1. Value of geostationary observations in a data assimilation system for ozone air quality in the US
Peter Zoogman, Daniel Jacob
Zoogman, P., D.J. Jacob, K. Chance, L. Zhang, P. Le Sager, A.M. Fiore, A. Eldering, X. Liu, V. Natraj, S.S. Kulawik, Ozone air quality measurement requirements for a geostationary satellite mission, Atmos. Environ., 45, 7,143-7,150, 2011.[
Zoogman, P., D.J. Jacob, K. Chance, H.M. Worden, D.P. Edwards, and L. Zhang, Improved monitoring of surface ozone air quality by joint assimilation of geostationary satellite observations of ozone and CO, Atmos. Environ., 84, , 2014.
Zoogman, P., D.J. Jacob, K. Chance, X. Liu, A. Fiore, M. Lin, K. Travis, Monitoring high-ozone events in the US Intermountain West using TEMPO geostationary satellite observations, Atmos. Chem. Phys., 14, , 2014. 

2. Exploiting ozone-CO transport error correlation in ozone data assimilation
General idea:
Boundary layer CO is better observed from space than ozone
Ozone assimilation could be improved in a joint ozone-CO system
by accounting for ozone-CO transport error correlation in forecast model
Problem is that sign of ozone-CO error correlation varies in scale-dependent way
Correlations and error correlations in GEOS-Chem at 50 km resolution, summer 2004
Correlation of concentrations is completely different from error correlation
Positive error correlation over ocean (outflow) and in plumes, negative error correlation over most of land
Magnitude and sign of error correlation depend on model resolution
Conclusion: too difficult to exploit
Zoogman et al. [2014a]

3. Can TEMPO data assimilation help monitor ozone in Intermountain West?
EPA [2014]
4th highest annual 8-h average ozone,
standard: 70 ppb
Seasonal dose in excess of 60 ppb [EPA, 2014]
Intermountain West
CMAQ with data fusion
Intermountain West is the only area in US where ozone is not decreasing
Background/stratospheric influences are a big issue
It is sparsely observed by ground monitors – can TEMPO help?

4. Produce synthetic TEMPO ozone data with GFDL AM3 as “true” atmosphere
Sample this virtual atmosphere
on TEMPO observing schedule
Use TEMPO averaging kernel matrix to simulate what TEMPO would see
Pressure, hPa
Sensitivity
DOFS
TEMPO synthetic ozone data

5. Assimilate synthetic TEMPO data into GEOS-Chem CTM
The “truth” and assimilation CTMs are independent
“True”
model
AM3-Chem
Instrument
Specification
Synthetic Observations
Forecast
GEOS-Chem
Assimilation
(Kalman filter)
Optimal
Estimate
Compare to “True” model
“Truth” = AM3-Chem model
0.5ox0.5o
Forecast = nested GEOS-Chem
0.5ox0.67o
ppbv
Zoogman et al. (2014)

6. Ability of surface+TEMPO ozone assimilation system
to reproduce “true” surface ozone in Intermountain West
April-June 2010 daily surface ozone
on 1/2ox2/3o grid
Free-running GEOS-Chem model
with assimilation of surface data
with assimilation of surface+TEMPO data
Zoogman et al. (2014)

7. Ability of assimilation system to reproduce frequency of high-ozone days
3-month assimilation for April-June 2010
Zoogman et al. (2014)

8. Ability of TEMPO assimilated data to observe stratospheric intrusions in the Intermountain West
Stratospheric ozone intrusion over New Mexico, 13 June 2010

9. Findings Recommendation
Assimilation of TEMPO UV/Vis ozone can significantly improve monitoring of ozone air quality in the US, increasingly as the nature of the ozone problem changes
In the Intermountain West, TEMPO could fill the gaps from a sparse surface network
Exploiting transport error correlation with other species through multi-species data assimilation does not seem useful for ozone
TEMPO provides a unique resource for attributing ozone air quality standard exceedances to background/stratospheric influences and thus diagnose “exceptional events”
Recommendation
It is time to start building the TEMPO chemical data assimilation system