Journal of the Korean earth science society (한국지구과학회지)

The Korean Earth Science Society (한국지구과학회)
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Wind Vector Retrieval from SIR-C SAR Data off the East Coast of Korea

  • Kim, Tai-Sung (Department of Science Education, Seoul National University) ;
  • Park, Kyung-Ae (Department of Earth Science Education/Research Institute of Oceanography, Seoul National University) ;
  • Moon, Woo-Il (Research Institute of Oceanography/Department of Geophysics, University of Manitoba)
  • Received : 2010.07.29
  • Accepted : 2010.09.14
  • Published : 2010.09.30
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Abstract

Sea surface wind field was retrieved from high-resolution SIR-C SAR data by using CMOD algorithms off the east coast of Korea. In order to extract wind direction information from SAR data, a two-dimensional spectral analysis method was applied to the normalized radar cross section of the image. An $180^{\circ}$-ambiguity problem in the determination of wind direction was solved by selecting a direction nearest to the wind vector of the ECMWF reanalysis data. Comparison of the wind retrieval patterns with the ECMWF and NCEP/NCAR dataset showed RMS errors in the range of 1.30 to $1.72\;ms^{-1}$. In contrast, comparison of wind directions revealed large errors of greater than $60^{\circ}$, which is enormously higher than the permitted limit of about $20^{\circ}$ for satellite scatterometer winds. Compared with wind speed results from different algorithms, wind vectors based on commonly-used CMOD4 algorithm showed good agreement with those derived by other algorithms such as CMOD_IFR2 and CMOD5, particularly at medium winds from 4 to $8\;ms^{-1}$. However, apparent discrepancy appeared at low winds (< $4\;ms^{-1}$). This study also addressed an importance of accurate wind direction data to improve the accuracy of wind speed retrieval and discussed potential causes of wind retrieval errors from SAR data.

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References

  1. Alpers, W., 1995, Measurement of mesoscale oceanic and atmospheric phenomena by ERS-1 SAR. Radio Science Bulletin, 275, 14-22.
  2. Ebuchi, N., 2000, Evaluation of NSCAT-2 wind vectors by using statistical distributions of wind speeds and directions. Journal of Oceanography, 56, 161-172. https://doi.org/10.1023/A:1011183029009
  3. Freilich, M.H. and Dunbar, R.S., 1999, The accuracy of the NSCAT-1 vector winds: Comparisons with NDBC buoys. Journal of Geophysical Research, 104, 11231-11246. https://doi.org/10.1029/1998JC900091
  4. Gerling, T.W., 1972, Structure of the surface wind field from Seasat SAR. Journal of Geophysical Research, 91, 2308-2320.
  5. Hersbach, H., 2003, CMOD5: An improved geophysical model function for ERS C-band scatterometry. ECMWF Technical Memorandum, 50 p.
  6. Hersbach, H., Stoffelen, A., and de Haan, S., 2007, An improved C-band scatterometer ocean geophysical model function: CMOD5. Journal of Geophysical Research, 112, C03006, doi:10.1029/2006JC003743.
  7. Hsu, S.A., 2005, Determining latent heat flux at sea: A comparison between wind-wave interaction and profile methods. Boundary-Layer Meteorology, 25, 417-421.
  8. IFREMER-CERSAT, 1999, WNF products user manual C2-MUT-W-01-IF. IFREMER-CERSAT, 86 p.
  9. Kang, M.K., Park, Y.W., Lee, M.J., and Lee, H.Y., 2007, Study on the extraction of ocean wind, wave and current using SAR. Journal of Korean Navigation and Port Research, 31, 35-42. https://doi.org/10.5394/KINPR.2007.31.1.035
  10. Kim, D.J. and Moon, W.I., 2002, Estimation of sea surface wind vector using RADARSAT data. Remote Sensing of Environment, 80, 55-64. https://doi.org/10.1016/S0034-4257(01)00267-X
  11. Leibovich, S., 1983, The form and dynamics of Langmuir circulations. Journal of Fluid Mechanics, 15, 391-427. https://doi.org/10.1146/annurev.fl.15.010183.002135
  12. Park, K.A. and Cornillon, P., 2002, Stability-induced modification of sea surface winds over Gulf Stream Rings. Geophysical Research Letters, 29, 2211-2214. https://doi.org/10.1029/2001GL014236
  13. Park, K.A., Cornillon, P., and Codiga, D.L., 2006, Modification of surface winds near ocean fronts: Effects of Gulf Stream rings on scatterometer (QuikSCAT, NSCAT) wind observations. Journal of Geophysical Research, 111, C03021. https://doi.org/10.1029/2005JC003016
  14. Robinson, I.A., 2004, Measuring the oceans from space: The principles and methods of satellite oceanography. Praxis, Chichester, UK, 669 p.
  15. Shuchman, R., Johannessen, J., and Rufenach, C., 1994, Determination of wind speed, wind direction and atmospheric structure using ERS-1 SAR data during NORCSEX’ 91. Geoscience and Remote Sensing Symposium IGASS’94, 3, 1259-1261.
  16. Stoffelen, A. and Anderson, D., 1997, Scatterometer data interpretation: Estimation and validation of the transfer function CMOD4. Journal of Geophysical Research, 102, 5767-5780. https://doi.org/10.1029/96JC02860
  17. Thompson, D.R. and Beal, R.C., 2000, Mapping high-resolution wind fields using synthetic aperture radar. Johns Hopkins APL Technical Digest, 21, 58-67.
  18. Ufermann, S. and Romeiser, R., 1999, Numerical study on signatures of atmospheric convective cells in radar images of the ocean. Journal of Geophysical Research, 104, 25707-25719. https://doi.org/10.1029/1999JC900224
  19. Won, J.S., Jeong, H.S., and Kim, T.R., 1998, Wind field estimation using ERS-1 SAR data: The initial report. International Symposium on Remote Sensing, 14, 286-291.
  20. Yoon, H.J., Park, K.S., and Kim, S.I., 2006, Ocean wind retrieval from RADAR SAR images in Korean seas. Journal of the Korean Institute of Maritime Information and Communication Sciences, 10, 706-711.

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