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Students of mechanical engineering at the UK University of Surrey use interactive computer-based learning courses. Experiments can be simulated on computer to allow a greater understanding of subjects than traditional lectures allow. Virtual instruments, which the computer simulates, together with signal data, allow students to put into practice the theory learned in lectures.

In this paper, we introduce a methodology for quality assessment of backtracking models. We present results illustrating the level of agreement between the backtracking models, and the accuracy of each model and the ensemble model in resolving the geo-temporal reference of a single point source. Both assessments are based on an ensemble of 12 different Lagrangian particle dispersion modelling (LPDM) systems utilized in receptor oriented (adjoint) mode during an international numerical experiment dedicated to source region estimation. As major result, we can confirm that the findings of Galmarini et al. [2004b. Ensemble prediction forecasting—Part II: application and evaluation. Atmospheric Environment 38, 4619–4632] and Delle Monache and Stull [2003. An ensemble air-quality forecast over Europe during an ozone episode. Atmospheric Environment 37, 3469–3474], regarding the superiority of the ensemble dispersion forecast over a single forecast, do also apply to LPDM when utilized for backtracking purposes, in particular if only vague a priori knowledge of the source time is available. This, however, is a likely situation in the context of the global nuclear monitoring performed by the Provisional Technical Secretariat (PTS) of the Comprehensive Nuclear-Test-Ban Treaty Organization (CTBTO), where quick but reliable source location identification is required. We introduce a simple methodology as a template for a future electronic emergency response system in the field of dispersion modelling. [Copyright &y& Elsevier]

In any crisis, there is a great deal of uncertainty, often geographical uncertainty or, more precisely, spatiotemporal uncertainty. Examples include the spread of contamination from an industrial accident, drifting volcanic ash, and the path of a hurricane. Estimating spatiotemporal probabilities is usually a difficult task, but that is not our primary concern. Rather, we ask how analysts can communicate spatiotemporal uncertainty to those handling the crisis. We comment on the somewhat limited literature on the representation of spatial uncertainty on maps. We note that many cognitive issues arise and that the potential for confusion is high. We note that in the early stages of handling a crisis, the uncertainties involved may be deep, i.e., difficult or impossible to quantify in the time available. In such circumstance, we suggest the idea of presenting multiple scenarios., (© 2017 Society for Risk Analysis.)