Intercomparison of SCIAMACHY NO2,

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Intercomparison of SCIAMACHY NO2, the Chimère
air-quality model and surface observations
Nadège Blond, LISA, Paris, France Henk Eskes,
Folkert Boersma, Ronald van der A KNMI,
Netherlands Michel van Roozendael, Isabelle De
Smedt BIRA-IASB, Belgium
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Slant column retrieval approach (BIRA-IASB)
DOAS slant column "raw" L1, v 4.02 L1, v 5.01
L1 Non-linear least-squares inversion
(Marquardt-Levenberg) Wavelength window 426.3
- 451.3 NO2 243K (Bogumil), O3 (Bogumil),
O2-O2, H2O 2nd order polynomial Undersampling
cross section Ring (Vountas) Offset
correction based on measurement over Indian Ocean
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Combined retrieval - modelling - assimilation
approach to SCIA NO2
Careful treatment needed for Clouds Surface
albedo Profile shape Aerosol
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Slant to vertical column retrieval approach (KNMI)
Air-mass factor calculation Temperature
correction (NO2 cross section) TM3 / TM4
(tropospheric) CTM Assimilation of slant
columns -gt stratospheric "background" Fresco
cloud fraction and cloud top pressure TOMS /
GOME combined albedo map (Herman, Koelemeijer)
DAK RTM height-dependent AMF lookup table
Tropospheric AMF based on TM profile shape,
clouds Product Detailed error estimates
Averaging kernels
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Validation results (ACVE-2), stratosphere J. C.
Lambert
NO2 products SCIA processor IUP SAO
BIRA-IASB Heidelberg
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Combined retrieval - modelling - assimilation
approach to GOME NO2
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Chimère model
Developed in France R. Vautard, H. Schmidt, L.
Menut, M. Beekman, N. Blond, ... ) Operational
air-quality forecasts http//www.prevair.org/ Mo
del ingredients MELCHIOR chemistry (82
species, 333 reactions) EMEP emissions ECMWF
meteorological analyses 15 vertical layers,
surface - 200 hPa Boundary conditions from
MOZART monthly-mean climatology
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Emissions
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Intercomparisons Chimère, SCIA and surface
observations
Motivation Lack of profile observations of NO2
for validation purposes use model as
intermediate for indirect validation
study Approach Space-time collocation of
Chimère fields to individual SCIA pixels
Application of averaging kernels Simulated
SCIA-equiv column kernel vector model NO2
profile One year of SCIA data, 2003 Cloud
free (cloud radiance lt 50) Advantages
Compare model-SCIA under exactly same conditions
(e.g. cloud free) Comparison independent of
profile shape assumptions in the retrieval
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Chimère and surface observations (RIVM, NL)

• surface observation
• - Chimère
• Netherlands
• (rural stations)
• Bias 0.1 ppb
• RMS 7.2 ppb
• Correl. 0.66

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SCIAMACHY vs. Chimère yearly mean
Yearly-mean bias 0.2 1015 molec cm-2, RMS 2.9,
correl.coeff. 0.73 Cloud-free pixels
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SCIAMACHY vs. Chimère 27 Feb 2004
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SCIAMACHY vs. Chimère 28 Mar 2004
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SCIAMACHY vs. Chimère 16 April 2004
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SCIAMACHY vs. Chimère 16 Sep 2004
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Synergy Surface - Chimère - SCIAMACHY
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Conclusions NO2 comparisons
SCIAMACHY - Chimère - surface Yearly mean
- very small bias SCIA - Chimère and
Chimère - surface - Correlation coefficients
0.7 typically SCIA and Chimère resolution
comparable Extended NO2 plumes compare well
Details show differences - Seasonality
(winter Chimère higher) - Individual days
- Distribution - Amount of detail