surface reflectivity from omi using modis to eliminate clouds: effects of snow on uv-vis trace gas...
TRANSCRIPT
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 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
Selecting Cloud- and Aerosol-Filtered Scenes
Grid MODIS Cloud Mask Check 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 flagReject 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)0 0.2 0.4 0.6 0.8 1
OMI LER[Kleipool et al., 2008]
GOME MinLER[Koelemeijer et al.,2003]
TOMS MinLER[Herman & Celarier, 1997]
Mean Diff. = 0.0002Std (σ) = 0.011
Mean Diff. = 0.012Std (σ) = 0.026
Mean Diff. = -0.008Std (σ) = 0.022
Snow-Covered LER Difference (Previous Climatology – Snow-Covered Surface LER)-0.8 -0.6 -0.4 -0.2 0 0.2
OMI LER
GOME MinLER
TOMS MinLER
Snow Weakly Represented in Previous Climatologies
Unrealistic Relation in OMI NO2 versus Cloud & Snow(Inconsistent with in situ data)
OMI Reported Cloud Fraction
≥ 5cm of snow
0 > snow < 5cm
no snow
Win
ter
Mea
n T
rop
. N
O2
(mo
lec/
cm2 )
Winter OMI NO2 over Calgary & Edmonton
OMI NO2 for Snow-Covered Scenes
corrected
correctedoriginal Bias NO Relative 2
With CloudFractionThreshold (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
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 NO2
Over Snow
NO2 BiasOver Snow
MODIS Filtered
OMI Scenes
Snow-Covered Surface LER
OMI Clouds
SurfaceReflectivity
OMI NO2
Previous “Statistical” Climatologies
Kleipool et. al [2008]
Is Minimum Best?
Previous Reflectivity Climatologies
Mean DifferenceStandard Deviation
OMI 0.0002 0.011
OMI Mininum -0.002 0.033
GOME Mininum 0.012 0.026
TOMS Mininum -0.008 0.022
NISE Classification
No Snow(0 cm)
Thin Snow(0 < snow depth ≤ 5 cm)
Thick Snow(snow depth > 5 cm)
Snow-free Land3872 observations
0.31 0.49 0.20
Dry Snow4301 observations
0.06 0.18 0.76
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 Vegetation354 nm
95%360 nm[Tanskanen and Manninen, 2007]
Max Vegetation470 nm[Moody et al., 2007]
Water (Lakes) 0.82 0.82 - -
Evergreen Needleleaf Forest 0.22 0.38 0.28 0.36
Deciduous Needleleaf Forest 0.32 0.39 0.30 0.43
Deciduous Broadleaf Forest - 0.17 - 0.43
Mixed Forest 0.21 0.32 - 0.39
Open Shrubland 0.80 0.75 0.83 0.73
Woody Savannas - 0.50 - 0.47
Grasslands 0.76 0.75 0.72 0.72
Permanent Wetlands- 0.70 - 0.69
Croplands 0.71 0.66 0.38 0.76
Cropland/Natural Vegetation Mosaic - 0.66 - 0.65
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 NO 2 over Edmonton, Canada.