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Open AccessArticle

A Hybrid Approach for Fog Retrieval Based on a Combination of Satellite and Ground Truth Data

Faculty of Geography, Philipps-University of Marburg, 35032 Marburg, Germany
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Remote Sens. 2018, 10(4), 628; https://doi.org/10.3390/rs10040628
Received: 9 February 2018 / Revised: 26 March 2018 / Accepted: 16 April 2018 / Published: 18 April 2018
(This article belongs to the Special Issue Remote Sensing of Low-Level Liquid Water Clouds and Fog)
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. View Full-Text
Keywords: fog detection; climatology; remote sensing; Meteosat Second Generation; machine learning fog detection; climatology; remote sensing; Meteosat Second Generation; machine learning
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  • Externally hosted supplementary file 1
    Doi: 10.5678/LCRS/DAT.311
    Description: Fog frequencies in Europe (monthly means)
MDPI and ACS Style

Egli, S.; Thies, B.; Bendix, J. A Hybrid Approach for Fog Retrieval Based on a Combination of Satellite and Ground Truth Data. Remote Sens. 2018, 10, 628. https://doi.org/10.3390/rs10040628

AMA Style

Egli S, Thies B, Bendix J. A Hybrid Approach for Fog Retrieval Based on a Combination of Satellite and Ground Truth Data. Remote Sensing. 2018; 10(4):628. https://doi.org/10.3390/rs10040628

Chicago/Turabian Style

Egli, Sebastian; Thies, Boris; Bendix, Jörg. 2018. "A Hybrid Approach for Fog Retrieval Based on a Combination of Satellite and Ground Truth Data" Remote Sens. 10, no. 4: 628. https://doi.org/10.3390/rs10040628

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