1. SSM/I Sea Ice Concentrations in the Marginal Ice Zone A Comparison of Four Algorithms with AVHRR Imagery submitted to IEEE Trans. Geosci. and Rem. Sensing 4 June 2004 Walt Meier NSIDC/CIRES Research Scientist

2. Motivation Most previous algorithm comparisons have involved isolated case studies (a few days) Comparisons have involved one or two algorithms Comparisons often encompass primarily regions of compact ice where errors are expected to be smallest

3. This Study Large scale independent comparison of SSM/I ice concentration algorithms –Four algorithms –Several days –Winter and summer –Three regions Focus on marginal/seasonal ice zone –Region of operational interest –High small-scale variability both in space and time –Region of large seasonal and interannual variability –Algorithms have most difficulty in such regions –Models of air-sea exchange most sensitive in such areas

4. Barents E. Greenland Baffin Map of Study Regions

5. AVHRR Imagery Images with considerable cloud free areas collected over one year period –June 2001 – August 2001 –November 2001 – March 2002 Images collected from Eastern Arctic –Barents Sea –Baffin Bay –East Greenland sea 2.5 km resolution on NSIDC polar stereographic grid >750 total scenes collected; 48 used in study

6. AVHRR Concentration: Summer Mixing Method June 2001 – August 2001 Assume Channel 2 (0.72-1.10  m) albedo reflects amount of ice present in a pixel Tiepoints defined for 100% ice and 100% water Ice concentration derived from linear interpolation between tiepoints Tiepoints determined locally in each image –Account for changes in sun and satellite angle and local ice changes Similar methodology used in several past comparisons, e.g.: Comiso and Steffen, 2001,Zibordi et al., 1995, Emery et al., 1991, Steffen and Schweiger, 1990

7. AVHRR Concentration: Winter Threshold Method December 2001 – March 2002 Assume surface temperature is below freezing, thus ice is continually forming Channel 4 (10.3-11.3  m) brightness temperature indicates if ice is present in pixel or not Ice/water threshold temperature (~271 K) defined –If Tb > threshold, Concentration = 100% –If Tb < threshold, Concentration =0% Threshold chosen locally within each individual AVHRR image Similar methodology used in several past comparisons, e.g.: Comiso and Steffen, 2001, Zibordi et al., 1995, Emery et al., 1991, Steffen and Schweiger, 1990

8. SSM/I Concentration Fields 25 km fields on NSIDC polar stereographic grid –Algorithms run on 24-hour composite brightness temperature fields acquired from NOAA at the National Ice Center Subsampled to same region as AVHRR images Rebinned (no interpretation) to same 2.5 km resolution as AVHRR for pixel-to-pixel comparison Weather filters used to eliminate false ice signals over open water (same filters used for all algorithms)

9. SSM/I Algorithms Bootstrap (BT): 19V, 19H, 37V –e.g., Comiso et al., 1997 Cal/Val (CV): 19V, 37V (37V, H near ice edge) –e.g., Ramseier et al., 1988 NASA Team (NT): 19V, 19H, 37V –e.g., Cavalieri et al., 1984 NASA Team 2 (N2): 19V, 19H, 37V, 85V, 85H –Markus and Cavalieri, 2000

10. NASA Team 2 Newest algorithm Uses 85 GHz channels in addition to standard 19 and 37 GHz channels –85 GHz susceptible to atmosphere –N2 uses inverse radiative transfer model to find ‘best- fit’ of 11 standard atmospheres –Atmosphere subtracted out from T b signal –85 GHz more sensitive to surface inhomogeneities  potentially more accurate if no atmospheric problems Standard algorithm for AMSR-E in the Arctic

11. SSM/I – AVHRR Difference TotalBTCVN2NT 13897 pixels Mean-5.31.8 -1.2 -9.0 St. Dev. 12.9 13.913.714.6 SummerBTCVN2NT 4125 pixels Mean-6.1-4.3 -2.6 -10.5 St. Dev. 14.6 16.915.715.9 WinterBTCVN2NT 9772 pixels Mean-5.00.7 -0.6 -8.4 St. Dev. 12.2 12.312.713.9 Values in yellow are the lowest difference or are within 95% confidence level of lowest difference.

12. SSM/I-AVHRR Mean Differences Differences for each case (numbered on x-axis) for each season. Error bars indicate 95% confidence levels. Summer Winter % Difference

13. SSM/I-AVHRR St. Dev. Differences Differences for each case (numbered on x-axis) for each season. Error bars indicate 95% confidence levels. Summer Winter % Difference

14. Case Study Barents Sea 17 June 2001

15. 020406080100 %

16. BT 72% NT 68% CV 81% N2 74% AV 79% 020406080100 %

17. 020406080100 %

18. AVBTCVN2NT 99.6%99.2%100.0%97.1%89.8% 020406080100 %

19. Case Study E. Greenland Sea 27 February 2002

20. 020406080100 %

21. AVBTCVN2NT 96%86%93%94%83% 020406080100 %

22. Clouds Previous comparison limited to clear sky regions Clouds prevalent –Over 8 months of images in the three regions (~750 total) –<60 had enough clear sky to make comparisons Algorithms likely do not perform as well under thick clouds, particularly N2 To investigate potential effects of clouds, a regional case study was conducted –Meier, W.N., T. Maksym, and M.L. Van Woert, Evaluation of Arctic operational passive microwave products: A case study in the Barents Sea during October 2001, Proc. 16 th Int’l Symposium on Ice, Dunedin, NZ, 2-6 Dec 2002, pp. 213-222. –Barents Sea, October 2001 – USCGC Healy cruise –SSM/I concentrations compared with Radarsat imagery – N2 did not show any noticeable degradation

23. 1 Oct. BSCV N2NT N SSM/I Contour Intervals 5% 15% 50% 90% © CSA 2001 OLS Underestimates ice edge

24. 11 Oct BSCV N2NT SSM/I Contour Intervals 5% 15% 50% 90% © CSA 2001 OLS Misses ice Captures lower concentration

25. Conclusions Performance of algorithms varies depending on season, ice conditions, etc. –Overall, NASA Team 2 and Bootstrap have lowest differences from AVHRR N2 tends to have lowest bias Bootstrap tends to have lowest difference SD –Cal/Val tends to overestimate concentration due to saturation to 100% concentration, especially in summer –NT is inferior algorithm in most situations Algorithms yield similar difference SD values, due at least in part to low resolution of sensor  no matter what algorithm is used, resolution limits the effectiveness of SSM/I

26. Acknowledgements Canadian Space Agency for Radarsat imagery DMSP and NOAA for OLS and SSM/I data Søren Anderson, Danish Meteorology Institute, for AVHRR data Midshipman Nathan Bastar for initial analysis