1. 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 18 th OMI Science Team Meeting De Bilt, Holland March 11-13, 2014

2. - 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 CALIOP AIRS 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] 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

3. 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 sites representative of the most commonly observed aerosol types: desert dust, carbonaceous, and urban-industrial particulate.

4. OMAERUV AOD validation: Comparison at representative locations Desert Dust Carbonaceous Aerosols Urban Industrial Aerosols

5. OMAERUV AOD Validation: The Global Picture Number of pairs per 0.02 AOD bin. Maximum pair density (50 to 110) shown in pink.

6. OMAERUV AOD Validation: The long-term record at selected sites Long-term OMAERUV and AERONET AOD records shows no obvious trends Aerosol Optical Depth GSFC Alta Floresta Dakar IER_Cinzana Xiang He OMAERUV AERONET

7. OMAERUV AOD Validation: The long-term sensor stability The lack of temporal trend highlights OMI sensor long-term stability

8. How does OMAERUV perform in relation to other algorithms? OMAERUV MISR MODIS-DB OMAERO Simultaneous comparison to AERONET in arid & semi-arid environments

9. -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 differenceAdjusted for wavelength difference

10. OMAERUV SSA assessment: Comparison at selected AERONET sites

11. 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

12. 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.

13. OMAERUV – SKYNET SSA COMPARISON (2006-2008) Preliminary comparison results are encouraging

14. Nine-year Global record of OMI Aerosol Absorption Optical Depth

15. 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

16. 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.

17. Backup Slides