The Effect of Forecast Error Accumulation on Four-Dimensional Data Assimilation

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  • 1 National Center for Atmospheric Research, Boulder, Colo. 80302
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Abstract

Updating experiments performed with 5° and 2.5° versions of the NCAR Global Circulation Model are described. Either wind or temperature is updated. Little difference in the asymptotic error of the induced field is found between updating the 5° model with data generated by the 5° model and updating the 2.5° model with data generated by the 2.5° model. This similarity is expected since both models are shown to have similar error growth rates. When the 5° model is updated with data generated by the 2.5° model, the error in the induced field approaches a much larger asymptote. However, this asymptotic error is still less than that of randomly chosen states. The larger asymptotic error is attributed to the rapid error accumulation in a 5° model forecast when compared to data generated by the 2.5° model.

Abstract

Updating experiments performed with 5° and 2.5° versions of the NCAR Global Circulation Model are described. Either wind or temperature is updated. Little difference in the asymptotic error of the induced field is found between updating the 5° model with data generated by the 5° model and updating the 2.5° model with data generated by the 2.5° model. This similarity is expected since both models are shown to have similar error growth rates. When the 5° model is updated with data generated by the 2.5° model, the error in the induced field approaches a much larger asymptote. However, this asymptotic error is still less than that of randomly chosen states. The larger asymptotic error is attributed to the rapid error accumulation in a 5° model forecast when compared to data generated by the 2.5° model.

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