Publikationen
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Egli, S.; Thies, B. & Bendix, J. (2018): A Hybrid Approach for Fog Retrieval Based on a Combination of Satellite and Ground Truth Data. Remote Sensing 10(4), 1-26.
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DOI: 10.3390/rs10040628
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Abstract:
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.
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Keywords: |
Fog detection |
fog |
ground fog |
retrieval of fog |
satellite climatology of fog |
ground fog detection |
fog remote sensing |
ground fog frequency |
fog monitoring |
Schulz, M.; Li, C.; Thies, B.; Chang, S. & Bendix, J. (2017): Mapping the montane cloud forest of Taiwan using 12 year MODIS-derived ground fog frequency data. PLOS ONE 12(2), 1-17.
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DOI: 10.1371/journal.pone.0172663
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Abstract:
Abstract:
Up until now montane cloud forest (MCF) in Taiwan has only been mapped for selected areas of vegetation plots. This paper presents the first comprehensive map of MCF distribution for the entire island. For its creation, a Random Forest model was trained with vegetation plots from the National Vegetation Database of Taiwan that were classified as “MCF” or “non-MCF”. This model predicted the distribution of MCF from a raster data set of parameters derived from a digital elevation model (DEM), Landsat channels and texture measures derived from them as well as ground fog frequency data derived from the Moderate Resolution Imaging Spectroradiometer. While the DEM parameters and Landsat data predicted much of the cloud forest’s location, local deviations in the altitudinal distribution of MCF linked to the monsoonal influence as well as the Massenerhebung effect (causing MCF in atypically low altitudes) were only captured once fog frequency data was included. Therefore, our study suggests that ground fog data are most useful for accurately mapping MCF.
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Keywords: |
Taiwan |
cloud forest |
ground fog |
satellite climatology of fog |
fog remote sensing |
Mountain forest |
vegetation mapping |
vegetation plots |
fog studies |
Random forests |
Vegetation cover |
Schulz, M. (2017): Delineating the montane cloud forest of Taiwan LCRS, phd thesis
Schulz, M.; Thies, B.; Chang, S. & Bendix, J. (2016): Detection of ground fog in mountainous areas from MODIS (Collection 051) daytime data using a statistical approach. Atmospheric Measurement Techniques 9, 1135 - 1152.
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DOI: 10.5194/amt-9-1135-2016
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Abstract:
Abstract:
The mountain cloud forest of Taiwan can be delimited
from other forest types using a map of the ground
fog frequency. In order to create such a frequency map from
remotely sensed data, an algorithm able to detect ground fog
is necessary. Common techniques for ground fog detection
based on weather satellite data cannot be applied to fog occurrences
in Taiwan as they rely on several assumptions regarding
cloud properties. Therefore a new statistical method
for the detection of ground fog in mountainous terrain from
MODIS Collection 051 data is presented. Due to the sharpening
of input data using MODIS bands 1 and 2, the method
provides fog masks in a resolution of 250 m per pixel. The
new technique is based on negative correlations between optical
thickness and terrain height that can be observed if
a cloud that is relatively plane-parallel is truncated by the
terrain. A validation of the new technique using camera data
has shown that the quality of fog detection is comparable to
that of another modern fog detection scheme developed and
validated for the temperate zones. The method is particularly
applicable to optically thinner water clouds. Beyond a cloud
optical thickness of ? 40, classification errors significantly
increase.
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Keywords: |
Fog detection |
Taiwan |
fog |
ground fog |
ground fog detection |
fog remote sensing |
Gultepe, I.; Tardif, R.; Michaelides, S.; Cermak, J.; Bott, A.; Bendix, J.; Müller, M.; Pagowski, M.; Hansen, B.; Ellrod, G.; Jacobs, W.; Toth, G. & Cober, S. (2007): Fog Research: A review of past achievements and future perspectives. Pure and Applied Geophysics 164, 1121-1159.
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DOI: 10.1007/s00024-007-0211-x
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Abstract:
Abstract:
The scientific community that includes meteorologists, physical scientists, engineers,
medical doctors, biologists, and environmentalists has shown interest in a better understanding of fog for
years because of its effects on, directly or indirectly, the daily life of human beings. The total economic
losses associated with the impact of the presence of fog on aviation, marine and land transportation can be
comparable to those of tornadoes or, in some cases, winter storms and hurricanes. The number of articles
including the word ‘‘fog’’ in Journals of American Meteorological Society alone was found to be about
4700, indicating that there is substantial interest in this subject. In spite of this extensive body of work, our
ability to accurately forecast/nowcast fog remains limited due to our incomplete understanding of the fog
processes over various time and space scales. Fog processes involve droplet microphysics, aerosol
chemistry, radiation, turbulence, large/small-scale dynamics, and surface conditions (e.g., partaining to the
presence of ice, snow, liquid, plants, and various types of soil). This review paper summarizes past
achievements related to the understanding of fog formation, development and decay, and in this respect,
the analysis of observations and the development of forecasting models and remote sensing methods are
discussed in detail. Finally, future perspectives for fog-related research are highlighted.
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Keywords: |
Fog review |
fog observations |
fog modeling |
fog remote sensing |
fog forecasting |