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Remote Sensing
Special Issues
Cloud Remote Sensing: Current Status and Perspective
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Cloud Remote Sensing: Current Status and Perspective

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

Deadline for manuscript submissions: closed (29 March 2024) | Viewed by 2855

Special Issue Editors

Dr. Alexander Kokhanovsky

E-Mail Website
Guest Editor
Max Planck Institute for Chemistry, 55128 Mainz, Germany
Interests: cloud remote sensing; aerosol remote sensing; trace gas remote sensing; snow remote sensing; radiative transfer
Special Issues, Collections and Topics in MDPI journals
Dr. Luca Lelli

E-Mail Website
Guest Editor
1. Remote Sensing Technology Institute, Atmospheric Processors, German Aerospace Centre (DLR), Oberpfaffenhofen, 82234 Wessling, Germany
2. Department of Physics, Institute of Environmental Physics, University Bremen, 28359 Bremen, Germany
Interests: clouds; aerosols; atmospheric composition; radiative transfer; time series analysis; trend detection; climate data records; climate networks
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Daniel Rosenfeld

E-Mail Website
Guest Editor
Program of Atmospheric Sciences, Institute of Earth Sciences, The Hebrew University of Jerusalem, Jerusalem 91904, Israel
Interests: cloud remote sensing; atmospheric radiative transfer; climatic effects of cloudiness
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Clouds are composed of liquid water droplets, ice crystals or a mixture of the two. Clouds with mixtures of ice particles and cloud droplets also occur. Clouds are inherently inhomogeneous media with inhomogeneity both in the vertical and horizontal directions. Therefore, theoretical studies on radiation transport in clouds (e.g., clouds of various shapes) are performed using the 3D radiative transfer theory. Accounting for 3D effects and cloud vertical inhomogeneity is critical in modern cloud remote sensing. In addition, the modelling of light-scattering properties of irregular ice crystals and effects of possible cloud pollution via various impurities (e.g., dust, smoke, volcanic eruptions) is at the frontier of modern cloud research and remote sensing.

Because clouds play an important role in the water cycle, atmospheric radiative transfer, weather prediction and climate change, they have been thoroughly studied using ground-based, shipborne, airborne and satellite instrumentation operating from the optical to thermal and microwave spectral ranges.

This Special Issue is focused on the latest developments in cloud remote sensing. We therefore invite papers on the following areas:

  • Ground-based cloud remote sensing;
  • Satellite cloud remote sensing;
  • Airborne cloud remote sensing;
  • Remote sensing of clouds using optical and thermal infrared techniques;
  • Microwave remote sensing of clouds;
  • Multi-angular cloud polarimetry;
  • Radiative transfer in clouds;
  • Light scattering by ice crystals and mixed-phase clouds;
  • Radiative properties of polluted and mixed phase clouds;
  • Radiative properties of hurricanes.

You may choose our Joint Special Issue in Atmosphere.

Dr. Alexander Kokhanovsky
Dr. Luca Lelli
Prof. Dr. Daniel Rosenfeld
Guest Editors

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
  • hurricanes
  • precipitation
  • cloud pollution
  • remote sensing
  • radiative transfer
  • light scattering
  • atmospheric ice crystals

Published Papers (2 papers)

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31 pages, 10832 KiB  
Article
Multi-LEO Satellite Stereo Winds
by James L. Carr, Dong L. Wu, Mariel D. Friberg and Tyler C. Summers
Remote Sens. 2023, 15(8), 2154; https://doi.org/10.3390/rs15082154 - 19 Apr 2023
Cited by 2 | Viewed by 1259
Abstract
The stereo-winds method follows trackable atmospheric cloud features from multiple viewing perspectives over multiple times, generally involving multiple satellite platforms. Multi-temporal observations provide information about the wind velocity and the observed parallax between viewing perspectives provides information about the height. The stereo-winds method [...] Read more.
The stereo-winds method follows trackable atmospheric cloud features from multiple viewing perspectives over multiple times, generally involving multiple satellite platforms. Multi-temporal observations provide information about the wind velocity and the observed parallax between viewing perspectives provides information about the height. The stereo-winds method requires no prior assumptions about the thermal profile of the atmosphere to assign a wind height, since the height of the tracked feature is directly determined from the viewing geometry. The method is well developed for pairs of Geostationary (GEO) satellites and a GEO paired with a Low Earth Orbiting (LEO) satellite. However, neither GEO-GEO nor GEO-LEO configurations provide coverage of the poles. In this paper, we develop the stereo-winds method for multi-LEO configurations, to extend coverage from pole to pole. The most promising multi-LEO constellation studied consists of Terra/MODIS and Sentinel-3/SLSTR. Stereo-wind products are validated using clear-sky terrain measurements, spaceborne LiDAR, and reanalysis winds for winter and summer over both poles. Applications of multi-LEO polar stereo winds range from polar atmospheric circulation to nighttime cloud identification. Low cloud detection during polar nighttime is extremely challenging for satellite remote sensing. The stereo-winds method can improve polar cloud observations in otherwise challenging conditions. Full article
(This article belongs to the Special Issue Cloud Remote Sensing: Current Status and Perspective)
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15 pages, 12746 KiB  
Technical Note
A Machine Learning-Based Multiple Cloud Vertical Structure Parameter Prediction Algorithm Only Using OCO-2 Oxygen A-Band Measurements
by Yixiao Lei, Siwei Li and Jie Yang
Remote Sens. 2023, 15(12), 3142; https://doi.org/10.3390/rs15123142 - 16 Jun 2023
Viewed by 977
Abstract
Measurements of the global cloud vertical structure (CVS) are critical to better understanding the effects of the CVS on climate. Current CVS algorithms based on OCO-2 have to be combined with cloud top height products from CALIPSO and CloudSat, which are no longer [...] Read more.
Measurements of the global cloud vertical structure (CVS) are critical to better understanding the effects of the CVS on climate. Current CVS algorithms based on OCO-2 have to be combined with cloud top height products from CALIPSO and CloudSat, which are no longer available after these two satellites left A-Train in 2018. In this paper, we derive a machine learning-based algorithm using only OCO-2 oxygen A-band hyperspectral measurements to simultaneously predict the cloud optical depth (COD), cloud top pressure (p_top), and cloud pressure thickness (CPT) of single-layer liquid clouds. For validation of real observations, the root mean square errors (RMSEs) of the COD, p_top, and CPT are 7.31 (versus the MYD06_L2), 35.06 hPa, and 26.66 hPa (versus the 2B-CLDCLASS-LIDAR). The new algorithm can also predict CVS parameters trained with p_tops from CALIPSO/CloudSat or CODs from MODIS. Controlled experiments show that known p_tops are more conducive to CPT prediction than known CODs, and experiments with both known CODs and p_tops obtain the best accuracy of RMSE = 20.82 hPa. Moreover, a comparison with OCO2CLD-LIDAR-AUX products that rely on CALIPSO shows that our CVS predictions only using OCO-2 measurements have better CODs for all clouds, better p_tops for clouds with a p_top < 900 hPa, and better CPTs for clouds with a CPT > 30 hPa. Full article
(This article belongs to the Special Issue Cloud Remote Sensing: Current Status and Perspective)
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