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Environmental model performances need to be assessed using some statisticalparameters, such as mean absolute error (MAE) and root mean square error(RMSE). The advantages and disadvantages of these parameters are still incontroversial. The purpose of this study is to introduce a statisticalparameter, type A uncertainty (UA), into model performance evaluations. Weparticularly focus on the relations between sample sizes and three evaluationparameters, and tested a few ocean color remote sensing algorithms anddatasets. The results indicate that RMSE, MAE and UA all vary with the samplesize n but present different trends. Based on our tested results andtheoretical analysis, we therefore conclude that UA is better than RMSE and MAEto express model uncertainty, because its downward trends indicate that themore samples we take, the less uncertainty we get. RMSE and MAE are goodparameters for assessing model accuracy rather than uncertainty.

Statistical parameters for assessing environmental model performance related to sample size: Case study in ocean color remote sensing