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Impact of Atmospheric Aerosols, Clouds, and their Interactions on Radiation...
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Impact of Atmospheric Aerosols, Clouds, and their Interactions on Radiation and Climate

A special issue of Atmosphere (ISSN 2073-4433). This special issue belongs to the section "Aerosols".

Deadline for manuscript submissions: closed (20 February 2025) | Viewed by 1994

Special Issue Editors

Dr. Akriti Masoom

E-Mail Website
Guest Editor
Physikalisch-Meteorologisches Observatorium Davos, World Radiation Center (PMOD/WRC), Davos, Switzerland
Interests: solar radiation; aerosols; clouds; numerical weather prediction; machine learning; trace gases; wildfires; atmospheric remote sensing; satellite observations; atmospheric measurements
Dr. Panagiotis Kosmopoulos

E-Mail Website
Guest Editor
Institute for Environmental Research and Sustainable Development, National Observatory of Athens, 15236 Penteli, Greece
Interests: renewable energy; environmental studies; computer science; earth observations; artificial intelligence; numerical models; smart cities; digital twins
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Aerosols, having both natural and anthropogenic origins, have complex physiochemical properties and undergo transformations leading to complex interactions with atmospheric events, such as the radiative transfer of solar radiation, cloud formation, precipitation, etc., and are associated with extreme weather events such as wildfires, dust storms, volcanic eruptions, etc., which are also susceptible to climate change. Clouds themselves, being the largest contributor to solar energy attenuation, play an important role in radiative and climate forcing. Cloud amount, type, and microphysical characteristics are crucial for atmospheric radiation and climate studies. An improved and adequate understanding of interactions between aerosols, clouds, and radiation is crucial for energy studies, air quality monitoring, and climate change and extreme-weather-events mitigation.

In this Special Issue, we aim to provide a collection of studies focusing on aerosols, clouds, and radiation, and their interactions using varied methodologies such as modelling, measurement, satellite observation, and their synergies. Recent satellite missions such as EarthCare are expected to further enhance the understanding of aerosol–cloud interactions that can be coupled with radiation assessments of these phenomena with an in-depth understanding.

Therefore, all studies related to theoretical, experimental, observational, or modelling representing novel and deeper understandings of aerosol–cloud–radiation interactions, as well as quantitative characterization and radiative impacts of aerosol/cloud type, composition, lifetime, source, and transport are welcome for submission. The proposed topics (though not limited to these) are as follows:

  • Impact of aerosol sources and transport on solar radiation;
  • Aerosol physical and chemical properties impact on solar radiation;
  • Aerosol (amount, type, and composition) trends and associated effect on solar energy;
  • Cloud properties (type, composition, height, etc.) effect on solar radiation;
  • Aerosols’ effect on cloud condensation nuclei, new particle formation, etc., and their combined impact on solar irradiance;
  • Aerosol/cloud physical, chemical, and radiative property variations during extreme weather events (e.g., wildfires, dust storms, volcanic eruptions, cyclones, and cloudburst).

Dr. Akriti Masoom
Dr. Panagiotis Kosmopoulos
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. Atmosphere is an international peer-reviewed open access monthly 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 2400 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

  • aerosol–cloud–radiation interaction
  • aerosol optical depth
  • cloud condensation nuclei
  • new particle formation
  • solar irradiance
  • aerosol transport
  • radiative forcing
  • aerosol chemistry
  • cloud microphysics
  • extreme weather events

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Published Papers (2 papers)

