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

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
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)
Snow-free
surface LER at
354 nm (unitless)
0
0.2
0.4
0.6
0.8
Snow-covered surface LER at 354 nm (unitless)
1
OMI LER
[Kleipool et al., 2008]
Mean Diff. = 0.0002
Std (σ) = 0.011
GOME MinLER
[Koelemeijer et al.,2003]
Mean Diff. = 0.012
Std (σ) = 0.026
TOMS MinLER
[Herman & Celarier, 1997]
Mean Diff. = -0.008
Std (σ) = 0.022
Snow Weakly Represented in Previous Climatologies
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)
Unrealistic Relation in OMI NO2 versus Cloud & Snow
(Inconsistent with in situ data)
Winter Mean Trop. NO2 (molec/cm2)
Winter OMI NO2 over Calgary & Edmonton
≥ 5cm of snow
0 > snow < 5cm
no snow
OMI Reported Cloud Fraction
OMI NO2 for Snow-Covered Scenes
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
With Cloud
Fraction
Threshold (f < 0.3)
-0.5
0
 original  corrected 
Relative NO2 Bias 

corrected


1.0
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
Surface
Reflectivity
OMI Clouds
MODIS
Filtered
OMI Scenes
Snow-Covered Surface LER
OMI NO2
NO2 Bias
Over Snow
Corrected NO2
Over Snow
Previous “Statistical” Climatologies
Kleipool et. al [2008]
Is Minimum Best?
Previous Reflectivity
Climatologies
Mean Difference
Standard
Deviation
OMI
0.0002
0.011
OMI Mininum
-0.002
0.033
GOME Mininum
0.012
0.026
TOMS Mininum
-0.008
0.022
No Snow
(0 cm)
Thin Snow
(0 < snow depth ≤ 5 cm)
Thick Snow
(snow depth > 5 cm)
Snow-free Land
3872 observations
0.31
0.49
0.20
Dry Snow
4301 observations
0.06
0.18
0.76
NISE Classification
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.
95%
354 nm
Max
Vegetation
354 nm
95%
360 nm
[Tanskanen and
Manninen, 2007]
Max Vegetation
470 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
-
0.70
-
0.69
Croplands
0.71
0.66
0.38
0.76
Cropland/Natural
Vegetation Mosaic
-
0.66
-
0.65
Vegetation Type
Permanent Wetlands
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.