Characterisation of night-time aerosols using starphotometry

• Main Author: Baibakov, Konstantin
• Other Authors: O'Neill, Norman Thomas
• Language: English
• Published: Université de Sherbrooke 2009
• Subjects: Lidar; Sunphotometry; Egbert; Aerosol optical depth; Starphotometry
• Online Access: http://savoirs.usherbrooke.ca/handle/11143/2613

This is a study concerning the use of starphotometry to retrieve night-time aerosol optical depths (AODs). In the summer of 2007 a SPSTAR03 starphotometer was installed at a rural site at Egbert, Ontario for the purpose of the nighttime AOD measurements. Two series of daytime / nighttime AODs were acquired using the CIMEL CE 318 sunphotometer and the SPSTAR03 from Aug. 31 to Sept. 19 2007 and from June 30 to July 5, 2008. The measurements were complemented by vertical backscatter coefficient profiles acquired using a pulsed lidar. We found that starphotometer AOD estimates, based on the application of a two star method (TSM) to low and high elevation stars, are susceptible to atmospheric inhomogeneity effects. Starphotometer AOD estimates based on the one star method (OSM) reduce this sensitivity, but require absolute calibration values. A level of continuity was obtained between the daytime sunphotometry and nighttime starphotometry data. A continuity parameter (defined as the average difference between the measured nighttime and interpolated daytime values) was calculated over four distinct periods. It yielded the differences of 0.160, 0.053, 0.139 (total, fine and coarse mode optical depths) for the low star and 0.195, 0.070, 0.149 for the high star. We argue that cloud screening would have reduced the continuity parameter differences for the coarse and total optical depths. For 5 out of , 8 nights of lidar operation, a combination of the Angstrom and Spectral Deconvolution Algorithm (SDA) analysis provided an indication of the nature of the atmospheric features seen in the lidar data. Fine and coarse-mode events were detected during the measurement periods using the SDA. Lidar data was used to better understand complex atmospheric phenomena and was found especially effective for cloud detection and general signal increase/decrease analysis.