National Ice CenterNational Ice CenterScience and Applied Technology Science and Applied Technology

ProgramProgram

Dr. Michael Van Woert, Chief Scientist

Planned Nowcast Product Evolution:Planned Nowcast Product Evolution: “NIC 5 Year Plan” “NIC 5 Year Plan”

REGIONAL NOWCAST REGIONAL NOWCAST PRODUCTPRODUCT

CURRENT PRODUCTCURRENT PRODUCT

dailynon-globalmanual, some automation

high resolution (<1km)

globalmodel / assimilation-based

low resolution (10 km)

GLOBAL NOWCAST GLOBAL NOWCAST PRODUCTPRODUCT

daily

weeklyglobalmanual

Science makesScience makesthe next step to the next step to NOWCAST products NOWCAST products possible.possible.

Planned Forecast Product Evolution:Planned Forecast Product Evolution: “NIC 10 Year Plan” “NIC 10 Year Plan”

PLANNED REGIONAL PLANNED REGIONAL FORECAST PRODUCTFORECAST PRODUCT

CURRENT CURRENT FORECAST PRODUCTFORECAST PRODUCT

Seasonal (30, 90 day)Non-globalStatistical Model

Climate Indices

GlobalCoupled Dynamical ModelData Assimilation Support

PLANNED GLOBAL PLANNED GLOBAL FORECAST PRODUCTFORECAST PRODUCT

Short-term (24-120 Hours)

regionalmanualheuristic

Science makesScience makesthe next step to the next step to FORECAST products FORECAST products possible.possible.

PIPS 2.0 Ocean/Ice ModelPIPS 2.0 Ocean/Ice Model Coupled Ice-Ocean Model(Hibler/Cox)

0.28 degree grid resolution(17-34 km)

15 vertical levels

Solid wall boundaries

Ocean loosely constrained to Levitus climatology

Forced by NOGAPS

Initialized with SSM/I

PIPS 2.0 domain. Hatched linesdrawn every 4th grid point

Forecast Skill Scores #1Forecast Skill Scores #1

AA

AASSrp

rf

Af = accuracy of the forecast system

Ap = accuracy of a perfect forecast

Ar = accuracy of a reference forecast

In this formulation SS represents the improvement in accuracy of the forecasts over the reference forecasts relative to the total improvement in accuracy.

Forecast Skill Scores #2Forecast Skill Scores #2

Accuracy defined as:

i

biaiNbaMSE )(2

1),(

),(),(

),(),(

ORMSEOPMSE

ORMSEOfMSESS

Forecast Skill Scores #3Forecast Skill Scores #3

),(

),(1

ORMSE

OfMSESS

SS>0 (skillful) when MSE(R,O) > MSE(f,O). SS<0 (unskillful) when MSE(R,O) <MSE(f,O)

Perfect forecast SS=1; MSE(f,O)=0No forecast skill SS=0; MSE(f,O)=MSE(R,O)

PIPS 24-Hour Forecast PIPS 24-Hour Forecast ValidationValidation

PIPS much better than climo

But with respect to persistence?

For More InfoFor More Info

See also – M. Van Woert et al., “Satellite validation of the May 2000 sea ice concentration fields from the Polar Ice Prediction System”, Canadian Journal of Remote Sensing, 443-456, 2001

NIC Forecast RequirementsNIC Forecast Requirements

Product Resolution Precision Tolerances Range

Ice Concen. 10 km +/- .5 Tenths 0-10/10ths

Ice Thickness 10 km Flag Old Ice (2nd Year and Multiyear +/- 25% Non-Multiyear Ice

0-5 meters

Ice Drift (Speed)

10 km (< 10cm/sec) +/- 5cm (>10cm/sec) +/- 20%

0 – 100 km day-1

Ice Drift (Direction)

10 km +/- 20% 360 Deg

Ice Edge 10 km +/- 10 km N/A

Ice Deformation

10 km +/- 25% of Range +/-5X10-8

sec-1

Fracture (Lead) Orientation

100 km 2 +/- 45o 360 deg

Polar Ice Prediction System 3.0Polar Ice Prediction System 3.0

• Navy ice modeling effort to use Los Alamos C-ICE model for operational sea ice analysis and forecasting

• Plan to couple to Global NCOM Ocean Model

• Provide end-user guidance to Technical Validation Panel

National Weather Service SupportNational Weather Service Support

Sea Ice

ice free

http://science.natice.noaa.gov/work/ice_con_test.grb

Daily weather in the United States is strongly linked to Arctic sea ice conditions.

