A Preliminary Assessment of the VIIRS Cloud Top and Base Height Environmental Data Record Reprocessing

Details

• Journal Title:
  Remote Sensing
• Personal Author:
  Liu, Qian ; Hao, Xianjun ; Zou, Cheng-Zhi ; Wang, Likun ; Qu, John J. ; Yan, Banghua
• NOAA Program & Office:
  NESDIS (National Environmental Satellite, Data, and Information Service) ; STAR (Center for Satellite Applications and Research)
• Description:
  The Visible Infrared Imaging Radiometer Suite (VIIRS) onboard the Suomi National Polar-orbiting Partnership (SNPP) satellite has been continuously providing global environmental data records (EDRs) for more than one decade since its launch in 2011. Recently, the VIIRS EDRs of cloud features have been reprocessed using unified and consistent algorithm for selected periods to minimize or remove the inconsistencies due to different versions of retrieval algorithms as well as input VIIRS sensor data records (SDRs) adopted by different periods of operational EDRs. This study conducts the first simultaneous quality and accuracy assessment of reprocessed Cloud Top Height (CTH) and Cloud Base Height (CBH) products against both the operational VIIRS EDRs and corresponding cloud height measurements from the active sensors of NASA’s CloudSat-CALIPSO system. In general, the reprocessed CTH and CBH EDRs show strong similarities and correlations with CloudSat-CALIPSOs, with coefficients of determination (R2) reaching 0.82 and 0.77, respectively. Additionally, the reprocessed VIIRS cloud height products demonstrate significant improvements in retrieving high-altitude clouds and in sensitivity to cloud height dynamics. It outperforms the operational product in capturing very high CTHs exceeding 15 km and exhibits CBH probability patterns more closely aligned with CloudSat-CALIPSO measurements. This preliminary assessment enhances data applicability of remote sensing products for atmospheric and climate research, allowing for more accurate cloud measurements and advancing environmental monitoring efforts.
• Source:
  Remote Sensing, 17(6), 1036
• DOI:
  https://doi.org/10.3390/rs17061036
• ISSN:
  2072-4292
• Format:
  pdf
• Publisher:
  MDPI AG
• Document Type:
  Journal Article
• Funding:
  Grant no. NA24NESX432C0001
• License:
  https://creativecommons.org/licenses/by/4.0/
• Rights Information:
  CC BY
• Compliance:
  Submitted
• Main Document Checksum:
  urn:sha-512:ee0c66f1f781a142565f7d9cb31b5164d2168deb6a6606cd1cdae01bfc884f8978c732c20c90d9f6bebfd90863e8ba40c6ae1d20b9d43e8faa777dfa85aeddad
• Download URL:
  https://repository.library.noaa.gov/view/noaa/71546/noaa_71546_DS1.pdf
• File Type:
  [PDF - 10.24 MB ]