1. Data assimilation: a powerful tool for atmospheric chemistry Jeff Xia

2. References David J.Lary,Data assimilation:a powerfull tool for atmospheric chemistry, Phil.Trans. R.Soc.Lond A357,3445-3457,1999 Levelt, P. F., B. V. Khattatov, J. C. Gille, G. P. Brasseur, X. X. Tie, and J. Waters, Assimilation of MLS ozone measurements in the global three-dimensional chemistry- transport model ROSE, Geophys. Res. Lett., 25, 4493- 4496, 1998.Assimilation of MLS ozone measurements in the global three-dimensional chemistry- transport model ROSE B. V. Khattatov, J.-F. Lamarque, L. V. Lyjak, R. Menard, P. F. Levelt, X. X. Tie, J. C. Gille, G. P. Brasseur, Assimilation of satellite observations of long- lived chemical species in global chemistry-transport models, J. Geophys. Res., 105, 29135, 2000Assimilation of satellite observations of long- lived chemical species in global chemistry-transport models

3. OUTLINE Introduction The objective Two important roles Two Case studies. Conclusion

4. Introduction Model forecast Satellite observation

5. How to get a complete picture of what’s really happened?

6. Data Assimilation can do it!

7. Definition Data assimilation is the process of finding the model representation which is most consistent with the observations. ---- Andrew Lorenc(1995)

8. Objective of Chemical Data Assimilation Produce a comprehensive self-consistent, synoptic analysis of the chemical state of our atmosphere. With the analysis to examine the chemical mechanisms involved in the atmosphere.

9. Two Important Roles 1.Obtain an initial condition as accurately as possible the real atmosphere state is for the Model Forecasting use. --- Sequential assimilation

10. 2. Obtain the real analysis dataset by assimilating past & future observation to the model. ---- Retrospective Assimilation

11. Model 3-D chemistry transport model Research for Ozone in Stratosphere and its Evolution (ROSE) Resolution: 5°(lat),11.25°(lon),19 layers(316 mbar-0.316 mbar) 50 species, associated with an extensive set of photochemical scheme as well as heterogeneous process.

12. MLS Observation On board UARS (Upper Atmosphere Research Satellite) observes the microwave atmospheric limb emissions on a global scale during day and night. It measures profiles of several trace gases including O3 with vertical resolution of about 6 km.

13. Case Study I Sequential assimilation( Levelt et al.1998) 1. Run ROSE for November and December of 1992 and assimilated all MLS ozone observations available for that period.Then get the assimilated initial conditions input for ROSE to Run the whole year of 1993. 2. Run ROSE without assimilation.

15. Case Study II Retrospective Assimilation ( Khattatov et al.2000) 1. Run the ROSE with assimilating 1993 whole year data from MLS to get the analysis datasets of this year. 2. Compare the results with HALOE data which is independent of MLS mesearment.

18. Conclusion Data Assimilation has done a very good job in generating the “real” initial condition for model running and in setting up the reliable analysis dataset. With the development of technology,data assimilation has become a powerful tool for our research.