Abstract
Fog has a substantial influence on various ecosystems and it impacts economy, traffic systems and human life in many ways. In order to be able to deal with the large number of influence factors, a spatially explicit high-resoluted data set of fog frequency distribution is needed. In this study, a hybrid approach for fog retrieval based on Meteosat Second Generation (MSG) data and ground truth data is presented. The method is based on a random forest (RF) machine learning model that is trained with cloud base altitude (CBA) observations from Meteorological Aviation Routine Weather Reports (METAR) as well as synoptic weather observations (SYNOP). Fog is assumed where the model predicts CBA values below a dynamically derived threshold above the terrain elevation. Cross validation results show good accordance with observation data with a mean absolute error of 298 m in CBA values and an average Heidke Skill Score of 0.58 for fog occurrence. Using this technique, a 10 year fog baseline climatology with a temporal resolution of 15 min was derived for Europe for the period from 2006 to 2015. Spatial and temporal variations in fog frequency are analyzed. Highest average fog occurrences are observed in mountainous regions with maxima in spring and summer. Plains and lowlands show less overall fog occurrence but strong positive anomalies in autumn and winter.
Highlights
Fog influences human life in various ways
Power grid stability can be affected by fog and low stratus (LST) as photovoltaic power generation strongly depends on the cloudiness level
We describe the Machine learning (ML) model generation and the final fog derivation in detail
Summary
Fog influences human life in various ways. Due to visibility reduction it can have a hindering and sometimes even lethal impact on air, sea and road traffic [1]. In order to be able to derive fog information from satellite data Cermak and Bendix [22] developed a microphysics-based approach that was tuned for radiation fog conditions. These CBAs are merged with a digital elevation model (DEM) to derive areas of ground touching clouds (=fog) This method showed satisfying validation results for a selected sample of MSG records in the autumn months of 2005, a general application on a multiannual data set was not performed. Due to the usual temperature decrease after sunset and generally low temperatures during the winter months, condensation on average sets in at lower levels which in turn increases the probability of fog occurrence As this leads to a strong fog increase during night times and in the winter months the usability of the approach at low sun elevations even beyond 0◦ improves the algorithm applicability significantly.
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