas and

run daily in support of ice operations at around 99% reliability

- Now considered an integral part of CIS operations and some products are now totally model generated

Coupled Sea Ice-Ocean Models

Coupled Sea Ice-Ocean Models

- CIOM east coast (BIO)- CIOM nested grid (BIO)- Gulf of St Lawrence (IML)- Arctic Ocean (IOS 2004)- Archipelago (IOS 2005)- CIOM Great Lakes (CIS 2005)

Coupled Sea Ice-Ocean Models Model Products

Coupled Sea Ice-Ocean Models Model Products

• Existing Products• ice drift• ice concentration• ocean currents

• New Products- forecast ice charts- ice drift charts

Coupled Sea Ice-Ocean Models Model Verification/Case Studies

Coupled Sea Ice-Ocean Models Model Verification/Case Studies

• Routine verification• concentration vs daily ice charts• ice drift vs Tracker-Radarsat and beacons

• Conclusions• presentation/utility essential• operations involvement essential• output sometimes extremely helpful and sometimes

misleading

FutureFuture

IICWG-3 Direction• Move Ice Services from labour intensive subjective

analyses to NWP-like mode• Automated analyses based on as many data sources as possible• Allow analysts to focus on operationally critical areas

• Emphasis on national and international collaboration• Limited resources• Build on existing expertise• Shared benefits

Additional CIS Direction• Move models to CMC• Promote model utility and development with DFO• Raise awareness of CIS needs with ACSD/CMC

Data AssimilationData Assimilation

Operational• Analysis

• ice concentration/thickness – averaging from ice charts• ocean and other ice fields from model forecast

• Initialization• Ice concentration/thickness – nudging or insertion• All other fields – insertion

Development• Analysis

• ice concentration/thickness – statistical interpolation using ice charts or SSM/I

• SST - statistical interpolation using AVHRR/AMSR

Statistical InterpolationStatistical Interpolation

K

kkBkOikiBiA nGnGWnGnG

1

)],(),([),(),( rrrr

Subscript A=analysis fieldB=background field (model)O=observed field

observed data are located at irregularly spaced points rk

analysis and model data are situated on model grid ri

weights Wik balance the influence of the observationand background values to minimize the expected errorin the analysis field

Determination of WeightsDetermination of Weights

K

likBlkOOlkBilW

1

2 ),()],(),([ rrrrrr

),( lkB rr),( lkO rr

the background and observation errorcorrelation for the locations rk and ri

2O the ratio of observation error variance to

background error variance

If we assume errors are homogeneous:

Error CorrelationsError Correlations

2

2

2

2)(

exp),(h

lklklkB L

hh

L

rrrr

hk hi bottom depths (in m)L horizontal decorrelation length (10 km) Lh depth decorrelation length (50 m)

kllkBlkO ),(5.0),( rrrr

kl the Kronecker delta

Background error correlation:

Observation error correlation:

Optimizing S.I. parameterOptimizing S.I. parameter

Input parameters to statistical interpolation 1. ε2 – relative weighting parameter

2. L – horizontal decorrelation length

3. Lh – depth decorrelation length (50m)

We want to isolate the values of these parameters that will generate the best analysis field with the lowest error

2 2

Optimizing Parameters for Ice Chart Data

Optimizing Parameters for Ice Chart Data

• Background – initial “best guess” field• 24-hour forecast from model run of

previous day

• Observation• Daily ice charts

• Verification• Radarsat image analysis

Source Data Ice charts valid 2003-05-13

Source Data Ice charts valid 2003-05-13

left - Radarsat image analysis chart valid 09:56 UTCcentre - daily ice chart valid 18:00 UTCright - Radarsat image analysis chart valid 21:18 UTC

Starting Point for o2Starting Point for o2

Error Variance

0

500

1000

1500

2000

2500

3000

3500

1/01/03 1/16/03 1/31/03 2/15/03 3/02/03 3/17/03 4/01/03 4/16/03 5/01/03

Daily Chart

Forecast

Determined value: 85.02 O

Results -- Optimal ε2Results -- Optimal ε2

Optimum value0.95

30

32

34

36

38

40

42

0.01 0.1 1 10

E2

RM

SE

(%

)

