Global dust distribution from improved thin dust layer detection using A-train satellite lidar observations

Abstract

A new dust detection algorithm was developed to take advantage of strong dust signals in the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) 532 nm perpendicular channel to more accurately identify optically thin dust layer boundaries. Layer mean particulate depolarization ratios and improved thin ice cloud detections by combining CALIPSO and CloudSat products were used to further refine the dust mask. Three year global mean results show that the new method detects dust occurrences total detected dust case numbertotal observation number of 0.12 and 0.028 below and above 4 km altitudes, while CALIPSO Level 2 products reported 0.07 and 0.012, respectively. The improvements are mainly in weak source and transporting regions, and the upper troposphere, where optically thin, but significant dust layers from the point of view of aerosol-cloud interactions are dominated. The results can help us to better understand global dust transportation and dust-cloud interactions and improve model simulations.