1. OMI Nitrogen Dioxide Validation Overview Ellen Brinksma & Edward Celarier with inputs from many others (AO, NASA, …)

2. OMI NO2 Validation Overview Introduction of OMI NO2 products OMNO2 data statistics / internal correlations Validation result summary (more results later today) Planned efforts

3. OMI NO 2 Data Products Currently available: Operational NO2 product (“OMNO2”) NASA-KNMI Near-real time product (“NRT”) KNMI, plots daily at www.temis.nl Both are based on NO2 operational slant columns, differ in airmass factors, surface albedo, NO2 under clouds. OMNO2NRT Profile shapeClimatologyAssimilation Surface albedo database GOME-derived [Koelemeijer] Koelemeijer/Herman- Celarier Ghost columnNoYes Processing SIPSTMCF

4. Total & tropospheric NO 2 (2005 average, 5316 orbits) Total NO 2 Tropospheric NO 2 OMNO2 Ascending orbits only

5. Validation Preliminary Results Validation/verification effort summary Correlations OMI with surface in-situ data Only few sources of groundbased columns DOAS SAOZ Brewer MAXDOAS Profile influence quantification Satellite comparisons

6. Correlations with in situ NO2 OMI NRT compared with in situ at Hellendoorn (rural) Preliminary conclusions For NL: good average correlations (r= 0.6-0.9) Clear relationship surface NO2 and OMI columns Work by F. Boersma More correlations in talk by L. Kramer Also other European/US efforts

7. Validation Preliminary Results DOAS, SAOZ, MaxDOAS: talk by M. van Roozendael Here: Only some comparisons with MAXDOAS during Dandelions (both NO2 products) Correlations ~ 0.6 to 0.7 (low cloud fractions) Analysis ongoing Homogeneity issue (even for “rural” location)

8. MAXDOAS – OMI – SCIAMACHY NO 2 ColumnValidation (1) : Cloud free data NO 2 tropospheric vertical columns: IASB MAX-DOAS: geometrical approximation Satellites: closest pixels within 200km over Cabauw and cloud selection Groundbased MAXDOAS - BIRA/IASB, courtesy M. van Roozendael et al. OMI Correlation coef: All data: 0.21 Cloud free: 0.67 (f<0.2) SCIAMACHY Correlation coef: All data: 0.42 Cloud free: 0.43 (f<0.3)

9. Heidelberg telescopes Directions of the Heidelberg telescopes Horizontal gradients in NO 2 1° elevation Thomas Wagner, Ossama Ibrahim Most days, more pollution towards S Some days, no horizontal gradients

10. Tropospheric NO 2 Comparisons with MaxDOAS (UnivBremen) f < 5% Av. Bias 36% Corr. 0.68 Av. Bias 19% Corr. 0.65 NRT trop NO 2 operational trop NO2

11. Product comparison NRT (y) and operational (x-axis) trop NO2 from OMI f < 5% f < 20%f < 10%

12. Brewer Cede et al. Comparison with OMNO2

13. SAOZ Ionov & Goutail Comparison with OMNO2

14. Assessment of Profile Influence Measurements were performed during INTEX B (Houston) Dandelions 2005

15. Spiral 1 (down) Spiral 2 (up)

16. OMI vs. GEOS-CHEM columns Validation of all clear-sky cases Column validation based on integrated profiles r = 0.89 n = 18 DC8-OMI = +0.31 RMS = 1.18 Results (and regression) are influenced by one outlier

17. DC8 vs. GEOS-CHEM profiles n = 29 DC8 data binned to GEOS-CHEM vertical grid GEOS-CHEM sampled at flight time No extrapolation needed Work by Folkert Boersma et al. Black = DC8 Red = GEOS CHEM

18. DC8 vs. GEOS-CHEM profiles Only profiles over land Black = DC8 Red = GEOS CHEM

19. DC8 vs. GEOS-CHEM profiles Only profiles over ocean Black = DC8 Red = GEOS CHEM Conclusion: Model overestimates land BL Sea profiles captured well Confirmed by similar work by E. Bucsela Plan: Paper by Boersma Bucsela et al. on OMI NO2 products during INTEX

20. DANDELIONS retrievals GEOS-CHEM annual mean (current OMI algorithm) GEOS-CHEM annual mean (current OMI algorithm) MAX-DOAS measurements Cabauw, 2005 May-June LIDAR measurements Cabauw, 2005 May-June

21. NO 2 total vertical columns retrieved from OMI near Cabauw (*) 2005 May 13 Retrieval assuming GEOS-CHEM model profile shapes Retrieval assuming MAX-DOAS measured profile shape **

