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, 1,2 Aaron van Donkelaar, 1 Joanna Joiner 3 and Edward A. Celarier 4 [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

Selecting Cloud- and Aerosol-Filtered Scenes Grid MODIS Cloud MaskCheck OMI Footprint ~12 min transport Clouds in Red Cloud- and Aerosol-Filtered SceneAnalysis repeated for scenes with AOD>0.2 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)

Snow-free surface LER at 354 nm (unitless) Snow-covered surface LER at 354 nm (unitless)

OMI LER [Kleipool et al., 2008] GOME MinLER [Koelemeijer et al.,2003] TOMS MinLER [Herman & Celarier, 1997] Mean Diff. = Std (σ) = Mean Diff. = Std (σ) = Mean Diff. = Std (σ) = 0.022

Snow-Covered LER Difference (Previous Climatology – Snow-Covered Surface LER) OMI LER GOME MinLER TOMS MinLER Snow Weakly Represented in Previous Climatologies

Unrealistic Relation in OMI NO 2 versus Cloud & Snow (Inconsistent with in situ data) OMI Reported Cloud Fraction ≥ 5cm of snow 0 > snow < 5cm no snow Winter Mean Trop. NO 2 (molec/cm 2 ) Winter OMI NO 2 over Calgary & Edmonton

OMI NO 2 for Snow-Covered Scenes With Cloud Fraction Threshold (f < 0.3) To correct NO 2 retrieval for snow Use snow-covered surface reflectivity Use MODIS-determined cloud-free scenes to correct clouds NO 2 bias for MODIS-determined cloud-free scenes Positive (negative) bias from underestimated (overestimated) surface LER OMI reports clouds when surface LER is underestimated

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)

Removed Slides Corrected NO 2 Over Snow NO 2 Bias Over Snow MODIS Filtered OMI Scenes Snow-Covered Surface LER OMI Clouds Surface Reflectivity OMI NO 2

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

Is Minimum Best? Previous Reflectivity Climatologies Mean Difference Standard Deviation OMI OMI Mininum GOME Mininum TOMS Mininum

NISE Classification No Snow (0 cm) Thin Snow (0 < snow depth ≤ 5 cm) Thick Snow (snow depth > 5 cm) Snow-free Land 3872 observations Dry Snow 4301 observations 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.

Vegetation Type 95% 354 nm Max Vegetation 354 nm 95% 360 nm [Tanskanen and Manninen, 2007] Max Vegetation 470 nm [Moody et al., 2007] Water (Lakes) Evergreen Needleleaf Forest Deciduous Needleleaf Forest Deciduous Broadleaf Forest Mixed Forest Open Shrubland Woody Savannas Grasslands Permanent Wetlands Croplands Cropland/Natural Vegetation Mosaic 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.

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.

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