1. National Ice Center Science and Applied Technology Program Dr. Michael Van Woert, Chief Scientist

2. Planned Nowcast Product Evolution: “NIC 5 Year Plan” Planned Nowcast Product Evolution: “NIC 5 Year Plan” REGIONAL NOWCAST PRODUCT CURRENT PRODUCT daily non-global manual, some automation high resolution (<1km) global model / assimilation-based low resolution (10 km) GLOBAL NOWCAST PRODUCT daily weekly global manual Science makes the next step to NOWCAST products possible.

3. Planned Forecast Product Evolution: “NIC 10 Year Plan” Planned Forecast Product Evolution: “NIC 10 Year Plan” PLANNED REGIONAL FORECAST PRODUCT CURRENT Seasonal (30, 90 day) Non-global Statistical Model Climate Indices Global Coupled Dynamical Model Data Assimilation Support PLANNED GLOBAL FORECAST PRODUCT Short-term (24-120 Hours) regional manual heuristic Science makes the next step to FORECAST products possible.

4. PIPS 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 lines drawn every 4 th grid point

5. Forecast Skill Scores #1 A f = accuracy of the forecast system A p = accuracy of a perfect forecast A r = 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.

6. Forecast Skill Scores #2 Accuracy defined as:

7. Forecast Skill Scores #3 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)=0 No forecast skill SS=0; MSE(f,O)=MSE(R,O)

8. PIPS 24-Hour Forecast Validation PIPS much better than climo But with respect to persistence?

9. For 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

10. NIC Forecast Requirements ProductResolutionPrecision TolerancesRange Ice Concen.10 km+/-.5 Tenths0-10/10ths Ice Thickness10 kmFlag Old Ice (2 nd Year and Multiyear +/- 25% Non-Multiyear Ice 0-5 meters Ice Drift (Speed) 10 km( 10cm/sec) +/- 20% 0 – 100 km day -1 Ice Drift (Direction) 10 km+/- 20%360 Deg Ice Edge10 km+/- 10 kmN/A Ice Deformation 10 km+/- 25% of Range+/-5X10 -8 sec -1 Fracture (Lead) Orientation 100 km 2 +/- 45 o 360 deg

11. Polar Ice Prediction System 3.0 Polar 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

12. National 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.

13. MIZ 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

14. The Model Free Drift –3% of the wind speed –23° to the right of the wind Conserve Ice –Single ice thickness category –2 nd 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 for Model of c(t) written as a 2-d matrix, A (t) Dimensions ~70,000 x 70,000 – mostly zeros!

15. Kalman Filter #1 Forecast step: C is the prior estimate of the sea ice concentration field (~7,000 elements) C f is the forecasted sea ice concentration field P is the prior estimate of the covariance (~7,000 x 7,000) P f is the forecasted covariance function A is the matrix of model coefficients and A T 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% ~

16. Kalman Filter #2 K is the Kalman gain E is the observation design matrix (1’s on the diagonal) y is the SSM/I sea ice concentration data vector R is the noise covariance for the SSM/I data (assumed diagonal and 5%) Correction Step:

17. Kalman Filter #3 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  c f (model)

18. Preliminary Results Initial Field December 31, 2001 Forecast January 04, 2002 Observed January 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

19. Not 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

20. WindSat/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, V 22 GHz H 37 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

21. Sea Ice Data Assimilation Sea Ice Data Assimilation Buoys Meier, Unpublished 19-Jan-92 50 cm s -1 Model Motion SSM/I MotionOI 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

22. Satellite-Derived Ice Motion Satellite-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

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