National Ice Center Science and Applied Technology Program
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Transcript of National Ice Center Science and Applied Technology Program
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National Ice CenterNational Ice CenterScience and Applied Technology Science and Applied Technology
ProgramProgram
Dr. Michael Van Woert, Chief Scientist
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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.
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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.
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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
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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.
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Forecast Skill Scores #2Forecast Skill Scores #2
Accuracy defined as:
i
biaiNbaMSE )(2
1),(
),(),(
),(),(
ORMSEOPMSE
ORMSEOfMSESS
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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)
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PIPS 24-Hour Forecast PIPS 24-Hour Forecast ValidationValidation
PIPS much better than climo
But with respect to persistence?
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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
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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
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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
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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.
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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
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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!
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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%
~
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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
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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)
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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
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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
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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
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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
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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
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Riverdance ends its Arctic run … Riverdance ends its Arctic run … minus the usual encore.minus the usual encore.