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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.