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Assimilation of satellite data in 3-D CTMs using sub-optimal Kalman filter
PAPER WITHDRAWN
Boris V. Khattatov, NCAR, Boulder, CO; and J. F. Lamarque, J. Gille, G. Brasseur, P. Levelt, P. Rasch, and W. Collins
We describe assimilation of global satellite observations of chemically important species in the global, three-dimensional chemistry-transport models. A sequential assimilation approach based on the sub-optimal Kalman filter was developed and implemented for use with general global chemistry-transport models. This method allows fast assimilation and mapping of satellite observations and provides estimates of analysis errors. Additionally, a number of diagnostic parameters are provided that allow assessment of uncertanties of the utilized model, such as biases and model error growth rate. The develped methodology has been used with a number of global CTMs (ROSE, MOZART2, MATCH) and applied to assimilation of various observations from several satellite-based instruments (ozone from UARS, CO from MAPS, CO from MOPITT, CO from IMG, aerosol extinction ratios from AVHRR & PATHFINDER). We present and discuss assimilation results from some of these studies.
Session 1, Modeling and data assimilation
Monday, 10 January 2000, 8:55 AM-12:00 PM
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