Aaron Naeger

A New Technique Using Infrared Satellite Measurements to Improve the Accuracy of the CALIPSO Cloud-Aerosol Discrimination Method

In this paper, we develop a new technique called the brightness temperature difference cloud and aerosol discrimination algorithm (BTD CAD) that uses thermal infrared satellite measurements to improve the accuracy of the cloud-aerosol lidar and infrared pathfinder satellite observations (CALIPSO) CAD algorithm. It has been shown that the CALIPSO CAD algorithm can misclassify dense dust as cloud because the CALIPSO two-wavelength backscatter lidar operates at 532 and 1064 nm where very similar scattering properties are known to exist between dense dust and cloud. Therefore, we use the 11 and 12 μm thermal infrared channels from both the moderate resolution imaging spectroradiometer (MODIS) and the spinning enhanced visible and infrared imager (SEVIRI), which are very sensitive to dust concentration, in order to reduce the frequency of the dust misclassifications encountered by the CALIPSO CAD algorithm. For the two Saharan dust events presented in this paper, both the MODIS and SEVIRI BTD CAD techniques performed well but the MODIS BTD CAD correctly reclassified more CALIPSO CAD misclassifications as dust. After applying both techniques to all the daytime CALIPSO transects over North Africa during June 2007, the MODIS and SEVIRI BTD CAD increased the total number of detected aerosol layers by approximately 10% and 4%, respectively. Even though the Version 3 (V3) CAD algorithm is significantly more accurate in deciphering between dense dust and clouds than the Version 2 algorithm, the V3 still showed some dust misclassifications among the case studies. Thus, the BTD CAD technique can help reduce the frequency of dust misclassifications encountered by the V3 CAD algorithm.