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Research

23 pages, 5658 KiB  
Article
Evaluation of Solar Radiation Prediction Models Using AI: A Performance Comparison in the High-Potential Region of Konya, Türkiye
by Vahdettin Demir
Atmosphere 2025, 16(4), 398; https://doi.org/10.3390/atmos16040398 - 30 Mar 2025
Cited by 1 | Viewed by 1013
Abstract
Solar radiation is one of the most abundant energy sources in the world and is a crucial parameter that must be researched and developed for the sustainable projects of future generations. This study evaluates the performance of different machine learning methods for solar [...] Read more.
Solar radiation is one of the most abundant energy sources in the world and is a crucial parameter that must be researched and developed for the sustainable projects of future generations. This study evaluates the performance of different machine learning methods for solar radiation prediction in Konya, Turkey, a region with high solar energy potential. The analysis is based on hydro-meteorological data collected from NASA/POWER, covering the period from 1 January 1984 to 31 December 2022. The study compares the performance of Long Short-Term Memory (LSTM), Bidirectional LSTM (Bi-LSTM), Gated Recurrent Unit (GRU), Bidirectional GRU (Bi-GRU), LSBoost, XGBoost, Bagging, Random Forest (RF), General Regression Neural Network (GRNN), Support Vector Machines (SVM), and Artificial Neural Networks (MLANN, RBANN). The hydro-meteorological variables used include temperature, relative humidity, precipitation, and wind speed, while the target variable is solar radiation. The dataset was divided into 75% for training and 25% for testing. Performance evaluations were conducted using Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and the coefficient of determination (R2). The results indicate that LSTM and Bi-LSTM models performed best in the test phase, demonstrating the superiority of deep learning-based approaches for solar radiation prediction. Full article
(This article belongs to the Special Issue Impact of Atmospheric Aerosols, Clouds, and their Interactions on Radiation and Climate)
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21 pages, 4929 KiB  
Article
Climatic Background and Prediction of Boreal Winter PM2.5 Concentrations in Hubei Province, China
by Yuanyue Huang, Zijun Tang, Zhengxuan Yuan and Qianqian Zhang
Atmosphere 2025, 16(1), 52; https://doi.org/10.3390/atmos16010052 - 7 Jan 2025
Viewed by 619
Abstract
This study investigates the climatic background of winter PM2.5 (particulate matter with a diameter of 2.5 micrometers or smaller) concentrations in Hubei Province (DJF-HBPMC) and evaluates its predictability. The key findings are as follows: (1) Elevated DJF-HBPMC levels are associated with an upper-tropospheric [...] Read more.
This study investigates the climatic background of winter PM2.5 (particulate matter with a diameter of 2.5 micrometers or smaller) concentrations in Hubei Province (DJF-HBPMC) and evaluates its predictability. The key findings are as follows: (1) Elevated DJF-HBPMC levels are associated with an upper-tropospheric northerly anomaly, a deepened southern branch trough (SBT) that facilitates southwesterly flow into central and eastern China, and a weakened East Asian winter monsoon (EAWM), which reduces the frequency and intensity of cold air intrusions. Near-surface easterlies and an anomalous anticyclonic circulation over Hubei contribute to reduced precipitation, thereby decreasing the dispersion of pollutants and leading to higher PM2.5 concentrations. (2) Significant correlations are observed between DJF-HBPMC and sea surface temperature (SST) anomalies in specific oceanic regions, as well as sea-ice concentration (SIC) anomalies near the Antarctic. For the atmospheric pattern anomalies over Hubei Province, the North Atlantic SST mode (NA) promotes the southward intrusion of northerlies, while the Northwest Pacific (NWP) and South Pacific (SPC) SST modes enhance wet deposition through increased precipitation, showing a negative correlation with DJF-HBPMC. Conversely, the South Atlantic–Southwest Indian Ocean SST mode (SAIO) and the Ross Sea sea-ice mode (ROSIC) contribute to more stable local atmospheric conditions, which reduce pollutant dispersion and increase PM2.5 accumulation, thus exhibiting a positive correlation with DJF-HBPMC. (3) A multiple linear regression (MLR) model, using selected seasonal SST and SIC indices, effectively predicts DJF-HBPMC, showing high correlation coefficients (CORR) and anomaly sign consistency rates (AS) compared to real-time values. (4) In daily HBPMC forecasting, both the Reversed Unrestricted Mixed-Frequency Data Sampling (RU-MIDAS) and Reversed Restricted-MIDAS (RR-MIDAS) models exhibit superior skill using only monthly precipitation, and the RR-MIDAS offers the best balance in prediction accuracy and trend consistency when incorporating monthly precipitation along with monthly SST and SIC indices. Full article
(This article belongs to the Special Issue Impact of Atmospheric Aerosols, Clouds, and their Interactions on Radiation and Climate)
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