Automated algorithm for remote sensing of atmospheric aerosols and trace gases using MFRSR measurements

Abstract

A substantial upgrade of our previously developed MFRSR data analysis algorithm is presented. The new version of the algorithm features an automated cloud screening procedure based on optical thickness variability analysis. This technique is objective, computationally efficient and is able to detect short clear-sky intervals under broken cloud conditions. The performance of the method has been compared with that of AERONET cloud screening algorithm. Another new feature is the adoption of a bimodal gamma distribution as the aerosol particle size model. The size of the fine mode particles and a ratio between optical thicknesses of the two modes are retrievable. The algorithm has been tested on a multi-year dataset from the MFRSR network at the DOE Atmospheric Radiation Measurement (ARM) Program site in Southern Great Plains (SGP). The aerosol optical thicknesses (total, fine, and coarse) obtained from our analysis were successfully compared with the corresponding AERONET almucantar retrievals from a CIMEL sunphotometer colocated with the MFRSR at the SGP Central Facility. Geographical and seasonal variability of aerosol properties has been observed in the multi-instrument dataset from all SGP Extended Facilities for the year 2000. The geographical trends in the fine mode particle size appear to reflect differences in the PM2.5 to PM10 ratios obtained from EPA monitoring data. Long-term temporal variability has been studied on the 1992-1997 dataset from the SGP Central Facility. A significant trend has been detected in coarse mode aerosol optical thickness following the Mt. Pinatubo eruption in 1991, while the fine mode optical thickness exhibits only seasonal variations during that period.