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Pavolonis, MJ, Heidinger, AK (2004). Daytime cloud overlap detection from AVHRR and VIIRS. JOURNAL OF APPLIED METEOROLOGY, 43(5), 762-778.

Abstract
Two algorithms for detecting multilayered cloud systems with satellite data are presented. The first algorithm utilizes data in the 0.65-, 11-, and 12-mum regions of the spectrum that are available on the Advanced Very High Resolution Radiometer (AVHRR). The second algorithm incorporates two different techniques to detect cloud overlap: the same technique used in the first algorithm and an additional series of spectral tests that now include data from the 1.38- and 1.65-mum near-infrared regions that are available on the Moderate Resolution Imaging Spectroradiometer ( MODIS) and will be available on the Visible/Infrared Imager/Radiometer Suite (VIIRS). VIIRS is the imager that will replace the AVHRR on the next generation of polar-orbiting satellites. Both algorithms were derived assuming that a scene with cloud overlap consists of a semitransparent ice cloud that overlaps a cloud composed of liquid water droplets. Each algorithm was tested on three different MODIS scenes. In all three cases, the second ( VIIRS) algorithm was able to detect more cloud overlap than the first ( AVHRR) algorithm. Radiative transfer calculations indicate that the VIIRS algorithm will be more effective than the AVHRR algorithm when the visible optical depth of the ice cloud is greater than 3. Both algorithms will work best when the visible optical depth of the water cloud is greater than 5. Model sensitivity studies were also performed to assess the sensitivity of each algorithm to various parameters. It was found that the AVHRR algorithm is most sensitive to cloud particle size and the VIIRS near-infrared test is most sensitive to cloud vertical location. When validating each algorithm using cloud radar data, the VIIRS algorithm was shown to be more effective at detecting cloud overlap than the AVHRR algorithm; however, the VIIRS algorithm was slightly more prone to false cloud overlap detection.

DOI:

ISSN:
0894-8763

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