A Pixel-Scale Algorithm of Cloud Type, Layer, and Amount for AVHRR Data. Part I: Nighttime

View More View Less
  • 1 UCAR Visiting Scientist Program, NOAA Science Center, Washington, D.C.
  • | 2 NOAA/NESDIS, Satellite Research Laboratory, Washington, D.C.
  • | 3 Washington, D.C.
© Get Permissions
Full access

Abstract

An automated pixel-scale algorithm has been developed to retrieve cloud type, related cloud layer(s), and the fractional coverages for all cloud layers in each AVHRR (Advanced Very High Resolution Radiometer) pixel at night. In the algorithm, cloud-contaminated pixels are separated from cloud-free pixels and grouped into three generic cloud types. Cloud layers in each cloud type are obtained through a cloud-type uniformity check, a thermal uniformity check, and a channel 4 ( 11 μm) brightness temperature histogram analysis, within a grid area. The algorithm allows for pixels to be mixed among different cloud layers of different cloud types, as well as between cloud layers and the ocean or land surface. A “neighbor-cheek” method is developed to identify the cloud layers associated with each mixed pixel and to calculate the coverages of each of the cloud layers in the pixel. Digital color images are generated based on information on the location, cloud type, cloud layer, and cloud amount of each individual pixel. Visualization comparisons show good agreement between color-coded images and the standard black and white satellite images. The results of the pixel-scale algorithm also show good agreements with the spatial coherence analysis and with National Weather Service surface and radiosonde observations. The pixel-scale algorithm has been developed for use in validation of output from CLAYR (clouds from AVHRR) project algorithms.

Abstract

An automated pixel-scale algorithm has been developed to retrieve cloud type, related cloud layer(s), and the fractional coverages for all cloud layers in each AVHRR (Advanced Very High Resolution Radiometer) pixel at night. In the algorithm, cloud-contaminated pixels are separated from cloud-free pixels and grouped into three generic cloud types. Cloud layers in each cloud type are obtained through a cloud-type uniformity check, a thermal uniformity check, and a channel 4 ( 11 μm) brightness temperature histogram analysis, within a grid area. The algorithm allows for pixels to be mixed among different cloud layers of different cloud types, as well as between cloud layers and the ocean or land surface. A “neighbor-cheek” method is developed to identify the cloud layers associated with each mixed pixel and to calculate the coverages of each of the cloud layers in the pixel. Digital color images are generated based on information on the location, cloud type, cloud layer, and cloud amount of each individual pixel. Visualization comparisons show good agreement between color-coded images and the standard black and white satellite images. The results of the pixel-scale algorithm also show good agreements with the spatial coherence analysis and with National Weather Service surface and radiosonde observations. The pixel-scale algorithm has been developed for use in validation of output from CLAYR (clouds from AVHRR) project algorithms.

Save