Correspondence: Xingming.liang @noaa, Tel: 301-763-8102 x149, Fax: 301-763-8572

1. Aerosol Quality Monitoring (AQUAM) Alexander Ignatov1 and XingMing Liang1,2 1NOAA/NESDIS/STAR, 2Colorado State University/CIRA 1. Introduction AQUAM is a web-based Near-Real Time tool to monitor aerosol optical depth (AOD) and angstrom exponent (AE) over global clear-sky oceans for AVHRR Ch1 (0.63um), Ch2 (0.86um) and Ch3a (1.61um) onboard NOAA-16, NOAA-17, NOAA-18, NOAA-19 and METOPA. The AVHRR AODs are produced by the Advanced Clear-Sky Processor for Oceans (ACSPO). The MODIS AODs for the AVHRR similar bands, Ch1 (0.65um), Ch2(0.86um) and Ch6(1.64um) extracted from MOD04 and MYD04 L2 product are employed as references to check AVHRR aerosol product. The current objective of AQUAM is to evaluate data quality of AVHRR AOD, check AVHRR self-consistency, and cross-consistency with the well-calibration MODIS. Our ultimate goal is aerosol correction on SST. 2. Objectives • Introduce the AQUAM system. • Demonstrate its utility to evaluate AVHRR AOD data quality and stability using daytime data. • Check AVHRR self-consistency and cross-consistency with MODIS. • Discuss the need for AVHRR AOD quality control. AQUAM: www.star.nesdis.noaa.gov/sod/sst/AQUAM 3. AVHRR/MODIS AOD global distribution and statistics 4. Evaluate AVHRR AOD Quality using Scattergram • Scattergrams are performed in both normal and log-normal scale in current AQUAM. Data in a log-log scale is more regularly distributed, due to a log-normal nature of AOD. • MODIS AOD is well quality-control compared to AVHRR. • The AOD maximum for AVHRR are smaller than MODIS, may be due to different cloud screen. • More noise in AVHRR product indicates that quality control for AVHRR AOD is needed. Global histogram in log(AOD) space for NOAA-19 AOD1 vs AOD2 NOAA-19 AOD1 (0.63μm), 2011-07-03 AQUA AOD1 (0.63μm), 2011-07-03 Global histogram in AOD space for AQUA Global histogram in AOD space for NOAA-19 Global histogram in log(AOD) space for AQUA AE12 vs AOD1 5. Evaluate self-consistency and cross-consistency using AOD time series AOD1 geometric mean AOD1 geometric standard deviation • The AOD log-normal distribution is more Gaussian and more appropriate to evaluate AVHRR AOD characteristic than normal. • The best ocean AOD field in MOD04/MYD04 L2 with cloud screen and quality assurance are selected as reference to evaluate AVHRR AOD data quality. • The AVHRR AOD are smaller than MODIS, but the geometric standard deviation are larger, indicate the AVHRR AOD is not well quality. • The global coverage for AVHRR is large than MODIS. AOD1 minimum Number of pixels (x 10000) 6. Conclusion and Future Work • AQUAM is a NRT web tool to monitor AVHRR aerosol product. It can be employed to evaluate AVHRR AOD quality, sensor self-consistency and cross-sensor consistency with MODIS. • AVHRR global coverage and number of pixels are larger than MODIS, may due to different cloud mark. • Geometric mean of AVHRR AOD is smaller than MODIS, and geometric SD is larger than MODIS during whole period. • More noise in AVHRR scattergrams indicate the QC for AVHRR AOD is needed. • Using Community Radiative Transfer Model (CRTM) simulation with the Goddard Chemistry Aerosol Radiation and Transport (GOCART), or Navy Aerosol Analysis and Prediction System, (NAAPS) as input to further evaluate AVHRR AOD is under way. • Adding AERONET analysis is also under way. Including in-situ data and model simulation will be expected to improved AVHRR AOD quality and ACSPO cloud mask. • Extending AQUAM analysis to VIIRS sensor is also planned in the next step. • Ten years MODIS data show significant seasonal cycle and AODs are central at about 0.17. • Geometric mean of AVHRR AODs are persistently smaller than MODIS, and the geometric SD are larger than MODIS, prove that AVHRR is not well calibration sensor in solar reflectance bands, improve AVHRR QC is necessary. • The negative AOD can be expected to used for the sensor calibration (Ignatov and Stowe, 2002). MODIS don’t have negative value, may due to MODIS QA flag selection in AQUAM. • AVHRR is better self-consistency than MODIS although it show more noise. • The number of pixels for AVHRR is larger than MODIS during all period. Acknowledgement This work is conducted under the Algorithm Working Group funded by GOES-R Program Office, NPOESS Ocean Cal/Val funded by IPO, and Polar PSDI, NDE and ORS Programs funded by NOAA. Thanks go to Lorraine Remer of NASA/GSFC for help in MOD04 and MYD04 product; Mark Liu, Yong Han, Yong Chen, Paul Van Delst, Fuzhong Weng of NOAA/NESDIS for help on CRTM simulation; Saran Lu of NOAA/NWS/NCEP, Mian Chen, Peter Colarco and Arlindo daSilva of NASA/GSFC for help on GOCART data. The views, opinions, and findings contained in this report are those of the authors and should not be construed as an official NOAA or U.S. Government position, policy, or decision. Gordon Research Conferences, Colby College, Waterville, ME, 10-15 July 2011 Correspondence: Xingming.liang@noaa.gov, Tel: 301-763-8102 x149, Fax: 301-763-8572