Multi-channel Imager Algorithm (MIA): A novel cloud-top phase classification algorithm

Details

• Journal Title:
  Atmospheric Research
• Personal Author:
  Hu, Jiaxi ; Rosenfeld, Daniel ; Zhu, Yannian ; Lu, Xin ; Carlin, Jacob
• NOAA Program & Office:
  CIMMS (Cooperative Institute for Mesoscale Meteorological Studies) ; NSSL (National Severe Storms Laboratory) ; OAR (Oceanic and Atmospheric Research)
• Description:
  The current Geostationary Operational Environmental Satellites (GOES-16 and 17) cloud-top phase classification algorithm is based primarily on empirical thresholds at multiple wavelengths that have varying absorption capabilities for water and ice. The performance of current GOES-16 cloud-top phase product largely depends on the accuracy of the selection of reflectance ratios. This study aims at presenting a novel cloud-top phase classification algorithm (the Multi-channel Imager Algorithm, MIA) that provides a more judicious selection of relationships between channels using a supervised K-mean clustering method on multi-channel Red-Green-Blue images. The K-mean clustering method works analogously to how human eyes separate different colors in a microphysical color rendering set of satellite images, which differentiates water, ice and unclassified thin clouds. For water phase, cloud-top temperature information is used to further distinguish supercooled water. To evaluate the performance of the MIA, an extensive comparison with Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP), Moderate Resolution Imaging Spectroradiometer, and current GOES-16 cloud-top phase products is conducted, using CALIOP as the benchmark. Compared to the current GOES-16 cloud-top phase product, MIA demonstrates a substantial improvement in phase classification, where hit rate increases from 69% to 76% over the Continental United States and 58% to 66% over the full disk domain.
• Keywords:
  Killer whale Cloud Top Phase Geostationary Satellite Machine Learning
• Source:
  Atmospheric Research, 261
• DOI:
  https://doi.org/10.1016/j.atmosres.2021.105767
• Document Type:
  Journal Article
• Funding:
  Grant no. NA16OAR4320115
• Place as Subject:
  Chukchi Sea
• Rights Information:
  Accepted Manuscript
• Compliance:
  Submitted
• Main Document Checksum:
  urn:sha-512:7b4e73359076f8e1f18654bde0c5c74ff3b8d9c780525e26c29672691d6063672a23a38d584ae6d86ff2e2904f46a94188784be17626db810fbf1b14463f196c
• Download URL:
  https://repository.library.noaa.gov/view/noaa/31428/noaa_31428_DS1.pdf
• File Type:
  [PDF - 1010.81 KB ]