Global Retrieval of Aerosol Absorption Using Multi-Satellite Retrieval: Algorithm Development And Performance Evaluation

Abstract

Aerosol absorption, quantified by single scattering albedo (SSA), is an important parameter for assessing the climatic impact of aerosols. While there are several satellites providing aerosol optical depth (AOD), the availability of SSA data is very limited. New and better satellite data retrievals are vital in filling this gap, offering a broad view of aerosol properties. A novel CERES-MODIS algorithm was developed for the first time to generate global SSA maps over visible wavelength. It builds on the critical optical depth concept using top-of-atmosphere fluxes from CERES and AOD data from MODIS sensors on Aqua and Terra satellites. Key steps include assessing the influence of surface reflectance and AOD on TOA albedo, applying critical optical depth to determine SSA, and using diurnally averaged fluxes to minimize angular effects. This approach addresses the interplay between surface reflectance, aerosol loading, and aerosol absorption/scattering properties, allowing for global SSA retrieval. Comparison and validation studies were conducted using CERES-MODIS and OMI (Ozone Monitoring Instrument) SSA, along with ground-based and aircraft measurements. The uncertainty in CERES-MODIS SSA retrieval was estimated at ±0.044 with surface albedo contributing to the highest uncertainty. CERES-MODIS showed good agreement with aircraft data over Indian region. Comparison with data from AERONET (Aerosol Robotic Network) ground station showed substantial agreement for dust and mixed type of aerosols. Comparison was also performed between CERES-MODIS and OMI over several regions of interest. Results show that CERES-MODIS offers broader spatial coverage and captures the seasonal variations and absorbing aerosols more effectively, especially over biomass burning and polluted regions. CERES-MODIS SSA shows a clear negative correlation with the MODIS fire count. CERES-MODIS SSA values are in good agreement with the various field campaign measurements over those regions. Whereas, OMI SSA dataset fails to show any correlation with increasing absorbing aerosols due to fire activity. OMI has better temporal resolution but has sparse coverage, especially over oceans, and underrepresents highly absorbing aerosols. Overall, this thesis develops a new algorithm to retrieve SSA globally, thus addressing the current data gaps. In doing so, the thesis presents both the method and a critical evaluation of its performance, thus contributing to the remote sensing of aerosol absorption. These global maps of SSA with improved accuracy provide an important input to climate models for the assessment of aerosol-climate impacts on both regional and global scales.