A31D-0070
Recent Improvements to CALIOP Level 3 Aerosol Profile Product for Global 3-D Aerosol Extinction Characterization

Wednesday, 16 December 2015
Poster Hall (Moscone South)
Jason Lucas Tackett1, Brian J Getzewich1, David M Winker2 and Mark A. Vaughan2, (1)Science Systems and Applications, Inc., Lanham, MD, United States, (2)NASA Langley Research Center, Hampton, VA, United States
Abstract:
With nine years of retrievals, the CALIOP level 3 aerosol profile product provides an unprecedented synopsis of aerosol extinction in three dimensions and the potential to quantify changes in aerosol distributions over time. The CALIOP level 3 aerosol profile product, initially released as a beta product in 2011, reports monthly averages of quality-screened aerosol extinction profiles on a uniform latitude/longitude grid for different cloud-cover scenarios, called “sky conditions”. This presentation demonstrates improvements to the second version of the product which will be released in September 2015. The largest improvements are the new sky condition definitions which parse the atmosphere into “cloud-free” views accessible to passive remote sensors, “all-sky” views accessible to active remote sensors and “cloudy-sky” views for opaque and transparent clouds which were previously inaccessible to passive remote sensors. Taken together, the new sky conditions comprehensively summarize CALIOP aerosol extinction profiles for a broad range of scientific queries. In addition to dust-only extinction profiles, the new version will include polluted-dust and smoke-only extinction averages. A new method is adopted for averaging dust-only extinction profiles to reduce high biases which exist in the beta version of the level 3 aerosol profile product. This presentation justifies the new averaging methodology and demonstrates vertical profiles of dust and smoke extinction over Africa during the biomass burning season. Another crucial advancement demonstrated in this presentation is a new approach for computing monthly mean aerosol optical depth which removes low biases reported in the beta version - a scenario unique to lidar datasets.