Special Issue "Remote Sensing of Low-Level Liquid Water Clouds and Fog"

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Atmosphere Remote Sensing".

Deadline for manuscript submissions: 31 December 2019.

Special Issue Editor

Prof. Jan Cermak
E-Mail Website
Guest Editor
Karlsruhe Institute of Technology (KIT), 76128 Karlsruhe, Germany
Interests: aerosols; clouds; satellite remote sensing; climatology; satellite climatology; ground-based remote sensing; air quality; aerosol effects on climate; aerosol-cloud interactions

Special Issue Information

Dear Colleagues,

Low-level liquid-water clouds, among them, fog as a special case, have received particular attention in recent research projects. Their role in the climate system, as well as the direct impacts of fog on activities at the Earth surface, make them worthwhile research subjects.

For this Special Issue, we invite contributions documenting recent studies on remote sensing of these clouds. In particular:

  • Satellite-based techniques and evaluation thereof
  • Ground-based remote sensing techniques
  • Application of remote-sensing products for the study of fog and low-level liquid-water cloud dynamics

Prof. Jan Cermak
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Fog
  • Low stratus
  • Satellite-based remote sensing
  • Ground-based remote sensing
  • Active sensors
  • Passive sensors
  • Algorithm development
  • Cloud process studies

Published Papers (3 papers)

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Research

Open AccessArticle
Fog and Low Cloud Frequency and Properties from Active-Sensor Satellite Data
Remote Sens. 2018, 10(8), 1209; https://doi.org/10.3390/rs10081209 - 02 Aug 2018
Cited by 2
Abstract
An analysis of fog and low cloud properties and distribution is performed using satellite-based LiDAR. Recent years have seen great progress in the remote sensing of fog and low clouds using passive satellite-based sensors. On this basis, maps of fog distribution and frequency [...] Read more.
An analysis of fog and low cloud properties and distribution is performed using satellite-based LiDAR. Recent years have seen great progress in the remote sensing of fog and low clouds using passive satellite-based sensors. On this basis, maps of fog distribution and frequency as well as baseline climatologies have been constructed. However, no information on fog altitude and vertical extent is available in this way, and fog/low cloud below other clouds cannot be detected in most cases. In this study, ten years of observations by the LiDAR aboard the CALIPSO (Cloud-Aerosol LiDAR and Pathfinder Satellite Observations) platform are used to construct a map and statistical evaluations of fog/low cloud distribution and properties. For the purpose of evaluation, a comparison is made to an evaluation of fog/low cloud distribution in Europe, derived from Meteosat measurements using the Satellite-Based Operation Fog Observation Scheme (SOFOS). Both maps show good agreement in spatial patterns in this region with very diverse fog formation mechanisms. It is found that fog/low cloud layers display distinct spatial differences in terms of geometrical thickness and detection accuracy. The number of fog/low cloud instances missed by passive-sensor retrievals due to multi-layer cloud situations is considerable, with clear regional differences. Full article
(This article belongs to the Special Issue Remote Sensing of Low-Level Liquid Water Clouds and Fog)
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Open AccessArticle
A Hybrid Approach for Fog Retrieval Based on a Combination of Satellite and Ground Truth Data
Remote Sens. 2018, 10(4), 628; https://doi.org/10.3390/rs10040628 - 18 Apr 2018
Cited by 5
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 [...] Read more.
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. Full article
(This article belongs to the Special Issue Remote Sensing of Low-Level Liquid Water Clouds and Fog)
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Open AccessArticle
Fog Detection Based on Meteosat Second Generation-Spinning Enhanced Visible and InfraRed Imager High Resolution Visible Channel
Remote Sens. 2018, 10(4), 541; https://doi.org/10.3390/rs10040541 - 01 Apr 2018
Cited by 7
Abstract
In this study, the Meteosat Second Generation (MSG)—Spinning Enhanced Visible and Infrared Imager (SEVIRI) High Resolution Visible channel (HRV) is used in synergy with the narrow band MSG-SEVIRI channels for daytime fog detection. A new algorithm, named MSG-SEVIRI SatFog, has been designed and [...] Read more.
In this study, the Meteosat Second Generation (MSG)—Spinning Enhanced Visible and Infrared Imager (SEVIRI) High Resolution Visible channel (HRV) is used in synergy with the narrow band MSG-SEVIRI channels for daytime fog detection. A new algorithm, named MSG-SEVIRI SatFog, has been designed and implemented. MSG-SEVIRI SatFog provides the indication of the presence of fog in near real time and at the high spatial resolution of the HRV channel. The HRV resolution is useful for detecting small scale daytime fog that would be missed in the MSG-SEVIRI low spatial resolution channels. By combining textural, physical and tonal tests, a distinction between fog and low stratus is performed for pixels identified as low/middle clouds or clear by the Classification-MAsk Coupling of Statistical and Physical Methods (C-MACSP) cloud detection algorithm. Suitable thresholds have been determined using a specific dataset covering different geographical areas, seasons and time of the day. MSG-SEVIRI SatFog is evaluated against METeorological Aerodrome Reports (METAR) data observations. Evaluation results in an accuracy of 69.9%, a probability of detection of 68.7%, a false alarm ratio of 31.3% and a probability of false detection of 30.0%. Full article
(This article belongs to the Special Issue Remote Sensing of Low-Level Liquid Water Clouds and Fog)
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