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Objective Determination of Feature Resolution in Two Sea Surface Temperature Analyses Public Deposited

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  • Considerable effort is presently being devoted to producing high-resolution sea surface temperature (SST) analyses with a goal of spatial grid resolutions as low as 1 km. Because grid resolution is not the same as feature resolution, a method is needed to objectively determine the resolution capability and accuracy of SST analysis products. Ocean model SST fields are used in this study as simulated "true'' SST data and subsampled based on actual infrared and microwave satellite data coverage. The subsampled data are used to simulate sampling errors due to missing data. Two different SST analyses are considered and run using both the full and the subsampled model SST fields, with and without additional noise. The results are compared as a function of spatial scales of variability using wavenumber auto-and cross-spectral analysis. The spectral variance at high wavenumbers (smallest wavelengths) is shown to be attenuated relative to the true SST because of smoothing that is inherent to both analysis procedures. Comparisons of the two analyses (both having grid sizes of roughly 1/20 degrees) show important differences. One analysis tends to reproduce small-scale features more accurately when the high-resolution data coverage is good but produces more spurious small-scale noise when the high-resolution data coverage is poor. Analysis procedures can thus generate small-scale features with and without data, but the small-scale features in an SST analysis may be just noise when high-resolution data are sparse. Users must therefore be skeptical of high-resolution SST products, especially in regions where high-resolution ( similar to 5 km) infrared satellite data are limited because of cloud cover.
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  • Reynolds, R. W., Chelton, D. B., Roberts-Jones, J., Martin, M. J., Menemenlis, D., & Merchant, C. J. (2013). Objective determination of feature resolution in two sea surface temperature analyses. Journal of Climate, 26(8), 2514-2533. doi:10.1175/JCLI-D-12-00787.1
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  • This work was funded in part by NOAA’s Climate Data Record Program, managed by the National Climatic Data Center. We are grateful to NCDC and the NOAA/Climate Program Office, which provided partial support for this work. Both DCB and RWR were partially supported by NASA Grant NS214A funded through Oregon State University. The research of JRJ and MJM leading to these results has received funding from the European Community’s Seventh Framework Programme FP7/2007-2013 under Grant Agreement 283367 (MyOcean 2). DM performed this work at the Jet Propulsion Laboratory, California Institute of Technology, under contract with NASA.
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  • Vol. 26 no. 8
  • description.provenance : Submitted by Deborah Campbell (deborah.campbell@oregonstate.edu) on 2013-06-04T18:03:24Z No. of bitstreams: 1 CheltonDudleyBCEOASObjectiveDeterminationFeature.pdf: 8807038 bytes, checksum: 30b035d66a7b5a29310ea6cc999a5439 (MD5)
  • description.provenance : Made available in DSpace on 2013-06-04T18:03:24Z (GMT). No. of bitstreams: 1 CheltonDudleyBCEOASObjectiveDeterminationFeature.pdf: 8807038 bytes, checksum: 30b035d66a7b5a29310ea6cc999a5439 (MD5) Previous issue date: 2013-04
  • Journal of Climate

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