The first decade OMI Near UV aerosol observations: An A-train algorithm Assessment of AOD and SSA The long-term OMAERUV record
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Combined use of OMI, CALIOP and AIRS observations in OMAERUV Aerosol Retrieval
OMAERUV uses a CALIOP-based Aerosol Layer Height Climatology and real-time AIRS
carbon monoxide data for aerosol type identification [Torres et al., 2013]
AOD June 2007 Monthly average
The combined use of AI and CO allows the identification of
smoke layers over arid areas.
AIRS CO allows the identification of heavy aerosol
loads over China, and other regions, otherwise
undistinguishable from cloud contamination.
- Torres, O., C. Ahn, and Z. Chen, Improvements to the OMI Near UV aerosol algorithm using A-train CALIOP and AIRS observations, Atmos. Meas. Tech., 6, 3257-3270, 2013
OMI 388 nm AOD retrievals were compared to AERONET observations at 44 sites
representative of the most commonly observed aerosol types: desert dust, carbonaceous, and urban-industrial particulate.
Ahn, C., O. Torres, and H. Jethva, Assessment of OMI Near UV Aerosol Optical Depth over land, JG R, accepted, 2014
Desert DustCarbonaceous Aerosols Urban Industrial Aerosols
Number of pairs per 0.02 AOD bin. Maximum pair density (50 to 110) shown in pink.
Aerosol Optical Depth
Long-term OMAERUV and AERONET AOD records shows no obvious trends
The lack of temporal trend highlights OMI sensor long-term stability
Simultaneous comparison to AERONET in arid & semi-arid environments
Unadjusted for wavelength difference
Adjusted for wavelength difference
SSA difference decreases with aerosol optical depth
SSA difference decreases rapidly with AI, close to zero for AI >2.0
SKYNET is a Japanese sky radiometer network (Chiba University) that uses sky-radiance
measurements to derive aerosol particle size and optical properties (similar to AERONET).
Aerosol absorption properties are derived at 340, 380, 400, 500, 670, 1020 nm.
Observing sites in Japan, China, Thailand, Mongolia, South Korea, India, Europe.
Preliminary comparison results are encouraging
Jethva, H., O. Torres, F. Waquet, D. Chand, and Y. Hu., How do A-train sensors inter-compare in the retrieval of above cloud aerosol optical depth? A case study assessment, Geophys. Res, Lett., 41, 186-192, 2014
OMAERUV AOD/SSA products have been evaluated by comparison to independent
ground-based and satellite observations.
On average 65% of evaluated AOD results agree with AERONET within 0.1 or 30%, yielding
0.81 corr. coef., 0.1 y-intercept, and rms 0.16
OMI retrieved SSA is quantitatively consistent with AERONET and SKYNET ground based
The correlative analysis with ground-based observations over the OMI sensor lifetime shows
remarkable longterm stability.
Future work includes the combined use of Aqua-MODIS and OMI for improved cloud