MIZ ModelMIZ Model

• Marginal Ice Zone Model (Maksym - now at USNA)– Thermodynamics model driven by SSM/I data – Validation data obtained on Healy cruise

Ice core thick section from Healy

With Coon and Toudal

1

1

The ModelThe Model• Free Drift

– 3% of the wind speed– 23° to the right of the wind

• Conserve Ice– Single ice thickness category– 2nd upwind difference scheme– Mass conserving

• NASA TEAM Sea Ice– EASE, equal area grid– 25 km resolution, daily– 435 x 435 elements ~70,000 O & I

• Force with ECMWF wind– 12 hour time step– Interpolated to SSM/I grid: d-2

)()( ECMWFtv F

xcucucc

tttttt

LLRR

)()()()(

)()1(

0)(

cvt

c

),( 12/1 iiR uuu )( 12/1 iiL uuu

iR cc for ,0Ru 1 iR cc for 0Ru

1 iL cc for for ,0LuiL cc 0Lu

Model of c(t) written as a 2-d matrix, A(t)

Dimensions ~70,000 x 70,000 – mostly zeros!

Kalman Filter #1Kalman Filter #1

)()()1(~~ ttt cc f

A

)()()()1( tttt Tf APAP

Forecast step:

C is the prior estimate of the sea ice concentration field (~7,000 elements)Cf is the forecasted sea ice concentration fieldP is the prior estimate of the covariance (~7,000 x 7,000)Pf is the forecasted covariance functionA is the matrix of model coefficients and AT is its transpose (~7,000 x 7,000)~ indicates that the value is an estimate

C(0) is the NASA Team sea ice data for December 31, 2001 [ y(0) ]P(0) is assumed diagonal and equal to 5%

~

Kalman Filter #2Kalman Filter #2

)1()1()1()1(

)1()1()1(

tttt

ttt T

f

Tf

REPEEP

K

)]1()1()1()[1()1()1(~~~ tttyttt ff ccc EK

K is the Kalman gainE is the observation design matrix (1’s on the diagonal)

y is the SSM/I sea ice concentration data vectorR is the noise covariance for the SSM/I data (assumed diagonal and 5%)

Correction Step:

)1()1()1()1()1( ttttt ff PEKPP

Kalman Filter #3Kalman Filter #3

RPP

K

f

f

][ ff ccc y K

• Assume single observation• Assume E=1

For R 0 (perfect obs), K 1 and c y (obs)For R inf (bad obs), K 0 and c cf (model)

Preliminary ResultsPreliminary Results

Initial FieldDecember 31, 2001

ForecastJanuary 04, 2002

ObservedJanuary 04, 2002

White indicates ice concentration >100% (i.e. thickness changes)2 hours per day – 2.7 GHz PC, 512 meg, Windows XP, M/S 4.0

Not Yet CompletedNot Yet Completed

• Careful analysis and selection of P(t=0)• Careful analysis and selection of R(t=0)• Display and analysis of P(t)• Inclusion of controls in the Kalman Filter• Examination of forecast skill• Include an ice thickness equation• Improve satellite-derived sea ice data products• Incorporate data assimilation of sea ice motion

WindSat/Coriolis MissionWindSat/Coriolis Mission

Passive Polarimetric Microwave Radiometer - Frequencies 6.8 GHz V, H 10.0 GHz V, H, U, V

18.7 GHz V, H, U, V22 GHz H37 GHz V, H, U, V

- Launch Jan 2003 - Naval Res. Lab. - Measure Wind Speed & Dir! - What about sea ice??? Work toward improved ice typing with QuikScat/Windsat: K. Partington, N. Walker, S. Nghiem, M. Van Woert

Sea Ice Data AssimilationSea Ice Data Assimilation

Buoys

Meier, Unpublished

19-Jan-92

50 cm s-1

50 cm s-1

Model Motion

SSM/I Motion OI Motion

50 cm s-1

• SSM/I– Many missing vectors– Noisy

• Model– Often wrong

• Objective Interpolation – Constrains model – Interpolates between data

• Kalman Filter– Moving in that direction

Satellite-Derived Ice MotionSatellite-Derived Ice Motion

• Scatterometer data and radiometer data complement each other in estimating ice motion– Where radiometer has

difficulties, scatterometer does well and visa versa

– Enables complete coverage motion maps

Meier, unpublished

Riverdance ends its Arctic run … Riverdance ends its Arctic run … minus the usual encore.minus the usual encore.