improving retrievals of tropospheric no 2 randall martin, dalhousie and harvard-smithsonian lok...
TRANSCRIPT
Improving Retrievals of Tropospheric NO2
Randall Martin, Dalhousie and Harvard-Smithsonian
Lok Lamsal, Gray O’Byrne, Aaron van Donkelaar, Dalhousie
Ed Celarier, Eric Bucsela, Joanna Joiner, NASA
Folkert Boersma, Ruud Dirksen, KNMI
Chao Luo, Yuhang Wang, Georgia Tech
September 14, 2009
Air Quality Working Group
Aura Meeting
Leiden, Netherlands
Seasonal Differences Between OMI NOSeasonal Differences Between OMI NO22 Products ProductsDirect Validation Has Not Arbitrated
0.1 1 2 3 4 5 6 7 8 9 10 Tropospheric NO2 Column (1015 molecules cm-2)
Standard (SP) DOMINO (DP) DP-SP
DJF 2005
JJA 2005
-5 -3 -1 1 3 5 Δ(1015 molecules cm-2)
Lamsal et al., JGR, submitted
Indirect Validation of OMI
(A) In-situ surface NO2 measurements from the SEARCH (photolytic) and EPA/AQS (molybdenum) networks at rural sites in Eastern US
Use GEOS-Chem NO2 profiles to estimate surface-level NO2 from OMI (Lamsal et al., JGR, 2008)
Apply GEOS-Chem to infer top-down emissions from OMI by mass balance (Martin et al., JGR, 2003)
(B) Updated bottom-up emission inventories for 2005-2006
Multiple Approaches Yield Similar ResultsMultiple Approaches Yield Similar Results
NOx Emissions, SEARCH domain NOx Emissions, US + Canada
SEARCH “True” NO2”, Southeast U.S. AQS/EPA “Corrected” NO2, Eastern U.S.
Lamsal et al., JGR, submitted
Stratosphere-troposphere Separation and AMF Together Explain Difference Between DP and SP
Air mass factor Strat-trop separation Combined
ΔTropospheric NO2 Column DP – SP (1015 molecules cm-2)
Lamsal et al., JGR, submitted
Produce DP_GC From DP Averaging Kernels and GEOS-Chem NOProduce DP_GC From DP Averaging Kernels and GEOS-Chem NO22 Profiles Profiles
NOx Emissions, SEARCH domain NOx Emissions, US + Canada
SEARCH “True” NO2”, Southeast U.S. AQS/EPA “Corrected” NO2, Eastern U.S.
Lamsal et al., JGR, submitted
Surface ReflectivitySurface Reflectivity
Lambertian Equivalent Reflectivity (LER)Lambertian Equivalent Reflectivity (LER)
OMI LER (Kleipool et al. 2008) Best Represents Surface LEROMI LER (Kleipool et al. 2008) Best Represents Surface LER
Use MODIS/Aqua to Eliminate Cloud and Aerosol from OMI Scenes
Use NISE Snow Flag to Eliminate Snow
Cloud-, Snow-, and Aerosol- Free LER (2005-2007)
Global Annual Mean Difference x100 (unitless)
Standard Deviation x100 (unitless)
TOMS MinLER -0.8 2.2
GOME MinLER 1.2 2.6
OMI MinLER -0.2 3.3
OMI LER (if snow-free) 0.02 1.1LER Difference of 2% 15-30% Bias in NO2 (Martin et al., 2002; Boersma et al., 2004)
O’Byrne et al., JGR, submitted
Unrealistic Relation in OMI NOUnrealistic Relation in OMI NO22 versus Cloud & Snow versus Cloud & Snow(In situ NO2 data show variation < 15%)
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
O’Byrne et al., JGR, submitted
Snow-covered Surface LER (unitless)
0 0.2 0.4 0.6 0.8 1
Large Spatial Variation in Snow-Covered Surface LERLarge Spatial Variation in Snow-Covered Surface LERCurrent Algorithms Assume Snow Reflectivity = 0.6Current Algorithms Assume Snow Reflectivity = 0.6
Snow Weakly Represented in Previous ClimatologiesSnow Weakly Represented in Previous ClimatologiesLeads to Ambiguity in Accounting for SnowLeads to Ambiguity in Accounting for Snow
O’Byrne et al., JGR, submitted
Snow-Covered LER Difference (Previous Climatology – Snow-Covered Surface LER)
-0.8 -0.6 -0.4 -0.2 0 0.2
OMI LER
Spatially-Varying Biases in OMI NO2 over Snow
2
original correctedRelative NO Bias 100*
corrected
With CloudFractionThreshold (f < 0.3)
-50 0 100
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
O’Byrne et al., JGR, submitted
With All CloudFractions
RecommendationsRecommendationsRemote Sensing Community:• Use two reflectivity databases: one snow-free, one for snow
•Switch from TOMS or GOME reflectivity databases to OMI
•Switch from annual mean to monthly mean NO2 profiles for SP
• Evaluate Stratosphere-Troposphere Separation
• Develop instrumentation with finer spatial resolution (more cloud-free scenes reduces dependence on assumed profile )
• Following DP, include Averaging Kernels (or Scattering Weights) in trace gas products so the user can remove the effect of the assumed profile
Modeling Community: Continue develop representation of vertical profile
Ground-based Measurement Needs:
•span satellite footprint•full year •research quality (e.g. NO2)•vertical profile