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Estimating Atmospheric Aerosols and Cloud Physics with Optical and Multispectral Sensors

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A Special Issue of Remote Sensing (ISSN 2072-4292) belonging to the section "Atmospheric Remote Sensing".

Deadline for manuscript submissions: closed (15 February 2026) | Viewed by 12442

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Editors

Dr. Yuying Wang

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Guest Editor

School of Atmospheric Physics, Nanjing University of Information Science & Technology, Nanjing 210044, China
Interests: atmospheric aerosols; cloud physics; aerosol–boundary layer interactions; aerosol–cloud–radiation interactions; air pollution

Dr. Haofei Wang

E-Mail Website
Guest Editor

National Satellite Meteorological Center (National Center for Space Weather), China Meteorological Administration, Beijing 100081, China
Interests: cloud property; aerosol property; atomospheric lidar; dust; planetary boundary layer height; multi-angle polarimeter
Special Issues, Collections and Topics in MDPI journals

Dr. Chunsong Lu

E-Mail Website
Guest Editor

Key Laboratory for Aerosol–Cloud Precipitation of China Meteorological Administration/Special Test Field of National Integrated Meteorological Observation, Nanjing University of Information Science & Technology, Nanjing 210044, China
Interests: cloud and precipitation physics; aerosol–cloud interaction; cloud physical parameterization; weather modification

Dr. Xiquan Dong

E-Mail Website
Guest Editor

Department of Hydrology and Atmospheric Sciences, University of Arizona, Tucson, AZ 85721, USA
Interests: atmospheric radiation; clouds; aerosols; precipitation; ground and satellite based sensing techniques; cloud physics and radiation

Special Issue Information

Dear Colleagues,

This Special Issue, “Estimating Atmospheric Aerosols and Cloud Physics with Optical and Multispectral Sensors”, will focus on advancing our understanding of aerosol–cloud interactions using remote sensing technologies. Aerosols and clouds play crucial roles in Earth’s climate system, influencing radiative forcing, precipitation patterns, and weather dynamics. Accurately measuring and characterizing these interactions is essential in improving climate models and weather forecasts.

For this Special Issue, we invite contributions that explore the use of optical and multispectral sensors, such as nephelometers, lidar, radiometers, and satellite-based imaging systems, to estimate the physical and chemical properties of aerosols and clouds. We encourage studies that develop new retrieval algorithms to improve the accuracy of aerosol optical depth (AOD), cloud microphysical properties, cloud condensation nuclei (CCN) concentrations, and so on. The integration of machine learning techniques into sensor data is also a key theme, with the aim of enhancing the interpretation of complex aerosol–cloud systems.

Contributors are encouraged to present case studies that demonstrate the application of remote sensing in different climatic regions, with a focus on high-resolution, multi-source data fusion. Studies that address global aerosol–cloud interaction sensitivity and provide observational evidence from diverse environments are particularly welcome.

The Special Issue seeks to bridge the gap between theoretical modeling and practical observations, providing a comprehensive platform for understanding how aerosols and clouds influence atmospheric processes, including their role in climate change and air quality management.

Dr. Yuying Wang
Dr. Haofei Wang
Dr. Chunsong Lu
Dr. Xiquan Dong
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

aerosol–cloud interactions
remote sensing
multispectral sensors
aerosol optical depth (AOD)
cloud microphysical properties
cloud condensation nuclei (CCN)
lidar
radiometers
satellite
machine learning

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Published Papers (6 papers)

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Research

21 pages, 10897 KB

Open AccessArticle

Vertically Resolved Supercooled Liquid Water over the North China Plain Revealed by Ground-Based Synergetic Measurements

by Yuxiang Lu, Qiang Li, Hongrong Shi, Jiwei Xu, Zhipeng Yang, Yongheng Bi, Xiaoqiong Zhen, Yunjie Xia, Jiujiang Sheng, Ping Tian, Disong Fu, Jinqiang Zhang, Shuzhen Hu, Fa Tao, Jiefan Yang, Xuehua Fan, Hongbin Chen and Xiang’ao Xia

Remote Sens. 2026, 18(1), 160; https://doi.org/10.3390/rs18010160 - 4 Jan 2026

Viewed by 1749

Abstract

Supercooled liquid water (SLW) in mixed-phase clouds significantly influences precipitation efficiency and aviation safety. However, a comprehensive understanding of its vertical structure has been hampered by a lack of sustained, vertically resolved observations over the North China Plain. This study presents the first systematic analysis of SLW vertical distribution and microphysics in this region, utilizing a year-long dataset (2022) from synergistic ground-based instruments in Beijing. Our retrieval approach integrates Ka-band cloud radar, microwave radiometer, ceilometer, and radiosonde data, combining fuzzy-logic phase classification with a liquid water content inversion constrained by column liquid water path. Key findings reveal a distinct bimodal seasonality: SLW primarily occurs at mid-to-upper levels (4–7.5 km) during spring and summer, driven by convective lofting, while winter SLW is confined to lower altitudes (1–2 km) under stable atmospheric conditions. The temperature-dependent occurrence probability of SLW clouds has an annual maximum at −12 °C. The diurnal variation in SLW in summer shows peaks in the afternoon and at night, corresponding to convective cloud activity. Spring, autumn, and winter do not exhibit strong diurnal variations. Retrieved microphysical properties, including liquid water content and droplet effective radius, are consistent with in situ aircraft measurements, validating our methodology. This analysis provides a critical observational benchmark and offers actionable insights for improving cloud microphysics parameterizations in models and optimizing weather modification strategies, such as seeding altitude and timing, in this water-stressed region. Full article