Jan

Feb

March

AVG

Results -- Optimal LResults -- Optimal L

34.3

34.4

34.5

34.6

0 10 20 30 40 50 60

L (km)

Ice

Co

nce

ntr

atio

n R

MS

E

Optimum Value~30 km

Optimizing Parameters for SSMI-NASA Team2 Total IceOptimizing Parameters for

SSMI-NASA Team2 Total Ice

• Background: model 24 hour forecast based on previous analysis

• Observations: total ice concentration analysis only, ice types/thickness partitioning from model

• NSIDC daily gridded data assumed valid for 18 UTC• Model ocean T&S adjusted in areas of analysis increments

• SST approaches Tf for increased ice• SST approaches Tf+0.5 for decreased ice• S adjusted in balance with above• Increments drop off linearly with depth

• Validation: Radarsat image analysis charts and daily ice charts

Preliminary ResultsPreliminary Results

3 Hour Forecast RMSE vs E2

43

44

45

46

47

48

49

50

51

52

53

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9

E2

J anFebMarApr

3 Hour Forecast RMSE vs L

43

44

45

46

47

48

49

50

51

0 10 20 30 40 50 60

L

J an (e2=0.05)Feb (e2=0.05)Mar (e2=0.05)Apr (e2=0.05)J an (e2=0.01)Feb (e2=0.01)Mar (e2=0.01)Apr (e2=0.01)

Ocean (over-) AdjustmentOcean (over-) Adjustment

SSMI with E2=0.05, L=30

-40

-35

-30

-25

-20

-15

-10

-5

0

0 12 24 36 48

Forecast Period (hours)

Ice

Co

nce

ntr

atio

n B

ias

(%)

January

February

March

April

SSMI with E2=0.05, L=30

-30

-25

-20

-15

-10

-5

0

5

0 12 24 36 48

Forecast Period (hours)

Ice

Ed

ge

Bia

s (%

)

January

February

March

April

SSM/I vs Ice ChartsSSM/I vs Ice Charts

0

10

20

30

40

50

60

Jan Feb Mar Apr Avg

RM

SE

SSMI

Charts

-35

-30

-25

-20

-15

-10

-5

0

5

10

Jan Feb Mar Apr Avg

Bia

s SSMI

Charts

Sea Ice DA Planning WorkshopMontreal 23-24 March 2004

Sea Ice DA Planning WorkshopMontreal 23-24 March 2004

• Goals: • to develop a sea ice data assimilation plan for

Canada• to forge stronger relationships between remote

sensing, modelling and data assimilation experts

• Participants• Ice and ocean modellers, remote sensing experts,

NWP modellers and data assimilation experts, managers and 5 US sea ice experts

Sea Ice DA Planning Workshop Key results

Sea Ice DA Planning Workshop Key results

• Data:• Start with easiest (ice charts) and build• PM observation operator

• Models• Level of complexity required is influenced by end products and data

to be assimilated• Will use existing dynamic-thermodynamic model and build

additional capabilities as required• DA technique

• 3Dvar will be used: • allows assimilation of direct observations• Currently in use for Canadian NWP• Incremental change from current CIS OI developments• Allows for development path to 4Dvar

MSC Coordination MeetingMSC Coordination Meeting

• Goals• To develop a coordinated approach to sea

ice research but more specifically sea ice modelling and data assimilation

• Participants• MSC research (models, DA, climate)• CMC (NWP)• CIS Applied Research

MSC Coordination MeetingKey Results

MSC Coordination MeetingKey Results

• Coupled atmosphere-ice-ocean modelling• CIS is viewed as an important client and

participant• CIS participation in development team• CIS membership on CPOP• CIS membership on ACSD management team

• Sea ice data assimilation• Led by MSC data assimilation group• Focus on sea ice for CIS operations• CIS participation

Future workFuture work

• Migrate to 3Dvar for ice charts, PM and SST

• Development of PM observation operator

• Migrate ice model operations to CMC

• Development of single ice model

• Verifications and simulations of coupled A-I-O system