VARIATIONAL DATA ASSIMILATION FOR TROPOSPHERIC CHEMISTRY MODELING

The method of variational adjoint data assimilation has been applied t o assimilate chemistry observations into a comprehensive tropospheric gas phase model. The rationale of this method is to find the correct i nitial values for a subsequent atmospheric chemistry model run when ob servations scattered in time are available. The variational adjoint te chnique is esteemed to be a promising tool for future advanced meteoro logical forecasting. The stimulating experience gained with the applic ation of four-dimensional variational data assimilation in this resear ch area has motivated the attempt to apply the technique to air qualit y modeling and analysis of the chemical state of the atmosphere. The p resent study describes the development and application of the adjoint of the second-generation regional acid deposition model gas phase mech anism, which is used in the European air pollution dispersion model sy stem. Performance results of the assimilation scheme using both model- generated data and real observations are presented for tropospheric co nditions. In the former case it is demonstrated that time series of on ly few or even one measured key species convey sufficient information to improve considerably the analysis of unobserved species which are d irectly coupled with the observed species. In the latter case a Lagran gian approach is adopted where trajectory calculations between two com prehensively furnished measurement sites are carried out. The method a llows us to analyze initial data for air pollution modeling even when only sparse observations are available. Besides remarkable improvement s of the model performance by properly analyzed initial concentrations it is shown that the adjoint algorithm offers the feasibility to esti mate the sensitivity of ozone concentrations relative to its precursor s.