Combing MSG thermal channels with meteorological data for fog forecasting and mapping over desert areas in the UAE

Satellite remote sensing is an important tool in the detection and short range forecasting (nowcasting) of fog events. Fog over land develops primarily during the late-night and pre-dawn hours, infrared remote sensing is indispensable in observing fog formation at night, while visible imagery helps to monitor the extent and density of fog after sunrise. Satellite remote sensing is widely used in the United States and Europe in detecting and nowcasting fog events. The temperature difference between two infrared bands (11 μm and 4 μm), available in geostationary satellites, forms the basis for fog detection and classification. In this study an algorithm for now-casting, detecting and monitoring fog events in the United Arab Emirates was developed. The major approach is based on developing a neural-networks system in MATLAB, mainly using METEOSAT SEVIRI data and local meteorological data. The final developed tool is named UAE-FAD (Fog Automatic Detector) and will provide real-time information for further research and development as well as for public and civil services.