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Atmosphere
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Interactions Among Aerosols, Clouds, and Precipitation, as Well as Their...
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Interactions Among Aerosols, Clouds, and Precipitation, as Well as Their Impact on Climate Systems

A special issue of Atmosphere (ISSN 2073-4433).

Deadline for manuscript submissions: closed (30 November 2025) | Viewed by 1497

Special Issue Editors

Prof. Dr. Christos Zerefos

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Guest Editor
Research Center for Atmospheric Physics and Climatology, Academy of Athens, GR-10680 Athens, Greece
Interests: air pollution; climate change; stratospheric ozone; extreme weather events
Special Issues, Collections and Topics in MDPI journals
Dr. Stavros Solomos

E-Mail Website
Guest Editor
Research Centre for Atmospheric Physics and Climatology, Academy of Athens, 10680 Athens, Greece
Interests: atmospheric physics; atmospheric modeling; aerosol science; energy; climate change; severe atmospheric phenomena
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Aerosols, originating from both natural and human sources, contribute to complex interactions within atmospheric processes. These include impacts on solar radiation transfer, cloud formation, precipitation, and more. The impact of aerosols on clouds and precipitation is related to their physicochemical properties and their efficiency to act as cloud condensation nuclei (CCNs) and ice nuclei (INs) for the formation of clouds. Other indirect impacts of aerosols on climate processes are related to their depositions, such as mineral dust deposits on snow, which affect surface albedo. Understanding the environmental interactions between aerosols, clouds, and radiation is essential for improving our knowledge on climate change processes, energy research, air quality, climate change, and extreme weather events. We invite studies focused on aerosols, clouds, radiation, and precipitation that examine their interactions through various approaches, including modeling, direct measurement, satellite observation, and integrated methodologies.

Prof. Dr. Christos Zerefos
Dr. Stavros Solomos
Guest Editors

Manuscript Submission Information

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Keywords

  • natural and anthropogenic aerosol sources
  • remote sensing of aerosols and their interactions in the atmosphere
  • station measurements and trends in aerosol quantity, type, and compositiom, as well as their impact on solar radiation and clouds
  • impact of aerosols on cloud condensation nuclei and ice nuclei
  • variations in aerosol and cloud properties during extreme weather events

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Published Papers (1 paper)

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Research

17 pages, 1397 KB  
Article
A Novel Approach for Reliable Classification of Marine Low Cloud Morphologies with Vision–Language Models
by Ehsan Erfani and Farnaz Hosseinpour
Atmosphere 2025, 16(11), 1252; https://doi.org/10.3390/atmos16111252 - 31 Oct 2025
Viewed by 1128
Abstract
Marine low clouds have a strong impact on Earth’s system but remain a major source of uncertainty in anthropogenic radiative forcing simulated by general circulation models. This uncertainty arises from incomplete understanding of the many processes controlling their evolution and interactions. A key [...] Read more.
Marine low clouds have a strong impact on Earth’s system but remain a major source of uncertainty in anthropogenic radiative forcing simulated by general circulation models. This uncertainty arises from incomplete understanding of the many processes controlling their evolution and interactions. A key feature of these clouds is their diverse mesoscale morphologies, which are closely tied to their microphysical and radiative properties but remain difficult to characterize with satellite retrievals and numerical models. Here, we develop and apply a vision–language model (VLM) to classify marine low cloud morphologies using two independent datasets based on Moderate Resolution Imaging Spectroradiometer (MODIS) satellite imagery: (1) mesoscale cellular convection types of sugar, gravel, fish, and flower (SGFF; 8800 total samples) and (2) marine stratocumulus (Sc) types of stratus, closed cells, open cells, and other cells (260 total samples). By conditioning frozen image encoders on descriptive prompts, the VLM leverages multimodal priors learned from large-scale image–text training, making it less sensitive to limited sample size. Results show that the k-fold cross-validation of VLM achieves an overall accuracy of 0.84 for SGFF, comparable to prior deep learning benchmarks for the same cloud types, and retains robust performance under the reduction in SGFF training size. For the Sc dataset, the VLM attains 0.86 accuracy, whereas the image-only model is unreliable under such a limited training set. These findings highlight the potential of VLMs as efficient and accurate tools for cloud classification under very low samples, offering new opportunities for satellite remote sensing and climate model evaluation. Full article
(This article belongs to the Special Issue Interactions Among Aerosols, Clouds, and Precipitation, as Well as Their Impact on Climate Systems)
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