Institute of Environmental Physics and Institute of Remote Sensing University of Bremen

1. Improving OMI NO2 retrievals TRYING TO IMPROVE BETTER UNDERSTAND OMI Science Team Meeting De Bilt, March 12, 2014 Andreas Richter, A. Hilboll, and J. P. Burrows Institute of Environmental Physics and Institute of Remote Sensing University of Bremen

2. Introduction • OMI data provides excellent coverage and unprecedented spatial detail • It will soon be followed by the TROPOMI / S5P instrument which has many similarities but promises even better performance • As preparation for S5P, IUP Bremen has started to look into OMI spectra

3. Choice of fittingwindow • Starting point is transfer of GOME-2 settings to OMI data • First check: how consistent are results from different fitting windows? • 425 – 497 nm vs 425 – 450 and 405 – 465 nm => very good • Second check: how consisitent are results with operational NASA product? • Very good correlation with 405 – 465 nm, offset of 1.5x1015molec cm-2 => will use GOME-2 settings

4. Choice of fittingwindow Why these specific settings? • Good results in GOME-2 data • Little noise • Good sensitivity to tropospheric NO2 • The same window is used in our ground-based analysis => somewhat random choice

5. Spike removal • In spite of the good signal to noise, OMI data in SAA region have large scatter • Data can easily be flagged using RMS of fits, but can‘t we do better?

6. Spike removal • Apply GOME-2 approach: • Run DOAS fit on original data • Identify “spikes“ using threshold of 10 x mean RMS • Re-run DOAS fit on reduced number of spectral points

7. Spike removal • Application to data in SAA region strongly reduces the number of outliers compared to operational NASA product • This approach ignores all flagging in lv1 data • Data elsewhere remains nearly unchanged • Is there an issue with potential for biases?

8. Spike removal • The number of useful retrievals in the SAA region is strongly increased

9. Pacific background No destriping Destriping Pacific background • Although the NO2 product is rather robust when using the mean solar background, some “stripes” are apparent • They can be removed using destriping • Use of earth-shine background leads to similar results => offset?

10. AMF dependence on NO2 column • One of the main DOAS assumptions is, that the light path enhancement (AMF) for a trace gas is independent of its column amount • For strong absorbers (O3, SO2, IR gases) this approximation is not good enough and the change of sensitivity with wavelength needs to be accounted in the fit ("modified“ DOAS) • NO2 is generally considered to be a weak absorber, but is that still true for very polluted scenarios?

11. AMF dependence on NO2 column • Up to a vertical column of about 1x1016molec cm-2, small dependence of AMF on NO2 amount • For larger columns, the AMF decreases and NO2 absorption structures appear

12. AMF dependence on NO2 column • In relative units, the effect is • 5% for a column of 5x1016molec cm-2 • 5 – 12% for 1x1017molec cm-2 • The effect is larger at smaller wavelengths (larger absorption cross-section, more Rayleigh scattering)

13. AMF dependence on NO2 column • Effect on DOAS fit on synthetic data is larger than on AMF alone as both smaller AMF and spectral structures reduce NO2 columns • Relative effect increases with SZA • Effect is similar in typical fitting windows • Even at a column of 5x1016molec cm2, the error is > 10%

14. Excursion: GOME-2 NO2 above China • Monthly GOME-2 tropospheric NO2 data are missing most of the large values • These were removed by cloud filtering as aerosol was so thick that data were classified as partially cloudy Nocloudscreening

15. Is it only Aerosols? • Even without cloud screening, there are data gaps over pollution hot spots on some days • This is due to quality checking as these fits are poor NoChisq. screening

16. Why are the fits poorer at strong pollution? • There are large and clearly structured residuals in fits over pollution hot spots • This is not random noise! • Comparison to NO2 cross-sections shows that scaling of NO2 should change over fitting window

17. Wavelength dependence of Air Mass Factor • For constant albedo, AMF of NO2 layer close to the surface increases with wavelength in a Rayleigh atmosphere • For a surface layer, this can be a significant effect • With radiative transfer modelling and a formal inversion, this should provide information on the altitude of the NO2 About+/- 20%

18. Empirical Approach for NO2(λ) • Take standard NO2 x-section • Scale to increase amplitude with wavelength • Orthogonalise to leave NO2 columns unchanged When introduced in the fit, large residuals are fixed

