Fog is a weather phenomenon that affects all of us, both in the business and private sectors. On the one hand it is vital for entire ecosystems worldwide, on the other hand it can lead to immense economic damage (airport etc.) and even life-threatening situations, for example in road traffic. The positive as well as the negative implications demonstrate the urgency of a good understanding of fog events and their prediction.
Fog forecasting has been a subject of research for many years. However, due to the complex interactions of a wide variety of variables relevant for fog formation, accurate prediction still remains very difficult. One reason for this is that the simulation of parameter interactions which are difficult to capture by modelling is affected by errors due to the complexity of the system. Another reason is the neglected but most likely important combination of temporal and spatial variables, which is currently not taken into account by the models. This is mainly due to the lack of suitable approaches for the combination of temporally high-resolution station measurements of fog-relevant variables and area-wide high-resolution satellite data.