Even though computational fluid-dynamics (CFD) has been used to simulate airflow in cities, accurate results are not always obtained because of difficulties related to defining the correct set of values of parameters, including boundary conditions. This paper uses a multi-model system-identification approach for improving the accuracy of airflow modeling. In this approach, multiple model predictions are generated through varying values of parameters that are not known precisely. Measurements are then carried out in order to falsify predictions that are not consistent with the measurements. A rejection criterion is defined through consideration of measurement and inherent modeling uncertainties. This paper proposes a methodology for evaluating modeling uncertainties within a system-identification framework. The methodology has been tested on an experimental facility of the Future Cities Laboratory, called “BubbleZERO”. It is shown that reliable predictions at locations where there are no sensors can be performed through implementation of the model-falsification approach.