Developing high resolution AOD imaging compatible with weather forecast model outputs for PM2.5 estimation

This project evaluates the potential of the Aerosol Optical Depth (AOD) measurements derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) at a wavelength of 0.55µm from both the Terra and Aqua satellites to estimate ground-level concentrations of fine particulate matter (PM2.5) in the Northeast. Since the PM25 product should be a daily average product to conform to EPA requirements, multiple AOD measurements need to be blended to obtain the best daily mosaic. In order for the mosaic AOD maps to be useful, the output needs to maximize resolution as well as its coverage which are in general contradictory requirements. For example, to account for clouds, the daily products from MODIS DAAC are forced to average the 10km resolution granules over large spatial domains (1 deg x 1 deg) to reduce the effects of cloud blockage. In our research, we attempt to combine the advantages of single granule resolution with 1 deg coverage. In particular, an algorithm was created to take each granule and project each data point at a 0.1 degree resolution using Inverse Distance Weighting (IDW); then, the projected granules are averaged to generate the high resolution 24-hour product. To improve spatial coverage, we adopt an iterative scheme to estimate cloud covered scenes by gradually reducing the resolution for those sectors, creating a hybrid map which maximizes resolution and coverage. After the blending product is achieved, for only grid points missing data (due to cloud coverage), we use Inverse distance Weighting to get a value that best represents the AOD at those points. By using a linear approach, we produce PM2.5 maps based on the AOD data obtained.