Sensor-Specific Error Statistics for SST in the Advanced Clear-Sky Processor for Oceans

Details

• Journal Title:
  Journal of Atmospheric and Oceanic Technology
• Personal Author:
  Petrenko, B. ; Ignatov, A. ; Kihai, Y. ; Dash, P.
• NOAA Program & Office:
  NESDIS (National Environmental Satellite, Data, and Information Service) ; STAR (Center for Satellite Applications and Research) ; JPSS (Joint Polar Satellite System Program Office) ; CIRA (Cooperative Institute for Research in the Atmosphere)
• Description:
  The formulation of the sensor-specific error statistics (SSES) has been redesigned in the latest implementation of the NOAA Advanced Clear-Sky Processor for Oceans (ACSPO) to enable efficient use of SSES for assimilation of the ACSPO baseline regression SST (BSST) into level 4 (L4) analyses. The SSES algorithm employs segmentation of the SST domain in the space of regressors and derives the segmentation parameter from the statistics of regressors within the global dataset of matchups.
• Keywords:
  Algorithms Observational Techniques And Algorithms Remote Sensing
• Source:
  Journal of Atmospheric and Oceanic Technology, 33(2), 345-359
• DOI:
  https://doi.org/10.1175/Jtech-D-15-0166.1
• Document Type:
  Journal Article
• Rights Information:
  Other
• Compliance:
  Submitted
• Main Document Checksum:
  urn:sha256:537fccdd44f018551307c326601a508e65db16d84ce2ccc444c747efc96e7aac
• Download URL:
  https://repository.library.noaa.gov/view/noaa/41035/noaa_41035_DS1.pdf
• File Type:
  [PDF - 3.87 MB ]