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Integrated water vapour as a diagnostic tool for fog detection and classification
CONFLICT OF INTEREST STATEMENT The authors declare no conflicts of interest. DISCLOSURE The authors have nothing to report. DATA AVAILABILITY STATEMENT The data that support the findings of this study are available on request from the corresponding author. The data are not publicly available owing to privacy or ethical restrictions. REFERENCES , , , , , et al. (2025) Study of fog dissipation in an internal boundary layer on Sable Island.
Fog Decision Support Systems: A Review of the Current Perspectives
1. Introduction 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].
Machine Learning for Fog-and-Low-Stratus Nowcasting from Meteosat SEVIRI Satellite Images
Abstract : Fog and low stratus (FLS) are meteorological phenomena that have a significant impact on all ways of transportation and public safety. Due to their similarity, they are often grouped together as a single category when viewed from a satellite perspective. The early detection of these phenomena is crucial to reduce the negative effects that they can cause.
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