Data assimilation: a powerful tool for atmospheric chemistry Jeff Xia

Data assimilation: a powerful Data assimilation: a powerful tool for atmospheric chemistrytool for atmospheric chemistry

Jeff Xia

ReferencesReferences 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.

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, 2000

OUTLINEOUTLINE

IntroductionThe objectiveTwo important roles Two Case studies.Conclusion

IntroductionIntroduction

Model forecast Satellite observation

How to get a complete picture of what’s really

happened?

Data Assimilation can do it!

DefinitionDefinition

Data assimilation is the process of finding the model representation which is most consistent with the observations.

---- Andrew Lorenc(1995)

Objective of Chemical Data Objective of Chemical Data AssimilationAssimilation

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.

Two Important RolesTwo Important Roles

1. Obtain an initial condition as accurately as possible the real atmosphere state is for the Model Forecasting use.

--- Sequential assimilation

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

ModelModel3-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.

MLS ObservationMLS ObservationOn 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.

Case Study ICase 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.

Case Study IICase 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.

ConclusionConclusion

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.