Mitigation of Striping Noise in ATMS Calibration Counts by Symmetric Filters

Xiaoxu Tian Earth System Science Interdisciplinary Center, University of Maryland, College Park, College Park, Maryland

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Xiaolei Zou Earth System Science Interdisciplinary Center, University of Maryland, College Park, College Park, Maryland

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Abstract

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.

© 2019 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Dr. Xiaolei Zou, xzou1@umd.edu

Abstract

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.

© 2019 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Dr. Xiaolei Zou, xzou1@umd.edu
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