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The table below summarises some difficulties in model evaluation.

Why is model evaluation difficult?
Difficulty Reaction Implied problem
The appropriate evaluation method depends on the context of the application and the data sets available An array of various evaluation methods must be developed. What weight should be ascribed to various performance measures?
Input data sets are limited. They reflect only few of the possible scenarios. a) Extrapolate model behaviour outside of validation domain a) Does the model give the right result for the right reason?
We must understand model behaviour!
b) Use many data sets b) Hard work!
Ambiguous results.
Processing of input data for model evaluation is far from trivial. Take care!
Identify pitfalls.
Use quality indicators.
Numerous problems!
The luxury of independent data sets can rarely be afforded. Use many data sets. Hard work!
Ambiguous results.
There are inherent uncertainties. Use Venkatram's conceptual framework. Ensembles are difficult to establish.

This question was asked on the wiki: Atmospheric Dispersion Modelling.

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