On the Predictability of Radiation Fog Formation in a Mesoscale Model: A Case Study in Heterogeneous Terrain

• Main Authors: Thierry Bergot, Renaud Lestringant
• Format: Article
• Language: English
• Published: MDPI AG 2019-03-01
• Series: Atmosphere
• Subjects: fog; numerical mesoscale modelling; predictability
• Online Access: https://www.mdpi.com/2073-4433/10/4/165

This study evaluates the predictability of the formation phase of a radiation fog event observed during the night of 31 October 2015 to 01 November 2015 in the north-east of France at three sites managed by OPE (Observatoire Pérenne de l’Environnement). The fog layer shows significantly different behaviors at the three areas, which are located only a few kilometers apart. Three fog life cycles were observed: the formation of a dense adiabatic fog, the formation of a thin patchy fog, or no fog formation despite favorable conditions. This event was studied with the Meso-NH numerical mesoscale model at two horizontal resolutions, 500 m and 50 m. Simulations at 50 m allow estimation of the spread of the predicted parameters over the heterogeneous terrain studied. These numerical simulations strongly suggest that this event involved numerous interactions and complex circulations. The wind above the nocturnal boundary layer greatly affects the transition of shallow patchy fog into thick adiabatic fog. These numerical simulations also show that the occurrence and type of fog could be very different over a small but heterogeneous area. It is also interesting to note that the spread of the simulated parameters was very high during the transition from shallow fog to a deep fog layer. The spread was concentrated during the regime transition between the fog formation and its maturity. This appeared to be the result of the complex interplay of processes at numerous ranges of scale. A new concept called “pseudo-process diagram„ is presented. These pseudo-process diagrams are very good tools to analyze fog, and allow a good illustration of the spread of fog during this chaotic phase. This kind of concept seems a promising tool to analyze fog predictability in depth.