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Retrieval, validation and assimilation of SCIAMACHY ozone columns

Retrieval, validation and assimilation of SCIAMACHY ozone columns. Henk Eskes, Ronald van der A, Ellen Brinksma, Pepijn Veefkind, Johan de Haan, Pieter Valks Royal Netherlands Meteorological Institute (KNMI) 1) Ozone column retrieval 2) Validation of SCIAMACHY ozone columns

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Retrieval, validation and assimilation of SCIAMACHY ozone columns

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  1. Retrieval, validation and assimilation of SCIAMACHY ozone columns Henk Eskes, Ronald van der A, Ellen Brinksma, Pepijn Veefkind, Johan de Haan, Pieter Valks Royal Netherlands Meteorological Institute (KNMI) 1) Ozone column retrieval 2) Validation of SCIAMACHY ozone columns 3) Data assimilation and validation

  2. Ozone column retrieval: new DOAS algorithm Heritage: • Based on the OMI operational ozone column algorithm OMI-DOAS - Pepijn Veefkind, J. De Haan • Implementation for GOME TOGOMI - P. Valks, R. Van Oss • Implementation for SCIAMACHY TOSOMI - H. Eskes and R. van der A ESA ITT - GOME total-ozone algorithm • BIRA • IFE - University Bremen • KNMI

  3. Innovations compared to e.g. GOME Fast Delivery, GDP vs 3 • New treatment of rotational Raman scattering (J. de Haan) • Empirical air-mass factor approach • TOMS v8 ozone profile data base • FRESCO cloud cover and cloud top height • Radiative transfer improvements • T-dep O3 cross section: ECMWF temperature profiles

  4. Rotational Raman: impact on retrieval Normalized Incident Sunlight Ozone Absorption Single Rayleigh Scattering Cabannes not scrambled 96.2 % Raman scrambled 3.8 % Ozone Absorption Ozone Absorption Received by sensor

  5. New approach to rotational Raman scattering Difference between old and new treatment of Raman

  6. Empirical air-mass factor

  7. DOAS fit example rms 0.5 %

  8. SCIAMACHY TOSOMI Example

  9. Validation: midlatitude example Brewer De Bilt (52.1 N, 5.18E) bias: -1.8% rms: 4.3% No obvious seasonal bias

  10. Validation: summary

  11. Validation: summary vs. latitude Main conclusions: • Tosomi 1.5% lower than ground based • RMS 4.9% increasing with ozone variability (representativity) • No clear geographical location dependence • No clear seasonal dependence

  12. Ozone data assimilation at KNMI • TM3DAM assimilation code: • • Driven by 6h meteo from ECMWF (wind, temperature, pres): • analyses and 10-day forecasts • • Same vertical levels as ECMWF (subset in lower troposphere) • • Second moments advection (Prather) • • Sub-optimal Kalman filter, detailed error covariance modelling • • Ozone chemistry parametrizations: • • Cariolle gas phase • • "Cold tracer" scheme for heterogeneous chemistry

  13. Assimilation: example

  14. Validation: RMS Tosomi retrieval - assimilation rms about 3% average total rms = retrieval + forecast + represent. DU

  15. Validation: RMS Tosomi retrieval - GOME-Togomi assimilation rms about 3% average total rms = retrieval + forecast + represent.

  16. Tropospheric Emission Monitoring Internet Service http://www.temis.nl/ ESA-Data User Programme project Sciamachy ozone related products: • Near-real time total ozone from Sciamachy, delivery to ECMWF for assimilation • 9-day medium-range forecasts of ozone • UV forecasts

  17. Summary / conclusions Tosomi: SCIAMACHY ozone column retrieval algorithm based on OMI-DOAS Validation with ground-based observations: No clear latitude dependence (or seasonal dependence) bias -1.5 % Validation with data assimilation: Important for the development of the retrieval implementation OmF: shows neutral lon-lat behaviour, rms 3% Data available onhttp://www.temis.nl/ Near-real time ozone columns, analysis records and 9-day forecasts of ozone and UV index 2004 ozone hole

  18. Validation of Tosomi retrieval with assimilation

  19. Validation of Tosomi retrieval with assimilation Theoretical error due to inaccurate lookup-table discretisation

  20. Validation of Tosomi retrieval with assimilation Before … DU

  21. Ozone hole 2004

  22. Ozone hole 2004 EP TOMS SCIAMACHY

  23. Ozone hole 2004

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