3.1 Evaluating Lower Threshold Sensitivities of Level 2 CALIOP AOT

Tuesday, 24 January 2017: 8:30 AM
Conference Center: Skagit 4 (Washington State Convention Center )
Travis D. Toth, Univ. of North Dakota, Grand Forks, ND; and J. R. Campbell, J. S. Reid, J. L. Tackett, M. A. Vaughan, and J. Zhang

For the past decade, the NASA Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) has provided global active-based space-borne retrievals of aerosol optical properties, such as aerosol optical thickness (AOT).  Due to instrument sensitivities and algorithm detection limits, null retrievals occur where no aerosol layers are detected through a corresponding profile column and the integrated AOT is zero.  The resulting Level 2 aerosol extinction profile thus consists of all retrieval fill values (RFV).  This causes an undersampling of total atmospheric aerosol loading within the global product, with implications for CALIOP-based studies involving aerosol modeling, air quality, and aerosol climate impacts.  Ambiguities already exist within the literature as a direct result of this issue, found as inconsistencies between published reports of global mean AOT observed using CALIOP.

In this study, using two years (2010-2011) of CALIOP Version 3 Level 2 aerosol profile data, the frequency of occurrence of the RFV issue is described.  Globally, RFV profiles comprise ~70% of all daytime 5 km CALIOP profiles and 45% of daytime cloud-free profiles, demonstrating the large number of data points rendered ambiguous by this issue.  Collocated Moderate Resolution Imaging Spectroradiometer (MODIS) and Aerosol Robotic Network (AERONET) observations are further used to study the lower detectability limit to AOT observed by CALIOP.  We find primary modes of AOT undersampling at 0.05.  RFV occurrence as a function of MODIS AOT is used to propose a potential parameterization that overcomes undersampling, thus rendering a better regional and global CALIOP-derived mean AOT.  Our understanding of the lower sensitivity of CALIOP-derived AOT will help guide instrumentation design and algorithm development for lidars of future satellite missions, such as Aerosol-Cloud-Ecosystems (ACE).

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