Abstract :
This paper makes the case for computing model performance statistics that can be used explicitly in characterizing the uncertainty in model estimates. Using a framework that relates model estimates to corresponding observations, we show that the relevant statistics are the geometric mean, mg, and the geometric standard deviation, sg, of the ratio of the observed to the model estimates of the variable of interest. The second part of the paper describes a graphical representation of the relationship between model estimate and observation that is used to derive model performance statistics. This diagram builds upon that proposed by Taylor [A., 2001. Summarizing multiple aspects of model performance in a single diagram. Journal of Geophysical Research, 106, 7183–7192] but represents the error in terms of two components: one that is correlated to the model estimate and can be thus reduced in principle through model improvement, and a component that can be reduced only by expanding the model input set.
