Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
8.3
4.2
Journals
Remote Sensing
Special Issues
Remote Sensing of Low-Level Liquid Water Clouds and Fog
remotesensing-logo
Submit to Remote Sensing Review for Remote Sensing Propose a Special Issue

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

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 "Atmospheric Remote Sensing".

Deadline for manuscript submissions: closed (31 December 2019) | Viewed by 24006

Special Issue Editor

Prof. Dr. Jan Cermak

E-Mail Website
Guest Editor
Institute of Meteorology and Climate Research, Karlsruhe Institute of Technology (KIT), Hermann-von-Helmholtz-Platz 1, 76344 Eggenstein-Leopoldshafen, Germany
Interests: meteorology; climate; atmospheric physics; air quality
Special Issues, Collections and Topics in MDPI journals

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 submissions that pass pre-check are 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 2700 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

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

Further information on MDPI's Special Issue polices can be found here.

Published Papers (4 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

16 pages, 4668 KiB  
Article
Application of a Nighttime Fog Detection Method Using SEVIRI Over an Arid Environment
by Michael Weston and Marouane Temimi
Remote Sens. 2020, 12(14), 2281; https://doi.org/10.3390/rs12142281 - 15 Jul 2020
Cited by 15 | Viewed by 3280
Abstract
Fog degrades horizontal visibility causing significant adverse impacts on transport systems. The detection of fog from satellite data remains challenging especially in the presence of higher clouds, dust, mist, or unknown underlying soil conditions. Observations from Meteosat second generation Spinning-Enhanced Visible and Infrared [...] Read more.
Fog degrades horizontal visibility causing significant adverse impacts on transport systems. The detection of fog from satellite data remains challenging especially in the presence of higher clouds, dust, mist, or unknown underlying soil conditions. Observations from Meteosat second generation Spinning-Enhanced Visible and Infrared Imager (MSG SEVIRI) over the United Arab Emirates (UAE), an arid area on the Arabian Peninsula, from 2016 to 2018 (two fog seasons) are used in this study. We implement an adaptive threshold-based technique using pseudo-emissivity values to detect nocturnal fog from SEVIRI. The method allows the threshold to vary spatially and temporally. Low clouds are detected with the analysis of the vertical temperature gradient. Fog classification was verified against four stations in the UAE, namely Abu Dhabi, Dubai, Al Ain, and Al Maktoum, where visibility and meteorological observations are available. The probability of detection (POD) (false alarm ratio (FAR)) was 0.81 (0.40), 0.83 (0.50), 0.83 (0.33), and 0.77 (0.44) at Abu Dhabi, Dubai, Al Ain, and Al Maktoum, respectively. In addition, the spatial frequency of fog is presented, which provides new insights into the fog dynamics in the region. Full article
(This article belongs to the Special Issue Remote Sensing of Low-Level Liquid Water Clouds and Fog)
►▼ Show Figures

Figure 1

7 pages, 15454 KiB  
Article
Fog and Low Cloud Frequency and Properties from Active-Sensor Satellite Data
by Jan Cermak
Remote Sens. 2018, 10(8), 1209; https://doi.org/10.3390/rs10081209 - 2 Aug 2018
Cited by 11 | Viewed by 5159
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)
►▼ Show Figures

Figure 1

26 pages, 19866 KiB  
Article
A Hybrid Approach for Fog Retrieval Based on a Combination of Satellite and Ground Truth Data
by Sebastian Egli, Boris Thies and Jörg Bendix
Remote Sens. 2018, 10(4), 628; https://doi.org/10.3390/rs10040628 - 18 Apr 2018
Cited by 26 | Viewed by 7027
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)
►▼ Show Figures

Graphical abstract

20 pages, 39621 KiB  
Article
Fog Detection Based on Meteosat Second Generation-Spinning Enhanced Visible and InfraRed Imager High Resolution Visible Channel
by Saverio Teodosio Nilo, Filomena Romano, Jan Cermak, Domenico Cimini, Elisabetta Ricciardelli, Angela Cersosimo, Francesco Di Paola, Donatello Gallucci, Sabrina Gentile, Edoardo Geraldi, Salvatore Larosa, Ermann Ripepi and Mariassunta Viggiano
Remote Sens. 2018, 10(4), 541; https://doi.org/10.3390/rs10040541 - 1 Apr 2018
Cited by 15 | Viewed by 7476
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)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-4
Back to TopTop