22. NO 2 total vertical columns retrieved from OMI near Cabauw (*) 2005 June 19 Retrieval assuming GEOS-CHEM model profile shapes Retrieval assuming MAX-DOAS measured profile shape **

23. Satellite comparison Compare SCIAMACHY and OMI NO2 October 2004 0.5° x 0.5° grid Bin and average data when -both SCIAMACHY and OMI are ‘cloud-free’ (f cl < 20%) -both SCIA and OMI retrievals are flagged ‘valid’ …SCIA and OMI sampled on the very same days Differences: 10:00 hrs vs. 13:45 hrs Much better statistics for OMI (more pixels per grid cell) work by Folkert Boersma

25. SCIA vs. OMI Agreement Spatial patterns agree r = 0.83 (n =1.7 x 10 5 ) Differences SCIAMACHY much noisier (10x fewer pixels) SCIAMACHY observes higher NO2 over polluted regions

26. Does OMI see less NO2? VCD=SCD/AMF Are OMI tropospheric slant columns smaller, or … … are OMI AMF’s larger?

27. Does OMI see less NO2? SCIA SCD’s lower by 0.6 10 15 SCD bias propagates into stratospheric SCD bias For tropospheric residual, this bias cancels: It is unlikely that instrumental effect explains smaller tropospheric SCD for OMI

28. Why are AMFs larger for OMI? Monthly mean cloud fractions very similar -FRESCO SCIA:0.073 -O 2 -O 2 OMI: 0.078 -Diff. too small to explain AMF differences of ~20% (plus would need smaller OMI cloud fractions) Large differences in cloud pressure!

29. OMI NO 2 Validation Summary OMNO 2 data verification Patterns realistic Large influence of stripes Correlations with clouds (no ghost column) Effects near terminator Validation preliminary results Correlations with surface reasonable (0.6-0.8) Correlations with DOAS, SAOZ reasonable (0.7) Profile influence quantification (Intex, Dandelions) SCIAMACHY / OMI similar, differences explained (Boersma, also talk by J.-C. Lambert) (continuing with future work)

30. New WSU MultiFunction DOAS Instrument George Mount, WSU location: central Washington State (~ 119W, 46N) site: campus of US Dept of Energy facility horizon: clear to about 1° weather conditions: generally clear and sunny dates: 17 April - 15 May 2006 instrument specs: spectral resolution 0.8 nm spectral sampling 6 pixels/FWHM CCD 400 x 1340 pixels 304 - 479 nm coverage can track sun can point anywhere in sky FOV: 1° x 0.01° data is currently being analysed - results at the Sept. Aura validation meeting instrument performed at about a 50% level - our 1st deployment have full met and at-site O 3, NOy, CO, etc. data

31. spectrograph CCD electronics integrating sphere filter wheel primary mirror cooling radiator solar mirror new MF DOAS instrument

32. field of view direction azimuths color coded elevation angles symbol coded angles chosen to match Chance (2005) vertical scale is prop to NO 2 slant col. horizontal scale is watch time instrument location: north of the city a main highway runs N-S nearby in situ meas. show an AM pollution plume that lasts about 3-4 h in situ meas. show only a small PM pollution plume deployed during INTEX-B

33. Future work (II) FTUVS measurements Table Mountain, Ca. (UV-VIS-NIR Spectrometer, Sander et al) Approach Method retrieves diurnal, absolute NO 2 column abundance Data twice per week, on average (time-shared with other Aura validation activities). No reference spectrum needed data west limb east limb data NO 2 referen ce N O 2 fit NO 2 columns derived from fits within microwindows that contain 10-20 NO 2 rotational lines.

34. NO 2 Column Abundance Correlation: OMI vs. FTUVS

35. FTUVS Summary and Future Work NO 2 detection by ground-based high-resolution solar absorption spectroscopy works well: * High sensitivity * Fully diurnal * No requirement for reference spectrum. The correlation with OMI NO 2 total column is reasonable. Complex topography requires that the OMI footprint centroid needs to be close to Table Mountain for best comparisons Intercomparisons will be performed at TMF with the WSU MAX-DOAS instrument in winter 06/07 Other instrument groups are invited to participate!

36. OMI NO 2 Future Efforts Future efforts Univ. Washington DOAS, Table Mountain FTUV Second NO2/aerosol campaign (Cabauw, Sep 06) MAX DOAS timeseries at KNMI Should give better collocated data, better profiles. Comparison assimilated SCIAMACHY & OMI NO2 Better investigation of main error sources: Surface albedo (need better climatology) Cloud effects Profile Homogeneity/representation errors: Comparisons during moderate pollution but away from variable sources (Cabauw?)