The impact of aerosol vertical distribution on aerosol optical depth retrieval using CALIPSO and MODIS data: Case study over dust and smoke regions

• Published in: Journal of Geophysical Research: Atmospheres
• Main Authors: Wu, Y. (author), de Graaf, M. (author), Menenti, M. (author)
• Format: Article in Journal/Newspaper
• Language: English
• Published: 2017
• Subjects: aerosol optical depth; aerosol vertical distribution; CALIPSO data; MODIS data; retrieval; simulation; Aerosol Robotic Network
• Online Access: http://resolver.tudelft.nl/uuid:963fc10a-2db1-41c2-a572-1d053a063433; https://doi.org/10.1002/2016JD026355

Global quantitative aerosol information has been derived from MODerate Resolution Imaging SpectroRadiometer (MODIS) observations for decades since early 2000 and widely used for air quality and climate change research. However, the operational MODIS Aerosol Optical Depth (AOD) products Collection 6 (C6) can still be biased, because of uncertainty in assumed aerosol optical properties and aerosol vertical distribution. This study investigates the impact of aerosol vertical distribution on the AOD retrieval. We developed a new algorithm by considering dynamic vertical profiles, which is an adaptation of MODIS C6 Dark Target (C6_DT) algorithm over land. The new algorithm makes use of the aerosol vertical profile extracted from Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) measurements to generate an accurate top of the atmosphere (TOA) reflectance for the AOD retrieval, where the profile is assumed to be a single layer and represented as a Gaussian function with the mean height as single variable. To test the impact, a comparison was made between MODIS DT and Aerosol Robotic Network (AERONET) AOD, over dust and smoke regions. The results show that the aerosol vertical distribution has a strong impact on the AOD retrieval. The assumed aerosol layers close to the ground can negatively bias the retrievals in C6_DT. Regarding the evaluated smoke and dust layers, the new algorithm can improve the retrieval by reducing the negative biases by 3–5%. Optical and Laser Remote Sensing Atmospheric Remote Sensing