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Improving the modeling of error variance evolution in the assimilation of chemical species: Application to MOPITT data
Author(s) -
Lamarque J.F.,
Gille J. C.
Publication year - 2003
Publication title -
geophysical research letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.007
H-Index - 273
eISSN - 1944-8007
pISSN - 0094-8276
DOI - 10.1029/2003gl016994
Subject(s) - data assimilation , assimilation (phonology) , kalman filter , variance (accounting) , environmental science , atmospheric sciences , ensemble kalman filter , meteorology , mathematics , statistics , geology , extended kalman filter , physics , philosophy , linguistics , accounting , business
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.