Retrieval of cloud top properties from advanced geostationary satellite imager measurements based on machine learning algorithms

Details

• Journal Title:
  Remote Sensing of Environment
• Personal Author:
  Min, Min ; Li, Jun ; Wang, Fu ; Liu, Zijing ; Menzel, W. Paul
• NOAA Program & Office:
  CIMSS (Cooperative Institute for Meteorological Satellite Studies)
• Description:
  The cloud-top height (CTH) product derived from passive satellite instrument measurements is often used to make climate data records (CDR). CALIPSO (Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations) provides CTH parameters with high accuracy, but with limited temporal-spatial resolution. Recently, the Advanced Himawari Imager (AHI) onboard Japanese Himawari-8/-9, provides high temporal (every 10 min) and high spatial (2 km at nadir) resolution measurements with 16 spectral bands. This paper reports on a study to derive the CTH from combined AHI and CALIPSO using advanced machine learning (ML) algorithms with better accuracy than that from the traditional physical (TRA) algorithms. We find significant CTH improvements (1.54–2.72 km for mean absolute error, MAE) from four different machine learning algorithms (original MAE from TRA method is about 3.24 km based on CALIPSO data validation), particularly in high and optically thin clouds. In addition, we also develop a joint algorithm to combine optimal machine learning and traditional physical (TRA) algorithms of CTH to further reduce MAE to 1.53 km and enhance the layered accuracy (CTH < 18 km). While the ML-based algorithm improves CTH retrieval over the TRA algorithm, the lower or higher clouds still exhibit relatively large uncertainty. Combining both methods provides the better CTH than either alone. The combined approach could be used to process data from advanced geostationary imagers for climate and weather applications.
• Source:
  Remote Sensing of Environment, 239, 111616
• DOI:
  https://doi.org/10.1016/j.rse.2019.111616
• ISSN:
  0034-4257
• Format:
  pdf
• Publisher:
  Elsevier BV
• Document Type:
  Journal Article-Accepted Manuscript
• Rights Information:
  Accepted Manuscript
• Rights Statement:
  The NOAA IR provides access to this content under the authority of the government's retained license to distribute publications and data resulting from federal funding. While users may legally access this content, the copyright owners retain rights that govern the reproduction, redistribution, and re-use of this work. The user is solely responsible for complying with applicable copyright law.
• Compliance:
  Library
• Main Document Checksum:
  urn:sha-512:1d217faa5ecd1198b145af533cb3140466f8231ec0ca46f3706d3f5c06aadb12f108d5646a7f32deb538afff37388be2558b8eca5a1b2e51aba65e046ab42f61
• Download URL:
  https://repository.library.noaa.gov/view/noaa/68428/noaa_68428_DS1.pdf
• File Type:
  [PDF - 4.05 MB ]