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Remote Sensing
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Satellite and Airborne Remote Sensing of Cloud Microphysical Properties
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Satellite and Airborne Remote Sensing of Cloud Microphysical Properties

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 2021) | Viewed by 5087

Special Issue Editor

Dr. Bastiaan Van Diedenhoven

E-Mail Website
Guest Editor
SRON Netherlands Institute for Space Research, Sorbonnelaan 2, 3584 CA Utrecht, The Netherlands
Interests: atmosphere; clouds; aerosol; optical properties.; remote sensing

Special Issue Information

Dear Colleagues,

Microphysical properties of cloud droplets and ice crystals, such as their size distributions, concentration, shape and thermodynamic phase, largely determine cloud evolution and radiative properties. Uncertainties related to processes that govern the variation in cloud microphysical properties are at the heart of our inability to sufficiently constrain cloud climate feedbacks and aerosol-cloud interactions, which remain the largest sources of uncertainties in current climate projections. Reducing biases and uncertainties in remote sensing of cloud microphysical properties is key to further enhance our knowledge of fundamental cloud processes. Satellite observations are crucial for studying cloud systems globally, while airborne remote sensing generally provides high spatial resolution observations and an opportunity to test and validate new instruments and techniques. This special issue calls for contributions that focus on the use, development and evaluation of new satellite and airborne active and passive remote sensing instruments and techniques to infer microphysical properties of clouds. Analyses using remote sensing results to study cloud processes on scales ranging from global to regional, as well as quantitative evaluations of novel methods using simulated and/or real measurements are invited.  

Dr. Bastiaan Van Diedenhoven
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

  • clouds
  • microphysics
  • remote sensing
  • radiation
  • climate

Published Papers (2 papers)

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Research

21 pages, 6285 KiB  
Article
Time Evolution of Storms Producing Terrestrial Gamma-Ray Flashes Using ERA5 Reanalysis Data, GPS, Lightning and Geostationary Satellite Observations
by Alessandra Tiberia, Alessandra Mascitelli, Leo Pio D’Adderio, Stefano Federico, Martino Marisaldi, Federico Porcù, Eugenio Realini, Andrea Gatti, Alessandro Ursi, Fabio Fuschino, Marco Tavani and Stefano Dietrich
Remote Sens. 2021, 13(4), 784; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13040784 - 20 Feb 2021
Cited by 7 | Viewed by 2354
Abstract
In this article, we report the first investigation over time of the atmospheric conditions around terrestrial gamma-ray flash (TGF) occurrences, using GPS sensors in combination with geostationary satellite observations and ERA5 reanalysis data. The goal is to understand which characteristics are favorable to [...] Read more.
In this article, we report the first investigation over time of the atmospheric conditions around terrestrial gamma-ray flash (TGF) occurrences, using GPS sensors in combination with geostationary satellite observations and ERA5 reanalysis data. The goal is to understand which characteristics are favorable to the development of these events and to investigate if any precursor signals can be expected. A total of 9 TGFs, occurring at a distance lower than 45 km from a GPS sensor, were analyzed and two of them are shown here as an example analysis. Moreover, the lightning activity, collected by the World Wide Lightning Location Network (WWLLN), was used in order to identify any links and correlations with TGF occurrence and precipitable water vapor (PWV) trends. The combined use of GPS and the stroke rate trends identified, for all cases, a recurring pattern in which an increase in PWV is observed on a timescale of about two hours before the TGF occurrence that can be placed within the lightning peak. The temporal relation between the PWV trend and TGF occurrence is strictly related to the position of GPS sensors in relation to TGF coordinates. The life cycle of these storms observed by geostationary sensors described TGF-producing clouds as intense with a wide range of extensions and, in all cases, the TGF is located at the edge of the convective cell. Furthermore, the satellite data provide an added value in associating the GPS water vapor trend to the convective cell generating the TGF. The investigation with ERA5 reanalysis data showed that TGFs mainly occur in convective environments with unexceptional values with respect to the monthly average value of parameters measured at the same location. Moreover, the analysis showed the strong potential of the use of GPS data for the troposphere characterization in areas with complex territorial morphologies. This study provides indications on the dynamics of con-vective systems linked to TGFs and will certainly help refine our understanding of their production, as well as highlighting a potential approach through the use of GPS data to explore the lightning activity trend and TGF occurrences. Full article
(This article belongs to the Special Issue Satellite and Airborne Remote Sensing of Cloud Microphysical Properties)
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21 pages, 7593 KiB  
Article
Differences in Cloud Radar Phase and Power in Co- and Cross-Channel—Indicator of Lightning
by Zbyněk Sokol and Jana Popová
Remote Sens. 2021, 13(3), 503; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13030503 - 31 Jan 2021
Cited by 4 | Viewed by 1928
Abstract
Thunderstorms and especially induced lightning discharges have still not been fully understood, although they are known to cause many casualties yearly worldwide. This study aims at filling the gap of knowledge by investigating the potential of phase and power of the co- and [...] Read more.
Thunderstorms and especially induced lightning discharges have still not been fully understood, although they are known to cause many casualties yearly worldwide. This study aims at filling the gap of knowledge by investigating the potential of phase and power of the co- and cross-channels of a vertical cloud radar to indicate lightning close to the radar site. We performed statistical and correlation analyses of vertical profiles of phase and power spectra in the co- and the cross-channel for 38 days of thunderstorms producing lightning up to 20 km from the radar in 2018–2019. Specifically, we divided the dataset into “near” and “far” data according to the observed distance of lightning to the radar and analyzed it separately. Although the results are quite initial given the limited number of “near” data, they clearly showed different structures of “near” and “far” data, thus confirming the potential of radar data to indicate lightning. Moreover, for the first time in this study the predictability of lightning using cloud radar quantities was evaluated. We applied a Regression Tree Model to diagnose lightning and verified it using Receiver Operating Characteristic (ROC) and Critical Success Index (CSI). ROC provided surprisingly good results, while CSI was not that good but considering the very rare nature of lightning its values are high as well. Full article
(This article belongs to the Special Issue Satellite and Airborne Remote Sensing of Cloud Microphysical Properties)
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