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Impact of Temporal-Spatio Resolution on Sea-Ice Drift and Deformation

  • Cathleen A. Geiger
  • Mark R. Drinkwater
Part of the Solid Mechanics and Its Applications book series (SMIA, volume 94)

Abstract

Crafting a zero-mean Gaussian noise numerical experiment, we examine the relationship between the temporal-spatio resolution of any given instrument and the impact that resolution window has on the propagation of error associated with the computation of sea-ice position, velocity, and deformation. These results are characterized through a fitted curve model describing agreement between two data sets (a pure signal and a noise corrupted signal) as a function of time sampling and spatial uncertainty. Using two example instruments (SAR and SSM/I), we demonstrate how this method can be applied to a variety of instruments to estimate the precision of motion vector products given an instrument’s chosen temporal and spatial resolution. Relevance to future satellite designs and preprocessing of current data sets is also discussed.

Keywords

Motion Vector Spatial Uncertainty Spatial Noise Eurasian Basin Fitted Curve Model 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. Drinkwater, M.R., 1998a. Active Microwave Remote Sensing Observations of Weddell Sea Ice. In Antarctic Sea Ice: Physical Processes, Interactions and Variability, Anarct. Res. Ser. 74: 187–212. Ed. M.O. Jeffries, AGU, Washington, D.C.CrossRefGoogle Scholar
  2. Drinkwater, M.R., 1998b. Satellite Microwave Radar Observations of Antarctic Sea Ice. In Analysis of SAR Data of the Polar Oceans, 8: 145–187, Eds. C. Tsatsoulis and R. Kwok, Springer-Verlag, Berlin.CrossRefGoogle Scholar
  3. Fily, M. and Rothrock D.A., 1987. Sea ice tracking by nested correlations. IEEE Trans. Geosci. Remote Sens. 25: 570–580.ADSCrossRefGoogle Scholar
  4. Foldvik, A., Middleton, H.H., and Foster, T.D., 1990. The tides of the southern Weddell Sea. Deep Sea Res. 37: 1345–1362.CrossRefGoogle Scholar
  5. Geiger, C.A., Zhao, Y., Liu, A.K., Häkkinen, S.M., 2000. Large-scale comparison between buoy and SSM/I drift and deformation in the Eurasian Basin during winter 1992–1993. J. Geophys. Res. 105: 3357–3368.ADSCrossRefGoogle Scholar
  6. Geiger, C.A., Ackley, S.F., and Hibler III, W.D., 1998. Sea ice drift and deformation processes in the western Weddell Sea. In Antarctic Sea Ice: Physical Processes, Interactions and Variability, Anarct. Res. Ser. 74: 141–160. Ed. M.O. Jeffries, AGU, Washington, D.C.CrossRefGoogle Scholar
  7. Hibler III, W.D., Weeks, W.F., Kovacs, A., and Ackley, S.F., 1974. Differential sea ice drift. Part 1: Spatial and temporal variations in sea ice deformation. J. Glaciol. 13: 437–455.ADSGoogle Scholar
  8. Hines, W.W. and Montgomery, D.C., 1990. Multiple Regression. In Probability and Statistics in Engineering and Management Science. (3rd Edition) 15: 487–558. John Wiley, New York.Google Scholar
  9. Kwok, R., Schweiger, A., Rothrock, D.A., Pang, S., and Kottmeier, C., 1998. Sea ice motion from from satellite passive microwave imagery assessed with ERS SAR and buoy motions. J. Geophys. Res. 103: 8191–8214.ADSCrossRefGoogle Scholar
  10. Li, S., Cheng, Z., and Weeks, W.F., 1998. Extraction of intermediate scale sea ice deformation parameters from SAR ice motion products. In Analysis of SAR Data of the Polar Oceans. pp. 69–90 Eds. C. Tsatsoulis and R. Kwok, Springer-Verlag, New York.CrossRefGoogle Scholar
  11. Lemke, P. (editor), 1994. The expedition ANTARKTIS X/4 of R/V Polarstern in 1992. Reports on Polar Research 140, pps. 99. Alfred Wegener Institut für Polar- und Meeresforschung, D-27568, Bremerhaven, Germany.Google Scholar
  12. Liu, A.K. and Cavalieri, D.J., 1998. On sea ice drift from the wavelet analysis of the Defense Meteorological Satellite Program (DMSP) Special Sensor Microwave Imager (SSM/I) data. Int. J. Remote Sens. 19: 1415–1423.ADSCrossRefGoogle Scholar
  13. Massom, R. A., 1992. Observing the advection of sea ice in the Weddell Sea using buoy and satellite passive microwave data. J. Geophys. Res. 79: 15559–15572.ADSCrossRefGoogle Scholar
  14. Maslanik, J., Agnew, T., Drinkwater, M.R., Emery, W., Fowler, C., Kwok, R., and Liu, A.K., 1998. Summary of ice-motion mapping using passive microwave data. pp. 1–25. Boulder, CO: Report prepared for the Polar Data Advisory Group, NASA Snow and Ice Distributed Active Archive Center, National Snow and Ice Data Center.Google Scholar
  15. Moritz, R.E., Runciman, K.A., and Colony, R., August 1999. Observed variability of monthly sea ice velocity and surface geostrophic winds: 1979–1996. Proceedings from The Arctic Buoy Programme, Seattle, Washington, USA, 3–4 August, 1998. Sponsored by WMO/IOC, International Arctic Buoy Programme, IAPO Informal Report No. 4.Google Scholar
  16. Padman, L., and C. Kottmeier, 2000. High-frequency ice motion and divergence in the Weddell Sea. J. Geophys. Res. 105: 3379–3400.ADSCrossRefGoogle Scholar
  17. Thorndike, A.S., 1986. Kinematics of sea ice. In The Geophysics of Sea Ice. NATO ASI Series, Series B, Vol. 146. 7: 489–549. Ed. N. Untersteiner, Plenum, New York.Google Scholar
  18. Willmott, C.J., Ackleson, S.G., Davis, R.E., Feddema, J.J., Klink, K.M., Legates, D.R., O’Donnell, J., and Rowe, C.M., 1985. Statistics for the evaluation and comparison of models. J. Geophys. Res. 90: 8995–9005.ADSCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2001

Authors and Affiliations

  • Cathleen A. Geiger
    • 1
  • Mark R. Drinkwater
    • 2
  1. 1.Center for Climatic Research, Department of GeographyUniversity of DelawareNewarkUSA
  2. 2.Oceans/Sea-Ice Unit, European Space AgencyESTEC Earth Sciences DivisionNoordwijkThe Netherlands

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