(This article belongs to the Special Issue Estimating Atmospheric Aerosols and Cloud Physics with Optical and Multispectral Sensors)

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25 pages, 7436 KB

Open AccessArticle

How Cloud Feedbacks Modulate the Tibetan Plateau Thermal Forcing: A Lead–Lag Perspective

by Fangling Bao, Husi Letu and Ri Xu

Remote Sens. 2026, 18(1), 122; https://doi.org/10.3390/rs18010122 - 29 Dec 2025

Viewed by 870

Abstract

The thermal forcing of the Tibetan Plateau (TP) significantly influences the Asian summer monsoon. However, its interaction with cloud feedbacks remains unclear due to the limitations of synchronous analysis and traditional cloud classification over the TP. By applying an improved cloud-classification algorithm—which integrates cloud microphysical properties to improve low-cloud detection—to CERES data (2001–2023), we generated a long-term cloud-type dataset. Combined with ERA5 reanalysis data, we systematically analyzed the trends and lead–lag relationships among cloud vertical structure, surface radiation, cloud radiative forcing (CRF), heat fluxes, snowfall, and the TP Monsoon Index (TPMI). Results indicate a vertical cloud redistribution over the TP, with high cloud cover (HCC) decreasing and low cloud cover (LCC) increasing. HCC is strongly synchronized with snowfall and significantly affects surface radiation, while net CRF and sensible heat flux show delayed responses, peaking when HCC leads by about one month. A composite analysis of winter low-HCC events reveals that reduced HCC suppresses snowfall, weakens net CRF, and reduces sensible heat flux after approximately 1–2 months, while the TPMI shows a significant response around month zero. These findings highlight the key role of cloud–radiation–snowfall interactions in modulating TP thermal forcing. Full article

(This article belongs to the Special Issue Estimating Atmospheric Aerosols and Cloud Physics with Optical and Multispectral Sensors)

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25 pages, 5060 KB

Open AccessArticle

A Comparative Analysis of CG Lightning Activities in the Hengduan Mountains and Its Surrounding Areas

by Jingyue Zhao, Yinping Liu, Yuhui Jiang, Yongbo Tan, Zheng Shi, Yang Zhao and Junjian Liu

Remote Sens. 2025, 17(21), 3574; https://doi.org/10.3390/rs17213574 - 29 Oct 2025

Viewed by 1384

Abstract

Based on five years of data (2017–2021) from the China National Lightning Detection Network (CNLDN), this study compares and analyzes the temporal and spatial distribution characteristics of cloud-to-ground (CG) lightning activities in the Hengduan Mountain region and its surroundings. It explores the relationship between CG lightning occurrences and altitude, topography, and various meteorological elements. Our findings reveal a stark east–west divide: high lightning density in the Sichuan Basin and the central Yungui Plateau contrasts sharply with lower densities over the eastern Tibetan Plateau and Hengduan Mountains. This geographical dichotomy extends to the diurnal cycle, where positive cloud-to-ground (PCG) lightning activities are more prevalent in the western part of the study area, while significant nocturnal activity defines the eastern basin and plateau. The study also finds that the relationship between CG lightning activities in the four sub-regions and 2 m temperature, precipitation, convective available potential energy, and Bowen ratio (the ratio of sensible heat flux to latent heat flux) exhibits similarities. Furthermore, we show that the relationship between lightning frequency and altitude is highly region-specific, with each area displaying a unique signature reflecting its underlying topography: a normal distribution over the eastern Tibetan Plateau, a bimodal pattern in the Hengduan Mountains, a sharp low-altitude peak in the Sichuan Basin, and a complex trimodal structure on the Yungui Plateau. These distinct regional patterns highlight the intricate interplay between large-scale circulation, complex terrain, and local meteorology in modulating lightning activity. Full article

(This article belongs to the Special Issue Estimating Atmospheric Aerosols and Cloud Physics with Optical and Multispectral Sensors)

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29 pages, 4947 KB

Open AccessArticle

Nowcasting of Surface Solar Irradiance Based on Cloud Optical Thickness from GOES-16

by Yulu Yi, Zhuowen Zheng, Taotao Lv, Jiaxin Dong, Jie Yang, Zhiyong Lin and Siwei Li

Remote Sens. 2025, 17(16), 2861; https://doi.org/10.3390/rs17162861 - 17 Aug 2025