19. Results Empirical Approach NO2(λ): GOME-2 • The empirical NO2 AMF proxy is found over the pollution hotspot in China • It is not found at other locations where the NO2 slant column is large • There is some noise in the retrieval of the proxy

20. Results Empirical Approach NO2(λ): OMI • As for GOME-2 data, the empirical NO2 AMF proxy is found over the pollution hotspot in China • There is more noise than in GOME-2 data • Problems with row anomaly

21. Results Empirical Approach NO2(λ): OMI • At very large NO2 columns, the wavelength dependency can clearly be found in both fitting windows • The retrieval improves when including the empirical parameter • The fitting coefficient – relative to that of NO2 – should contain information on the presence of BL NO2

22. Is there more than China? GOME-2 • Fit is improved by AMF proxy everywhere over pollution hotspots

23. Comparison to NO2columns: GOME-2 • Overall pattern similar to NO2 map • Differences in distributions of maxima • Artefacts over water • noise

24. Impact of Clouds: GOME-2 • On many days in winter, very large NO2 slant columns are observed over Europe and the US • The NO2 AMF proxy picks up only very few of these signals • This is linked to the fact that most of the events are related to cloudy scenes or snow on the surface, resulting in small wavelength dependence

25. Sensitivity Study for NO2(λ) / NO2 • Synthetic data: • Rayleigh atmosphere • Constant albedo • NO2 layer in different altitudes • DOAS fit on spectra • NO2 temperature dependence corrected by using 2 NO2 x-sections • AMF proxyincluded • Ratio of AMF proxy / NO2 to normalise signal • Ratio of AMF proxy and NO2 has strong dependence on NO2 layer height • Dependence on albedo is small between 3% and 7%

26. Sensitivity Study NO2(λ) / NO2: SZA • Effect varies with SZA; larger effect at larger SZA • At large SZA, AMF proxy also found for high NO2 • Dependence on albedo is small between 3% and 7%

27. Sensitivity Study NO2(λ) / NO2: Bright Surfaces • multiple scattering over bright surfaces is stronger at shorter wavelengths • wavelength dependence of AMF is inverted • Increasing albedo reduces effect as expected for reduced importance of Rayleigh scattering • For large albedo (> 50%), negative fit factors are found for AMF proxy => wavelength dependence is inverted and only weakly dependent on altitude

28. NO2(λ) / NO2: Impact of Aerosols and Clouds Shielding • Similar effect for both, AMF proxy and NO2=> will cancel in ratio • Ratio will give layer height for cloud free part of pixel Light path enhancement • Light path enhancement in clouds / aerosols depends only weakly on wavelength • Effect on NO2 but no effect on AMF proxy • Ratio will no longer be representative of NO2 layer height

29. NO2(λ) / NO2: Case Study Highveld: GOME-2 • NO2 plume from Highveld power plants can be tracked onto the ocean • NO2 SC values increase downwind of the source • AMF Proxy also has higher values within the plume, but • Is more narrow • Has largest values at beginning of plume, not at the end of it

30. OMI NO2: The Indonesian Mystery • Clear sea- land contrast • Strange shadows • Oszillation with lower values in the North and higher values in the South

31. OMI NO2: The Indonesian Mystery • Effectis also visible in pseudo-stratosphericcolumns (nosurface a priori) • IUP productseemslessaffected Nocloudscreening, simple stratospheric AMF

32. OMI NO2: The Indonesian Mystery NASA IUP product has • Less “shadow“ structures • Less NO2over the islands (basicallyno NO2overmany of the smallerones) • Overall lowervalues => moredetailedinvestigationneeded! IUP Nocloudscreening, simple stratospheric AMF

33. Summary • We have joined the OMI NO2 club • Application of our GOME-2 settings to OMI proved successful • Good consistency between fitting windows but offset vs. Operational product • Spike removal works very well • At large NO2 values (> 5 x 1016molec cm-2), AMF becomes a clear function of NO2 column which needs to be corrected (> 10% effect) • At large BL NO2 values, the wavelength dependence of the NO2 AMF becomes relevant in the fit in particular in the GOME-2 fitting window and can be used to derive some information on NO2 layer height • Over the Indonesian region, something strange is going on in the operational NO2 product • We‘re looking forward to digging deeper into OMI (and TROPOMI) NO2 data! Funding by DLR Bonnunder Contract 50EE1247