Evaluating the uncertainties of data rendered by computational models

Abstract

Computational models allow calculation of the value of an output quantity from a set of linked input quantities. The value of the output quantity yielded by a model is evidently influenced by errors in the determination of the input quantities. Therefore, the uncertainties of the output data can be expressed in terms of the uncertainties of the input quantities by using a Monte Carlo-based uncertainty propagation technique. As an example, we evaluated the uncertainty of the spectral UV irradiance rendered by a radiative transfer model under cloudless sky conditions. This model allows calculation of the spectrally resolved solar UV irradiance from some set of measured input quantities linked with the concentration of atmospheric constituents, the surface reflectivity as well as the spectral characteristics of the aerosol modulation. Although only a single model was used in this work, the methodology applied to evaluate the uncertainty is general and can be applied to any other computational model.

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