1. Intercomparison of tropospheric ozone measurements from TES and OMI –
Intercomparison of tropospheric ozone measurements from TES and OMI – a new method using a chemical transport model as comparison platform
Lin Zhang, Daniel Jacob, Xiong Liu, Jennifer Logan, and the TES Science Team
Following Xiong’s talk on the OMI ozone retrieval, I will show you comparing the tropospheric ozone measurements from OMI with TES.
Aura Science Team Meeting
(Oct. 28, 2008)
Work supported by NASA Earth and Space Science Fellowship

2. Concurrent ozone measurements from IR and UV
Nadir-looking instrument measuring backscattered solar radiation ( nm)
Daily global coverage at a spatial resolution of 13 x 24 km2 at nadir
Retrieve ozone at 24 ~2.5 km layers
TES
Infrared-imaging Fourier transform spectrometer ( µm)
16 orbits of nadir vertical profiles at a spatial resolution of 5x8 km2 and spaced 1.6° along the orbit track every other day.
Retrievals of ozone and CO at 67 levels from surface up to 0.1 hPa, version 3 data
As we know, Both TES and OMI are aboard Aura. They provide concurrent measurements of tropospheric ozone at 1:30 pm, but from different spectrum region. OMI measures backscattered UV radiation, while TES measurements IR.
Do they provide consistent measurements of tropospheric ozone?
What can we learn by comparing both measurements with chemical transport models?

3. Vertical sensitivity of TES and OMI ozone retrievals
Both retrievals are obtained from the optimal estimation method [Rodgers, 2000]:
Averaging kernel
July 2006
TES
Degrees of Freedom for tropospheric ozone
OMI
The first question we may have is whether the two ozone retrieval have different vertical sensitivity.
Zonal average of Diagonal terms of AK

4. 2006 ozone at 500 hPa averaged on 4ox5o resolution
Tropospheric ozone from TES and OMI
2006 ozone at 500 hPa averaged on 4ox5o resolution
Here shows TES and OMI measurements of tropospheric ozone at 500 hPa where both instruments are relatively sensitive. This shows the year 2006 averaged over each season. Notice here all the data are reprocessed with a single fixed a priori, which means all the geographic and seasonal variations are obtained from the satellite information and differences between the instruments reflect the measurement itself.
OMI observations are selected along TES pixels. The data are reprocessed with a single fixed a priori.

5. 2006 ozone at 500 hPa averaged on 4ox5o resolution
Tropospheric ozone from TES, OMI and GEOS-Chem
2006 ozone at 500 hPa averaged on 4ox5o resolution
Here we add model simulations. The model simulation is in 4x5 resolution, and sampled along TES observations at the observing time, and then smoothed by corresponding averaging kernels. Model simulation capture the general feathers. We also see some differences between the two model simulation By applying the different averaging kernels,. The one with OMI averaging kernel is overall lower than the one the TES averaging kernels in high ozone regions. This shows that some of the difference can be explained by different vertical sensitivities.
The data and model results are reprocessed with a single fixed a priori. GEOS-Chem simulation in 4ox5o resolution is sampled along the TES/OMI pixels, and then smoothed by corresponding averaging kernels.

6. Validation with ozonesonde
Ozonesonde data from , available at AURA AVDC
Coincidence Criteria: < 2o longitudes & Latitudes, < 10 hours
60oS-60oN, 500 hPa:
TES has a positive bias of 5.4 ± 9 ppbv
OMI has a positive bias of ± 5 ppbv
This figure put together the validation of TES and OMI ozone measurements at 500 hPa.

7. Methods for the intercomparison
Sparse in time and space
Validation
Validation
Generally we cannot directly compare two satellite measurements, because they have different vertical sensitivity. There are three methods to do the comparison. The first method is comparison through the sonde measurements.
1. Sonde method: Validation with ozonesonde measurements

8. Methods for the intercomparison
Sparse in time and space
Validation
Validation
The second method is direct comparison after adjusting their different a priori and averaging kernels. This is introduced by Rodgers and Conner.
Direct comparison (Rodgers and Conner, 2003)
1. Sonde method: Validation with ozonesonde measurements
2. Direct method: Compare OMI/TES directly after considering their different a priori constrains and vertical sensitivity (Apply OMI averaging kernels to TES retrievals)

9. Methods for the intercomparison
Sparse in time and space
Validation
Validation
Comparison
Comparison
Comparison
A new method is to use chemical transport models as comparison platform. In the past, we have used them separately to compare with model. Now…..
Direct comparison (Rodgers and Conner, 2003)
1. Sonde method: Validation with ozonesonde measurements
2. Direct method: Compare OMI/TES directly after considering their different a priori constrains and vertical sensitivity (Apply OMI averaging kernels to TES retrievals)
3. CTM method: Use GEOS-Chem CTM as a comparison platform

10. Methods for the intercomparison
Sparse in time and space
Validation
Validation
Evaluation
Interpretation
Evaluation
Evaluation
Interpretation
Interpretation
Now we can integrated them in the framework. we use both TES and OMI measurements to evaluate the model, and see whether they provide consistent constrains on the model simulation. Further we can use the model to interpret the observations. So now we are not just use the satellite observations to compare with the model, but can use model to transfer information between two and provide confidence to the satellite measurements.
Direct comparison (Rodgers and Conner, 2003)
1. Sonde method: Validation with ozonesonde measurements
2. Direct method: Compare OMI/TES directly after considering their different a priori constrains and vertical sensitivity (Apply OMI averaging kernels to TES retrievals)
3. CTM method: Use GEOS-Chem CTM as a comparison platform

