improving retrievals of tropospheric no 2

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Improving Retrievals of Tropospheric NO 2 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

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Improving Retrievals of Tropospheric NO 2. 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 - PowerPoint PPT Presentation

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Page 1: Improving Retrievals of Tropospheric NO 2

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

Page 2: Improving Retrievals of Tropospheric NO 2

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

Page 3: Improving Retrievals of Tropospheric NO 2

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

Page 4: Improving Retrievals of Tropospheric NO 2

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

Page 5: Improving Retrievals of Tropospheric NO 2

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

Page 6: Improving Retrievals of Tropospheric NO 2

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

Page 7: Improving Retrievals of Tropospheric NO 2

Surface ReflectivitySurface Reflectivity

Lambertian Equivalent Reflectivity (LER)Lambertian Equivalent Reflectivity (LER)

Page 8: Improving Retrievals of Tropospheric NO 2

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

Page 9: Improving Retrievals of Tropospheric NO 2

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

Page 10: Improving Retrievals of Tropospheric NO 2

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

Page 11: Improving Retrievals of Tropospheric NO 2

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

Page 12: Improving Retrievals of Tropospheric NO 2

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