Journal of Meteorological Research

, Volume 28, Issue 2, pp 185–198 | Cite as

Discrimination and validation of clouds and dust aerosol layers over the Sahara desert with combined CALIOP and IIR measurements

  • Jingjing Liu (刘晶晶)
  • Bin Chen (陈 斌)
  • Jianping Huang (黄建平)
Articles

Abstract

This study validates a method for discriminating between daytime clouds and dust aerosol layers over the Sahara Desert that uses a combination of active CALIOP (Cloud-Aerosol Lidar with Orthogonal Polarization) and passive IIR (Infrared Imaging Radiometer) measurements; hereafter, the CLIM method. The CLIM method reduces misclassification of dense dust aerosol layers in the Sahara region relative to other techniques. When evaluated against a suite of simultaneous measurements from CALIPSO (Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations), CloudSat, and the MODIS (Moderate-resolution Imaging Spectroradiometer), the misclassification rate for dust using the CLIM technique is 1.16% during boreal spring 2007. This rate is lower than the misclassification rates for dust using the cloud aerosol discriminations performed for version 2 (V2-CAD; 16.39%) or version 3 (V3-CAD; 2.01%) of the CALIPSO data processing algorithm. The total identification errors for data from in spring 2007 are 13.46% for V2-CAD, 3.39% for V3-CAD, and 1.99% for CLIM. These results indicate that CLIM and V3-CAD are both significantly better than V2-CAD for discriminating between clouds and dust aerosol layers. Misclassifications by CLIM in this region are mainly limited to mixed cloud-dust aerosol layers. V3-CAD sometimes misidentifies low-level aerosol layers adjacent to the surface as thin clouds, and sometimes fails to detect thin clouds entirely. The CLIM method is both simple and fast, and may be useful as a reference for testing or validating other discrimination techniques and methods.

Key words

CALIPSO CLIM dust detection 

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Copyright information

© The Chinese Meteorological Society and Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Jingjing Liu (刘晶晶)
    • 1
  • Bin Chen (陈 斌)
    • 1
  • Jianping Huang (黄建平)
    • 1
  1. 1.Key Laboratory for Semi-Arid Climate Change of the Ministry of Education, College of Atmospheric SciencesLanzhou UniversityLanzhouChina

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