Data Mining Support for Aerosol Optical Depth Retrieval and Analysis

Aerosol Optical Depth (AOD) indicates the amount of depletion that a beam of radiation undergoes as it passes through the atmosphere. One of the biggest challenges of current climate research is to characterize and quantify the effect of AOD on the Earth''s radiation budget. We will describe a novel data mining method for improving AOD prediction or so called retrieval accuracy based on training neural networks that take advantage of high resolution satellite observations and collocated high quality ground based measurements. The experimental results obtained using thousands of observations over the entire globe suggest that ensembles of neural networks are more accurate than the operational MODIS AOD retrieval algorithm. Our study of differences between neural networks and the MODIS algorithm over the continental United States also revealed information that can help improve quality of the MODIS algorithm.