Machine Learning-Based Retrieval of Cloud and Fog Properties and Climate Feedbacks from Satellite Observations
A Special Issue of Remote Sensing (ISSN 2072-4292) belonging to the section "Atmospheric Remote Sensing".
Deadline for manuscript submissions: 31 October 2026 | Viewed by 262
Editors
Interests: aerosol–cloud interactions; cloud modeling; cloud and fog observations
Special Issues, Collections and Topics in MDPI journals
Interests: convection; atmospheric modeling; precipitation; wrf; atmospheric physics; remote sensing; climate dynamics; climate modeling; meteorology; atmospheric sciences
Special Issue Information
Dear Colleagues,
Cloud processes remain one of the major sources of uncertainty in weather and climate research. Satellite observations provide massive datasets with extensive spatial and temporal coverage, while machine learning has emerged as a fast-growing approach to extract cloud properties and their climate feedback from these data. At the same time, appropriate physical models and fast radiative transfer algorithms for cloudy atmospheres are essential to interpret and constrain machine learning results, ensuring scientifically consistent outcomes. The combination of satellite observations, machine learning, and physical modeling thus provides a valuable opportunity to deepen our understanding of clouds and precipitation.
This Special Issue focuses on research that relates to the micro- and macro-cloud characteristics, cloud radiation forcing, cloud phase, in-cloud processes, and fog detection involving the machine learning method and satellite observations. It is expected that the understanding of cloud and precipitation can be improved, which can help us reduce the uncertainty in weather forecast and climate simulations. Papers involving satellite observation or machine learning methods may address, but are not limited to, the following topics:
- Cloud characteristics based on satellite observation;
- Cloud climate feedback;
- Aerosol–cloud interactions;
- Fog detection and forecast;
- Application of satellite data to model development and evaluation;
- Detection of the cloud macro- and micro-characteristics via satellite data;
- Application of machine learning for fog research.
For this Special Issue, original research articles, reviews, perspectives, and case studies are all welcome.
Dr. Jinghua Chen
Dr. Junjun Li
Prof. Dr. Yuxuan Bian
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 250 words) can be sent to the Editorial Office for assessment.
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-anonymized 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
- machine learning
- cloud feedback
- cloud–aerosol interactions
- fog detection
- cloud properties
- satellite observations
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