Tools for uncertainty propagation in the Model Web using Monte Carlo simulation

In this paper, we describe an approach for integrating Monte Carlo simulation in the Model Web to propagate uncertainty in model inputs and processes. In our approach, the models and model services are not capable to handle uncertainties. Therefore, we developed separate Web service components that can be used to manage uncertainties in the model workflow using Monte Carlo simulation. This allows flexible application of the developed uncertainty services with existing model services to quantify uncertainties propagated from the model inputs to the outputs. The approach is evaluated in an air quality modelling scenario where AUSTAL2000, a local air quality prediction model, is connected to the Model Web and uncertainty-enabled with the tools presented.