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Intercomparison of SCIAMACHY NO 2 , the Chim è re air-quality model and surface observations

Intercomparison of SCIAMACHY NO 2 , the Chim è re air-quality model and surface observations. Nad è ge 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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Intercomparison of SCIAMACHY NO 2 , the Chim è re air-quality model and surface observations

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  1. 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

  2. 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

  3. Combined retrieval - modelling - assimilation approach to SCIA NO2 Careful treatment needed for: • Clouds • Surface albedo • Profile shape • Aerosol

  4. Slant to vertical column retrieval approach (KNMI) Air-mass factor calculation: • Temperature correction (NO2 cross section) • TM3 / TM4 (tropospheric) CTM • Assimilation of slant columns -> 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

  5. Validation results (ACVE-2), stratosphere J. C. Lambert NO2 products: • SCIA processor • IUP • SAO • BIRA-IASB • Heidelberg

  6. Combined retrieval - modelling - assimilation approach to GOME NO2

  7. Chimère model Developed in France R. Vautard, H. Schmidt, L. Menut, M. Beekman, N. Blond, ... ) Operational air-quality forecasts: http://www.prevair.org/ Model 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

  8. Emissions

  9. 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 < 50%) Advantages: • Compare model-SCIA under exactly same conditions (e.g. cloud free) • Comparison independent of profile shape assumptions in the retrieval

  10. Chimère and surface observations (RIVM, NL) • surface observation • - Chimère • Netherlands: • (rural stations) • Bias 0.1 ppb • RMS 7.2 ppb • Correl. 0.66

  11. SCIAMACHY vs. Chimère: yearly mean Yearly-mean bias = 0.2 1015 molec cm-2, RMS 2.9, correl.coeff. 0.73 Cloud-free pixels

  12. SCIAMACHY vs. Chimère: 27 Feb 2004

  13. SCIAMACHY vs. Chimère: 28 Mar 2004

  14. SCIAMACHY vs. Chimère: 16 April 2004

  15. SCIAMACHY vs. Chimère: 16 Sep 2004

  16. Synergy: Surface - Chimère - SCIAMACHY

  17. 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

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