Mitigation of Striping Noise in ATMS Calibration Counts by Symmetric Filters
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Mitigation of Striping Noise in ATMS Calibration Counts by Symmetric Filters

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  • Journal Title:
    Journal of Atmospheric and Oceanic Technology
  • Personal Author:
  • NOAA Program & Office:
    NESDIS (National Environmental Satellite, Data, and Information Service)
  • Description:
    Global observations from the Advanced Technology Microwave Sounder (ATMS) onboard the Suomi National Polar-Orbiting Partnership satellite are affected by striping-patterned noise. An optimal symmetric filter method to mitigate the striping noise in warm counts, cold counts, warm load temperatures, and scene counts instead of antenna temperatures is developed and tested in this study. The optimal filters are developed based on the results free of striping noise obtained with a striping noise detecting method by combining the principal component analysis and the ensemble empirical mode decomposition. The two-point algorithm is then used to calculate antenna temperatures with warm counts, cold counts, warm load temperatures, and scene counts before and after applying the optimal filters. The necessity of applying the striping noise mitigation to the scene counts besides the calibration counts (warm and cold counts) is also shown. This explains why the traditional method to smooth only calibration counts has failed to remove the ATMS striping noise. The optimal filters proposed in this study, which remove the high-frequency striping noise without altering low-frequency weather signals, outperform the conventional boxcar filters adopted in the current operational ATMS calibration system.
  • Keywords:
  • Source:
    Journal of Atmospheric and Oceanic Technology, 36(7), 1297-1312
  • DOI:
  • ISSN:
    0739-0572;1520-0426;
  • Format:
    PDF
  • Publisher:
    American Meteorological Society
  • Document Type:
    Journal Article
  • Funding:
    Grant no. NA14NES4320003
  • Rights Information:
    Other
  • Compliance:
    Library
  • Main Document Checksum:
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