Selecting Cloud- and Aerosol-Filtered Scenes

1. Surface Reflectivity from OMI using MODIS to Eliminate Clouds: Effects of Snow on UV-Vis Trace Gas Retrievals Gray O’Byrne,1 Randall V. Martin,1,2Aaron van Donkelaar,1 Joanna Joiner3 and Edward A. Celarier4 [1] Department of Physics and Atmospheric Science, Dalhousie University, Halifax, Nova Scotia, Canada [2] Harvard-Smithsonian Center for Astrophysics, Cambridge, Massachusetts, USA [3] National Aeronautics and Space Administration, Goddard Space Flight Center, Greenbelt, Maryland, USA [4] SGT, Inc., Greenbelt, Maryland, USA

2. Selecting Cloud- and Aerosol-Filtered Scenes Clouds in Red Grid MODIS Cloud Mask Check OMI Footprint ~12 min transport Analysis repeated for scenes with AOD>0.2 Cloud- and Aerosol-Filtered Scene Use LER from OMRRCLD as Surface LER for filtered scenes Separate Snow-Free and Snow According to NISE Dry Snow flag Reject Additional scenes: -According to Sun Glint flag -If OMRRCLD cloud (or scene) pressure is 100hPa away from Surface Pressure -If LER > 0.3 (snow-free case only)

3. Snow-free surface LER at 354 nm (unitless) 0 0.2 0.4 0.6 0.8 1 Snow-covered surface LER at 354 nm (unitless)

4. OMI LER [Kleipool et al., 2008] GOME MinLER [Koelemeijer et al.,2003] TOMS MinLER [Herman & Celarier, 1997] Mean Diff. = 0.0002 Std (σ) = 0.011 Mean Diff. = 0.012 Std (σ) = 0.026 Mean Diff. = -0.008 Std (σ) = 0.022

5. OMI LER GOME MinLER TOMS MinLER -0.8 -0.6 -0.4 -0.2 0 0.2 Snow-Covered LER Difference (Previous Climatology – Snow-Covered Surface LER) Snow Weakly Represented in Previous Climatologies

6. Unrealistic Relation in OMI NO2 versus Cloud & Snow Winter OMI NO2 over Calgary & Edmonton ≥ 5cm of snow 0 > snow < 5cm no snow Winter Mean Trop. NO2 (molec/cm2) OMI Reported Cloud Fraction (Inconsistent with in situ data)

7. OMI NO2 for Snow-Covered Scenes With Cloud Fraction Threshold (f < 0.3) -0.5 0 1.0 • To correct NO2 retrieval for snow • Use snow-covered surface reflectivity • Use MODIS-determined cloud-free scenes to correct clouds • NO2 bias for MODIS-determined cloud-free scenes • Positive (negative) bias from underestimated (overestimated) surface LER • OMI reports clouds when surface LER is underestimated

8. Moving Forward • Separate LER databases for snow-free and snow-covered scenes • BRDF representation of surface • MODIS for snow detection • Future instruments with discrete bands at longer wavelengths (for cloud and snow discrimination)

9. Removed Slides Surface Reflectivity OMI NO2 OMI Clouds NO2 Bias Over Snow Filtered OMI Scenes MODIS Corrected NO2 Over Snow Snow-Covered Surface LER

10. Previous “Statistical” Climatologies Kleipool et. al [2008]

11. Is Minimum Best?

12. Table 2. Comparison of the NISE classification in the OMI snow flag to collocated ground based measurements of snow depth. For the Snow-free and Dry Snow classifications a breakdown is given of the fraction of measurements that fall into 3 different snow depth categories. The data are from November, December, January, February and March of 2005 and 2006 over Edmonton and Calgary, Canada.

13. Table 3. OMI derived surface LER of various snow-covered land types. The IGBP percentage land types are taken from the MODIS land cover product. The first method (95%) uses only grid squares containing at least 95% of a single land type to infer the mean LER. The second method (Max Vegetation) uses the maximum land cover type for each grid square. Results from two other sources are presented for comparison.

14. Figure 4. Monthly mean LER of seasonal snow-covered lands at 354 nm. Only locations with clear-sky observations of non-climatological snow cover for all six months (Nov-Apr) are used in computing the mean LER. Mountainous regions are masked. Error bars represent the standard deviation of the spatial mean.

15. Figure 6. Random AMF error versus surface reflectivity for tropospheric NO­2 over Edmonton, Canada.