Abstract
Fog events occur at Melbourne Airport, Melbourne, Victoria, Australia, approximately 12 times each year. Unforecast events are costly to the aviation industry, cause disruption, and are a safety risk. Thus, there is a need to improve operational fog forecasting. However, fog events are difficult to forecast because of the complexity of the physical processes and the impact of local geography and weather elements. Bayesian networks (BNs) are a probabilistic reasoning tool widely used for prediction, diagnosis, and risk assessment in a range of application domains. Several BNs for probabilistic weather prediction have been previously reported, but to date none have included an explicit forecast decision component and none have been used for operational weather forecasting. A Bayesian decision network [Bayesian Objective Fog Forecast Information Network (BOFFIN)] has been developed for fog forecasting at Melbourne Airport based on 34 years’ worth of data (1972–2005). Parameters were calibrated to ensure that the network had equivalent or better performance to prior operational forecast methods, which led to its adoption as an operational decision support tool. The current study was undertaken to evaluate the operational use of the network by forecasters over an 8-yr period (2006–13). This evaluation shows significantly improved forecasting accuracy by the forecasters using the network, as compared with previous years. BOFFIN-Melbourne has been accepted by forecasters because of its skill, visualization, and explanation facilities, and because it offers forecasters control over inputs where a predictor is considered unreliable.
Generalisations of a Bayesian decision-theoretic randomisation procedure and the impact of delayed responses
