Development of global aerosol models using cluster analysis of Aerosol Robotic Network (AERONET) measurements

By Ali H. Omar, Jae-Gwang Won, D. Winker, Soon-chang Yoon, Oleg Dubovik, M. Patrick McCormick. 2005

396 citations recorded by the source corpus.

Subfields: Atmospheric Science, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/759f3ccdff89ff2e3fd1c3d035f784785c54a94a

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In Atmospheric Science

In Global and Planetary Change

Citations in the corpus

Comparison of CALIOP level 2 aerosol subtypes to aerosol types derived from AERONET inversion data (2009, methodology): “These classes are based on extensive analysis of AERONET observations [ Omar et al., 2005 ].”
Overview of the CALIPSO Mission and CALIOP Data Processing Algorithms (2009, methodology): “The analysis of AERONET retrievals yielded six distinct clusters, which were labeled desert dust, smoke, clean continental, polluted continental, clean marine, and polluted dust (Omar et al. 2005).”
Enhancement of aerosol characterization using synergy of lidar and sun - photometer coincident observations: the GARRLiC algorithm (2013, methodology): “For example, processing of lidar observations from the CALIPSO spaceborne platform relies on the climatological models of lidar ratio derived by cluster analysis of the entire database of AERONET retrievals obtained for∼ 10 yr of observations (Omar et al., 2005).”
EARLINET correlative measurements for CALIPSO: First intercomparison results (2010, background): “In the past, global aerosol distribution and optical properties were investigated by means of passive remote sensing instruments aboard satellites or ground ‐ based Sun photom-eter networks like AERONET [ Holben et al. , 1998; Kaufman et al. , 2000; Omar et al. , 2005; Kahn et al. , 2007].”
Aerosol classification from airborne HSRL and comparisons with the CALIPSO vertical feature mask (2013, neutral): “The types are described by Omar et al. (2009) with 1825”
The CALIPSO Lidar Cloud and Aerosol Discrimination: Version 2 Algorithm and Initial Assessment of Performance (2009, background): “SCA is composed of three main submodules: cloud– aerosol discrimination (CAD) (Liu et al. 2004), aerosol subtyping (Omar et al. 2005, 2009, hereafter OmarSI), and cloud ice-water phase discrimination (Hu et al. 2009, hereafter HuSI).”