Cited by 3 | Viewed by 3452

Abstract

Surface solar irradiance (SSI) is a critical factor influencing the power generation capacity of photovoltaic (PV) power plants. Dynamic changes in cloud cover pose significant challenges to the accurate nowcasting of SSI, which in turn directly affects the reliability and stability of renewable energy systems. However, existing research often simplifies or overlooks changes in the optical and morphological characteristics of clouds, leading to considerable errors in SSI nowcasting. To address this limitation and improve the accuracy of ultra-short-term SSI forecasting, this study first forecasts changes in cloud optical thickness (COT) within the next 3 h based on a spatiotemporal long short-term memory model, since COT is the primary factor determining cloud shading effects, and then integrates the zenith and regional averages of COT, along with factors influencing direct solar radiation and scattered radiation, to achieve precise SSI nowcasting. To validate the proposed method, we apply it to the Albuquerque, New Mexico, United States (ABQ) site, where it yielded promising performance, with correlations between predicted and actual surface solar irradiance for the next 1 h, 2 h, and 3 h reaching 0.94, 0.92, and 0.92, respectively. The proposed method effectively captures the temporal trends and spatial patterns of cloud changes, avoiding simplifications of cloud movement trends or interference from non-cloud factors, thus providing a basis for power adjustments in solar power plants. Full article

(This article belongs to the Special Issue Estimating Atmospheric Aerosols and Cloud Physics with Optical and Multispectral Sensors)

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23 pages, 7707 KB

Open AccessArticle

Unraveling Aerosol and Low-Level Cloud Interactions Under Multi-Factor Constraints at the Semi-Arid Climate and Environment Observatory of Lanzhou University

by Qinghao Li, Jinming Ge, Yize Li, Qingyu Mu, Nan Peng, Jing Su, Bo Wang, Chi Zhang and Bochun Liu

Remote Sens. 2025, 17(9), 1533; https://doi.org/10.3390/rs17091533 - 25 Apr 2025

Cited by 2 | Viewed by 1614

Abstract

The response of low-level cloud properties to aerosol loading remains ambiguous, particularly due to the confounding influence of meteorological factors and water vapor availability. We utilize long-term data from Ka-band Zenith Radar, Clouds and the Earth’s Radiant Energy System, Modern-Era Retrospective analysis for Research and Applications Version 2, and European Centre for Medium-Range Weather Forecasts Reanalysis v5 to evaluate aerosol’s effects on low-level clouds under the constrains of meteorological conditions and liquid water path (LWP) over the Semi-Arid Climate and Environment Observatory of Lanzhou University during 2014–2019. To better constrain meteorological variability, we apply Principal Component Analysis to derive the first principal component (PC1), which strongly correlates with cloud properties, thereby enabling more accurate assessment of aerosol–cloud interaction (ACI) under constrained meteorological conditions delineated by PC1. Analysis suggests that under favorable meteorological conditions for low-level cloud formation (low PC1) and moderate LWP levels (25–150 g/m²), ACI is characterized by a significantly negative ACI index, with the cloud effective radius (CER) increasing in response to rising aerosol concentrations. When constrained by both PC1 and LWP, the relationship between CER and the aerosol optical depth shows a distinct bifurcation into positive and negative correlations. Different aerosol types show contrasting effects: dust aerosols increase CER under favorable meteorological conditions, whereas sulfate, organic carbon, and black carbon aerosols consistently decrease it, even under high-LWP conditions. Full article

(This article belongs to the Special Issue Estimating Atmospheric Aerosols and Cloud Physics with Optical and Multispectral Sensors)

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21 pages, 8807 KB

Open AccessArticle

Retrieval of Cloud Optical Thickness During Nighttime from FY-4B AGRI Using a Convolutional Neural Network

by Daozhen Xia, Dongzhi Zhao, Kailin Li, Zhongfeng Qiu, Jiayu Liu, Jiaye Luan, Si Chen, Biao Song, Yu Wang and Jingyuan Yang

Remote Sens. 2025, 17(5), 737; https://doi.org/10.3390/rs17050737 - 20 Feb 2025

Cited by 4 | Viewed by 2121

Abstract

Cloud optical thickness (COT) stands as a critical parameter governing the radiative properties of clouds. This study develops a convolutional neural network (CNN) model to retrieve the COT of single-layer non-precipitating clouds during nighttime using FY-4B satellite data. The model integrates multi-channel brightness, temperature, and geographic and temporal features, without relying on auxiliary meteorological data, using the multi-point averaged 532 nm COT from CALIPSO as ground truth for training. Performance evaluation demonstrates robust retrieval accuracy, achieving coefficients of determination (R²) of 0.88 and 0.73 for satellite zenith angles (SAZAs) < 70° and >70°, respectively. Key advancements include the incorporation of temporal features, the Squeeze-and-Excitation (SE) module, and a multi-point averaging technique, each validated through ablation experiments to reduce bias and enhance stability. Meanwhile, a model error analysis experiment was conducted that further clarified the performance boundaries of the model. These findings underscore the model’s capability to retrieve the COT of single-layer non-precipitating clouds during nighttime with high precision. Full article

(This article belongs to the Special Issue Estimating Atmospheric Aerosols and Cloud Physics with Optical and Multispectral Sensors)

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