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

- 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

OMICALIOP

AIRS

Combined use of OMI, CALIOP and AIRS observations in OMAERUV Aerosol RetrievalOMAERUV uses a CALIOP-based Aerosol Layer Height Climatology and real-time AIRS carbon monoxide data for aerosol type identification [Torres et al., 2013]

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.

AOD June 2007 Monthly average

Without CO

With CO

Ahn, C., O. Torres, and H. Jethva, Assessment of OMI Near UV Aerosol Optical Depth over land, JG R, accepted, 2014

Global Validation of OMAERUV Aerosol Optical Depth

OMI 388 nm AOD retrievals were compared to AERONET observations at 44 sitesrepresentative of the most commonly observed aerosol types: desert dust, carbonaceous, and urban-industrial particulate.

OMAERUV AOD validation: Comparison at representative locations

Desert Dust Carbonaceous 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

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

Aero

sol O

ptica

l Dep

thGSFC

Alta Floresta

Dakar

IER_Cinzana

Xiang He

OMAERUV AERONET

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

-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

Comparison of OMAERUV Single Scattering Albedo to AERONET Observations

Unadjusted for wavelength difference Adjusted for wavelength difference

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

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

OMI Retrieval of Aerosols above clouds: Multi-sensor comparison

Concluding Remarks

OMAERUV AOD/SSA products have been evaluated by comparison to independentground-based and satellite observations.

On average 65% of evaluated AOD results agree with AERONET within 0.1 or 30%, yielding0.81 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 showsremarkable longterm stability.

Future work includes the combined use of Aqua-MODIS and OMI for improved cloud contamination screening.

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