Retrieving Asian dust AOT and height from hyperspectral sounder measurements: An artificial neural network approach

摘要

In order to examine potential use of infrared (IR) hyperspectral measurements for dust monitoring, a statistical artificial neural network (ANN) approach was taken as an inverse method of retrieving pixel-level aerosol optical thickness (AOT) and dust height (z_(dust)). The ANN model was trained by relating Atmospheric Infrared Sounder (AIRS) brightness temperatures across 234 channels, surface elevation, and relative air mass to collocated AOT derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) and z_(dust) derived from Cloud Aerosol Lidar Infrared Pathfinder Satellite Observation (CALIPSO) observations for Asian dust cases. Results showing correlation coefficients of 0.84 and 0.79, and mean biases of 0.03 and about -0.02 km for AOT and z_(dust), respectively, suggest that dust retrievals from hyperspectral IR sounder measurements are comparable to MODIS-derived AOT and CALIPSO-measured z_(dust). The pixel-level retrievals of AOT and z_(dust) during both day and night from IR hyperspectral measurements may offer great potential to improve our ability to monitor and forecast the evolving features of Asian dust.