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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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  • The first decade OMI Near UV aerosol observations:

  • An A-train algorithm

  • Assessment of AOD and SSA

  • The long-term OMAERUV record

  • Omar Torres, Changwoo Ahn, Hiren Jehva

  • NASA,GSFC/SSAI/GESTAR-USRA

  • The 18th OMI Science Team Meeting

  • De Bilt, Holland

  • March 11-13, 2014


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

CALIOP

OMI

AIRS

Without CO

With CO

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


Global Validation of OMAERUV Aerosol Optical Depth

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


OMAERUV AOD validation: Comparison at representative locations

Desert DustCarbonaceous Aerosols Urban Industrial Aerosols


OMAERUV AOD Validation: The Global Picture

Number of pairs per 0.02 AOD bin. Maximum pair density (50 to 110) shown in pink.


OMAERUV AOD Validation: The long-term record at selected sites

GSFC

Alta Floresta

Dakar

Aerosol Optical Depth

IER_Cinzana

Xiang He

OMAERUV AERONET

Long-term OMAERUV and AERONET AOD records shows no obvious trends


OMAERUV AOD Validation: The long-term sensor stability

The lack of temporal trend highlights OMI sensor long-term stability


How does OMAERUV perform in relation to other algorithms?

OMAERUV

MISR

MODIS-DB

OMAERO

Simultaneous comparison to AERONET in arid & semi-arid environments


Comparison of OMAERUV Single Scattering Albedo to AERONET Observations

Unadjusted for wavelength difference

Adjusted for wavelength difference

  • Jethva, H., O. Torres, and C. Ahn, Global Assessment of OMI Aerosol single-scattering albedo in relation to Ground-based AERONET Inversion, J. Geophys. Res., submitted, 2014


OMAERUV SSA assessment: Comparison at selected AERONET sites


OMAERUV SSA assessment: Summary of results

SSA difference decreases with aerosol optical depth

SSA difference decreases rapidly with AI, close to zero for AI >2.0


OMAERUV SSA evaluation using SKYNET observations (new results!)

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.


OMAERUV – SKYNET SSA COMPARISON (2006-2008)

Preliminary comparison results are encouraging


Nine-year Global record of OMI Aerosol Absorption Optical Depth


OMI Retrieval of Aerosols above clouds: Multi-sensor comparison

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


Concluding Remarks

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

observations.

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

contamination screening.


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