i'm calculating the Geometric Mean Bias (MG) for the results of an air quality dispersion model against observations with the following equation from Chang and Hanna (2004);

$MG = exp(\overline{LnC_o} - \overline{LnC_p})$

A result of 1 indicates a perfect match of data means. My value for 32 observation & prediction pairs comes out at 1.2, fine. But my reading of various papers and this site suggests that values > 1 indicate the model is overestimating.

The thing is, my model is clearly underestimating, all but 4 predictions are below the matching observation. If to have MG > 1 you need $(\overline{LnC_o} - \overline{LnC_p})$ to be > 0 (for the exponential), then surely a model must be underestimating if MG > 1 ? i.e. $\overline{LnC_o} > \overline{LnC_p}$ ? I must be missing something basic.


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