1. Concurrent measurements of tropospheric NO2 from OMI and SCIAMACHY
Folkert Boersma, Daniel Jacob, Henk Eskes, Rob Pinder, Jun Wang, and Ronald van der A
Folkert Boersma

2. Motivation Examine consistency between two satellite data sets
Example: GOME and SCIAMACHY
Jan-May 2003:
Overlap between GOME and SCIA
Average difference:
0.03
GOME
SCIA
van der A et al. (2006)

3. GOME and SCIAMACHY NO2 Same retrieval: Slant columns from BIRA
Stratospheric columns through data assimilation in TM4
Same AMF look-up table and inputs

4. OMI and SCIAMACHY NO2
GOME and SCIAMACHY overpass at similar times (10:30 and 10:00)
OMI and SCIAMACHY overpass at different times (13:30 and 10:00)
Consistent retrieval algorithms
NO2 has a relatively short lifetime (4-24 hours)
NOx has diurnal cycle in source and loss terms
Can we observe fast photochemistry and changes in emissions from space?
QUESTION:

5. Common algorithm SCIAMACHY and OMI NO2 with same retrieval
Similarities
Non-linear least squares fitting for slant columns
Stratospheric columns through data assimilation in TM4
Same AMF approach and inputs (albedo, profile shape, temp.)
Main differences
Slant column fitting details
Cloud inputs

6. Similar NO2 absorption cross sections
Relative amplitude SCIAMACHY 2.4% stronger than for OMI
SCIA
OMI
Absorption cross sections are bias-corrected here (mean bias = 2.4%; SCIA higher)
Relative difference determined by comparing top-valley distances for the six peaks (left valley-top): 2.6%; right valley top: 2.3%
Relative difference determined as standard deviation SCIA Xsec vs standard deviation OMI Xsec = 1.5%

7. Similar cloud retrievals
Clouds
-SCIAMACHY: FRESCO+ (760 nm)
-OMI: O2-O2 (470 nm)
Average cloud fraction agrees within 1.5%
Bias between cloud pressures of ~60 hPa
SCIAMACHY underestimates the reflectance by ~13% at 442 nm (10% of this is in the irradiance). Since then, improved radiometric calibration has likely solved much of the problem but BIRA fits are still being done with the Indian Ocean spectrum.

8. Collocated, cloud-free (rcl<50%) measurements at 0.5°0.5°

9. Differences larger than expected uncertainties

10. Differences over source regions
What is the cause of the differences?
- slant columns  information in the spectra
- AMF  information in the physics of retrieval
Compare slant columns and AMFs correcting for different viewing geometries:
- slant columns: Ns/Mg
- AMFs: Mtr/Mg

11. Ratio SCIAMACHY:OMI over source regions

12. What causes AMF differences?
AMF depends on:
albedo
cloud fraction
cloud height
a priori profile shape
Cloud fractions are the same within 0.05 (bias <0.015)
Differences too small to explain AMF differences of up to 20%

13. What causes AMF differences?
Differences in NO2 profile shape
Better mixed boundary layer at 13:30 hrs
NO2 profile peaks at higher altitudes
Sensitivity study with TM gives on average up to 15% higher AMFs at 13:30 compared to 10:30
How frequent is the BL updated in TM? Could this lead to too high AMFs for SCIA (and too low columns).

14. What causes AMF differences?
Differences in cloud pressures
(+60 hPa)
Sensitivity study with 60 hPa higher cloud pressures gives ~10% higher OMI AMFs

15. Diurnal variation of NO2 columns
NNO2: NO2 column
(t): NO2:NOx ratio
E(t): NOx emissions
k(t) : rate constant
NNOx: NOx column  E
rate constant k(t) applies to conversion of NOx to HNO3
NO2+OH+M  HNO3+M k

16. GEOS-Chem Relative decrease in NO2 column from 10:00 to 13:30 pm
Observed GEOS-Chem
US: -16% -28%
EU: -6% -13%
China: -26% -22%

18. 2003
2005
2001
2004
2002
Biomass burning mainly in afternoon
Jun Wang
Relative increase in NO2 column from 10am to 1:30 pm
Observed GEOS-Chem
Africa: +48% +16%
Indon.: +60% +10%
Brazil: +54% +13%

19. Extra SLIDES

20. tm4no2a-omi, v

21. tm4no2a-omi, v

22. tm4no2a-omi, v

23. Southeastern United States
August 2006
tm4no2a_omi, version 0.9
n = 27 (including days with ‘zoom in’)

26. Geometrically normalized

30. Other retrieval differences
Reference spectra
SCIAMACHY: Earth radiance Indian Ocean
OMI: solar irradiance spectrum
Stripes
SCIAMACHY: no stripes
OMI: stripe correction
(tm4no2a-omi, v0.9)
SCIAMACHY underestimates the reflectance by ~13% at 442 nm (10% of this is in the irradiance). Since then, improved radiometric calibration has likely solved much of the problem but BIRA fits are still being done with the Indian Ocean spectrum.

31. Statistical analysis slant column differences

32. Average from weighting with cos2()-1
No weighting
Average from weighting with cos2()-1
Absorption cross sections are bias-corrected here (mean bias = 2.1%; SCIA higher)
Relative difference determined by comparing top-valley distances for the six peaks (left valley-top): 2.6%; right valley top: 2.3%
Relative difference determined as standard deviation SCIA Xsec vs standard deviation OMI Xsec = 1.5%

33. Sampled when both SCIA and OMI took measurements

34. Nv,omi>
r = (n=1.9x105)
weighted:unweighted = 1.014

35. OLD
New lv0-1
New – no stripe correction
Bas Mijling

36. Retrieval method What is the air mass factor (AMF)?
represents the relative length of the light path,
converts the slant into a vertical column
M = Ns/Nv
Computed with radiative transfer model
AMF depends on a priori knowledge of atmospheric state
Cloud fraction
Cloud height
Vertical distribution NO2
Surface albedo

37. Saturday
Sunday
GOA is a project in which a.o. IUP and KNMI participate

38. Slant column of Air Mass Factor effect?
Both contribute to higher SCIAMACHY NO2 columns

39. Does China have less of a rush hour?

40. Outlook – “Veni proposal”
Key issue: relation between NOx emissions and observed NO2 columns
NO2 is chemically produced, transported, and removed: need a model
Proposal to develop data assimilation system with TM5 (0.25° x 0.25°)
Data assimilation system
Ensemble of NOx and VOC emissions
Observations NO2 & HCHO
Ensemble Kalman Filter
Improved NOx and VOC emissions + uncertainties
TM5 is global CTM with nested grid option to zoom (0.25° x 0.25°)
Apply system on U.S., Europe, and China
Also incorporate EPA, EMEP surface observations

41. EPA emission reports Trend 1995-2005 Ronald van der A (KNMI)
Studies by Richter et al. (2005) and van der A et al. (2006) also show negative trend in NO2 columns
OMI
This slide could be cut
Mobile
Other Industry
Power plants

42. Outlook – “Veni” Innovations
Hi-res satellite observations applied in air pollution in a nested-grid CTM
High regional resolution, while…
…accounting for long-range transport (China!)
Data ass. with EnKF used for the first time for estimating NOx and VOC emissions
Apply anywhere in the world xb
H(xb), P
y, R
xa = xb + K(y - H(xb))
TM5
Emiss.
OMI