Fog Decision Support Systems: A Review of the Current Perspectives

Bari, Driss ; Bergot, Thierry ; Tardif, Robert

Année de publication
<p align=justify>Accurate and timely fog forecasts are needed to support decision making for various activities which are critically affected by low visibility conditions. The societal impact of fog has significantly increased in recent decades, due to increasing air, marine, and road traffic, as well as the emergence of solar power as a source of renewable energy. In fact, the financial costs related to fog have become comparable to the losses from other weather events, such as storms [1]. Low visibility levels in fog lead to delays in air travel, hazardous navigation in crowded waterways and ports, and unsafe traffic conditions on roadways. More recently, information on fog is required for the applications of solar energy production and autonomous driving. Therefore, improved decision support systems tailored to a wide range of activities that are impacted by fog are needed more than ever. At the core of such systems, improved nowcasting (minutes to hours) and forecasting (hours to days) techniques for fog onset, severity, and dissipation are necessary. Further refinement of numerical weather prediction (NWP) models, new observation platforms and observational networks, and advanced analysis capabilities offered by artificial intelligence and machine learning algorithms all represent potential sources of improvement for next-generation fog predictions. Each of these approaches offer possibilities, but they also have their own limitations in providing forecasts with added value to decision makers. One aspect representing a significant challenge and requiring further attention is the capability of providing clear and reliable information on forecast uncertainty. Several aspects of these capabilities and challenges are discussed in this review. This Special Issue, in particular, provides an overview of recent advances in the development of decision support systems, and their related components, for fog nowcasting and forecasting. The contributions highlight the use of different approaches (e.g., data-driven techniques, NWP models and ensemble forecasting systems, artificial intelligence and machine learning algorithms), either used individually or in combination (i.e., blending information from various sources), for generating improved fog predictions. We would like to thank all of the authors who contributed to this Special Issue for their hard work in creating the material contained within, as well as for considering the revisions based on the reviewers' comments. We also thank the reviewers for their constructive comments and suggestions. All of these contributions serve as key elements in this review to provide a fresh perspective on the state of the art of fog decision support systems and the remaining challenges to the production of useful fog predictions.</p>
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