Four-dimensional data assimilation experiments with International Consortium...

The core information for this publication's citation.: 
Chai, T., G. Carmichael, Y. Tang, A. Sandu, M. Hardesty, P. Pilewskie, S. Whitlow, E. Browell, M. Avery, P. Nedéléc, J. T. Merrill, A. M. Thompson, and E. J. Williams (2007), Four-dimensional data assimilation experiments with International Consortium for Atmospheric Research on Transport and Transformation ozone measurements, J. Geophys. Res., 112, D12S15, doi:10.1029/2006JD007763.
Abstract: 

Ozone measurements by various platforms during the International Consortium for Atmospheric Research on Transport and Transformation (ICARTT) operations in the summer of 2004 are assimilated into the STEM regional chemical transport model (CTM). Under the four-dimensional variational data assimilation (4D-Var) framework, the model forecast (background) error covariance matrix is constructed using both the so-called NMC (National Meteorological Center, now National Centers for Environmental Prediction) method and the observational (Hollingworth-Lönnberg) method. The inversion of the covariance matrix is implemented using truncated singular value decomposition (TSVD) approach. The TSVD approach is numerically stable even with severely ill conditioned vertical correlation covariance matrix and large horizontal correlation distances. Ozone observations by different platforms (aircraft, surface, and ozonesondes) are first assimilated separately. The impacts of the various measurements are evaluated on their ability to improve the predictions, defined as the information content of the observations under the current framework. In the end, all observations are assimilated into the CTM. The final analysis matches well with observations from all platforms. Assessed with all the observations throughout the boundary layer and midtroposphere, the model bias is reduced from 11.3 ppbv for the base case to -1.5 ppbv. A reduction of 10.3 ppbv in root mean square error is also seen. In addition, the potential of improving air quality forecasts by chemical data assimilation is demonstrated. The effect of assimilating ozone observations on model predictions of other species is also shown.

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Research Program: 
Tropospheric Composition Program (TCP)
Mission: 
INTEX-NA