11. What do the methods actually compare?
Let
Sonde method: TES – sonde/TES AK = bTES
OMI – sonde/OMI AK = bOMI
TES – OMI = bTES – bOMI
2. Direct method: AOMIbTES – bOMI + AOMI(ATES – I)(X – Xa)
(Rodgers and Conner, 2003)
Let’s go through some mathematics to see what the three methods really compare. Here we separate the systematic bias from the total observational errors of the retrievals (b_tes and b_omi). The first method, we use sonde to validation both satellite measurements. For the comparison purpose, we infer the differences. This is the TES – OMI bias that cannot be explained by the different vertical sensitivity.
The second method also tries to adjust the different vertical sensitivity, but it calculated something quite different. It smooth the TES bias with the OMI vertical sensitivity, and also an other term coming from the departure from the a priori. Only when both TES and OMI averaging kernels are unity matrix, it returns the true bias.
The third method, using model as the comparison platform, and use model to adjust the different vertical sensitivity. It gets the bias and with an additional term, because the model is not perfect. The additional term is 0 when the model was the truth, or …the vertical sensitivity were the same. In that case, we don’t even need to use a model or any intermediate.
3. CTM method: (TES – CTM/TES AK) – (OMI – CMT/OMI AK)
= bTES – bOMI + (ATES – AOMI)(X – XCTM)

12. Quantify differences between TES and OMI
1. TES – OMI (sonde) = bTES – bOMI
2. TES (OMI AK) – OMI = AOMIbTES – bOMI + AOMI(ATES – I)(X – Xa)
3. TES – OMI (GC) = bTES – bOMI + (ATES – AOMI)(X – XCTM)
500 hPa
76 TES/OMI/sonde coincidences for 2006
Direct method
CTM method
In the direct method, slopes < 1 reflect application of AOMI reduce the sensitivity to diagnose the bias.
The CTM method preserves the variability of the differences in the comparison.
TES – OMI Sonde method [ppbv]
We can test the three methods by using the 76 coincidences for here shows the equations for the three comparison methods. And also the scatter-plots of the TES-OMI bias obtained the three methods. The left panels show the comparison of the direct method with the sonde method. The slopes are generally smaller than 1, This reflects smoothing the TES bias with the OMI averaging kernels will reduce the ability to diagnose the differences (Rodger’s paper only mention applying it would reduce the smoothing error). Especially at 850 hPa, OMI has very low sensitivity.
In the CTM method, the right panels, it shows a high correlation, and the slopes are close to 1. The small offset from 1:1 line reflects CTM bias. But what’s more important, all the variability are preserved.
850 hPa
Direct method
CTM method
TES – OMI Sonde method [ppbv]

13. Difference between TES and OMI at 500 hPa
TES – OMI
Sonde method
Direct method
CTM method
Now broaden the picture. Here shows the comparison results from the three methods at 500 hPa. We can get a global picture from the direct and CTM methods. Again this is averaged over different seasons. We can see the direct method and sonde method show similar comparisons at this pressure level. At some places TES is higher than OMI, such as the summertime northern hemisphere, while some places TES is lower than OMI. The sonde method show some of the features. Mostly at 500 hPa the bias is smaller than 10 ppbv, and the average is close to 0. The average bias from sonde is higher, because they are averaged differently.
TES – OMI Mean ± 1 sigma
2.6 ± 6.6 ppbv
-0.1 ± 3.6 ppbv
-0.3 ± 5.0 ppbv

14. Difference between TES and OMI at 850 hPa
TES – OMI
Sonde method
Direct method
CTM method
In the lower troposphere, at 850 hPa, we see large differences among the three comparisons. The direct comparison doesn’t show any of the large differences between TES and OMI as shown from the other two methods. As we proved, in the direct method, application of the OMI averaging kernels to TES biases will reduce the variability of the differences. Especially at 850 hPa where OMI has low sensitivity, and the CTM method proves to be a better to capture the pattern.
TES – OMI Mean ± 1 sigma
3.3 ± 6.8 ppbv
-0.3 ± 1.9 ppbv
2.7 ± 5.5 ppbv

15. For 2006 and averaged on 4ox5o resolution
Differences with GEOS-Chem at 500 hPa
For 2006 and averaged on 4ox5o resolution
Minus 3 ppbv from both TES and OMI measurements. Regions with the bias between TES and OMI larger than 10 ppbv are masked as black.
GC – sonde
GC/TES AK – (TES– 3)
GC/OMI AK – (OMI– 3)
Now we show the comparison of TES and OMI ozone measurements with GEOS-Chem at 500 hPa. Here we minus 3 ppbv from both TES and OMI measurements, and we only show regions where TES and OMI are within 10 ppbv.

16. For 2006 and averaged on 4ox5o resolution
Differences with GEOS-Chem at 500 hPa
For 2006 and averaged on 4ox5o resolution
Minus 3 ppbv from both TES and OMI measurements. Regions with the bias between TES and OMI larger than 10 ppbv are masked as black.
GC – sonde
GC/TES AK – (TES– 3)
GC/OMI AK – (OMI– 3)
We can see both TES and OMI show model underestimation of ozone concentrations over the over the tropical regions, some of them are due to low biomass burning emissions in the model. We also see an overestimation over the North America in the summer. The sonde comparison show model largely overestimates ozone in the wintertime of the northern hemisphere. It is much weaker in the satellite comparison, because they both have lower sensitivity in the winter. We are looking forward to further model analyses to answer these differences.

17. Extra

18. 2006 ozone at 500 hPa averaged on 4ox5o resolution
Tropospheric ozone measurements from TES and OMI
2006 ozone at 500 hPa averaged on 4ox5o resolution
OMI observations sample along the TES pixels
OMI observations are sampled along the TES pixels.
Convert the different a priori to a fixed a priori:

19. Examples of clear-sky Averaging Kernels
(a) TES (67 levels)
(b) OMI (24 layers)