Ground fog detection and analysis with Machine Learning (GFog-ML)

• Applicants: Professor Dr. Jörg Bendix; Dr. Boris Thies
• Subject Area: Physical Geography
• Term: from 2015 to 2019
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 270101240

Fog and Low Stratus (LST) are of utmost importance for the radiation balance and thus, the earth's climate system. The lack of knowledge on its spatio-temporal dynamics in combination with the indirect aerosol effect is one main reason for uncertainties in the latest IPCC projections of the Fifth Assessment Report (AR5). At the same time, fog/LST are causing problems for traffic security and air quality which requires reliable and operational nowcasting procedures. Particularly a proper distinction between LST and ground fog would mean a big step forward in operational nowcasting but would also form the basis for long-term monitoring of fog/LST dynamics with the newest generation of operational weather satellite systems. The main aim of the current project is to develop a novel daylight/night technique for the discrimination of LST and ground fog based on the European geostationary Meteosat Second Generation (MSG) system with its SEVIRI / HRV (Spinning Enhanced Visible and Infrared Imager / High Resolution Visible) instrument onboard. The novel technique will be used to generate for the first time a 10 years data set of LST/ground fog (fog hours) and its properties (effective radius, optical depth). The project is based on the readily available processing chain of LST detection, a unique data set for training/validation from the Marburg Ground Truth and Profiling Station, and will make use of the machine learning technique Random Forest (RF) to calculate 3 and 1 km resolved datasets of LST, ground fog (the latter resolution during daylight only) and fog properties. The comprehensive dataset enables for the first time the spatial-explicit mapping of ground fog hours as required by operational meteorology. The climatological analysis of the generated time series will focus on the detection of changes in regional/local ground fog / LST occurrence over the last decade due to autochtonous (land used and aerosol concentration) and changes in allochthonous (atmospheric circulation) formation factors.

• DFG Programme: Research Grants