Improving the modeling of error variance evolution in the assimilation of chemical species: Application to MOPITT data

Details Improving the modeling of error variance evolution in the assimilation of chemical species: Application to MOPITT data Improving the modeling of error variance evolution in the assimilation of chemical species: Application to MOPITT data

May 2003

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2003georl..30i..23l&link_type=abstract

Geophysical Research Letters, Volume 30, Issue 9, pp. 23-1, CiteID 1470, DOI 10.1029/2003GL016994

Physics

3

Atmospheric Composition And Structure: Constituent Sources And Sinks, Atmospheric Composition And Structure: Troposphere-Constituent Transport And Chemistry, Meteorology And Atmospheric Dynamics: Numerical Modeling And Data Assimilation

Scientific paper

This study focuses on improvement to the modeling of the evolution of the model error variance in the problem of assimilating satellite observations of chemical species. The model error variance evolution equation for the assimilation of CO is described here with localized sources in addition to transport and error growth. The assimilation of carbon monoxide (CO) observations from MOPITT is performed using a sub-optimal Kalman filter in the MOZART-2 chemistry-transport model. It is shown that this new approach can dramatically improve the ability of the assimilation to diverge from erroneous model-generated features.

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