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Special Issue "Remote Sensing of Aerosols, Planetary Boundary Layer, and Clouds"

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

Deadline for manuscript submissions: 15 February 2024 | Viewed by 2679

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Special Issue Editors

Dr. Tianning Su

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Guest Editor

Earth System Science Interdisciplinary Center, University of Maryland, College Park, MD, USA
Interests: remote sensing; planetary boundary layer; aerosols; cloud; deep learning
Special Issues, Collections and Topics in MDPI journals

Dr. Changqing Lin

E-Mail Website
Guest Editor

Division of Environment and Sustainability , Hong Kong University of Science and Technology, Hong Kong, China
Interests: remote sensing; aerosols; air pollutions; public health
Special Issues, Collections and Topics in MDPI journals

Dr. Pengguo Zhao
E-Mail Website
Guest Editor

Department of Atmospheric Sciences, Chengdu University of Information Technology, Chengdu, China
Interests: aerosols; clouds; aerosol-cloud interactions; remote sensing

Dr. Jing Wei

E-Mail Website
Guest Editor

Department of Atmospheric and Oceanic Science, Earth System Science Interdisciplinary Center, University of Maryland, College Park, MD 20740, USA
Interests: remote sensing; artificial intelligence; big data; air pollution; aerosol; particulate matter; trace gas; cloud
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In the field of atmospheric research, a comprehensive understanding of aerosols, the planetary boundary layer (PBL), and clouds holds significant importance. Pivotal in their role, remote sensing technologies have enabled an in-depth exploration of these atmospheric constituents, all of which significantly influence weather patterns, air quality, and the Earth's energy equilibrium. Given their intricate interactions within climatic systems, these components demand sustained scrutiny and research. Over the past several decades, the field of remote sensing has seen remarkable evolution, particularly due to advancements in LiDAR and radar technologies, satellite imagery, and ground-based measurements. These innovations have exponentially amplified our capacity to study and understand atmospheric phenomena.

This Special Issue invites cutting-edge research utilizing remote sensing in the study of aerosols, the PBL, and clouds. The goal is to foster dialogue, encourage multidisciplinary approaches, and accelerate the progress achieved in this vital area of atmospheric science. Contributions should leverage and advance remote sensing technologies and methodologies, fostering a deeper understanding of and enabling the prediction of changes in Earth's atmosphere and climate. Potential topics could range from observational, analytical, and modeling facets of aerosols, PBL, and clouds to the refinement of remote sensing methodologies and integration of multi-source data. Other areas of interest include the examination of implications of these atmospheric constituents on climate change, weather forecasting, and air quality. Article themes may include, but are not limited to, the following:

Aerosol properties and distributions;
PBL dynamics and interactions with clouds;
Cloud characteristics and classification;
Advances in remote sensing algorithms for aerosol, PBL, and clouds;
Interactions between aerosols and PBL;
Application of artificial intelligence in atmospheric studies.

Dr. Tianning Su
Dr. Changqing Lin
Dr. Pengguo Zhao
Dr. Jing Wei
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

atmospheric remote sensing
cloud
planetary boundary layer
atmospheric dynamics
aerosol–boundary–layer interactions
lidar and radar technologies
satellite

Published Papers (5 papers)

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17 pages, 15296 KiB

Open AccessArticle

Vertical Profiles of Particle Number Size Distribution and Variation Characteristics at the Eastern Slope of the Tibetan Plateau

and

Remote Sens. 2023, 15(22), 5363; https://doi.org/10.3390/rs15225363 - 15 Nov 2023

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Abstract

An unmanned aerial vehicle (UAV) observation platform obtained the first vertical profiles of particle number size distribution (PNSD) from 7 to 16 July 2022 on the eastern slope of the Tibetan Plateau (ESTP). The results were from two flanks at the Chuni (CN) and Tianquan (TQ) sites, which are alongside a mountain (Mt. Erlang). The observations revealed a significant negative correlation between the planetary boundary layer height (PBLH) and the particle number concentration (PNC), and the correlation coefficient was −0.19. During the morning, the rise in the PBLH at the CN and TQ sites caused decreases of 16.43% and 58.76%, respectively, in the PNC. Three distinct profile characteristics were classified: Type I, the explosive growth of fine particles with a size range of 130–272 nm under conditions of low humidity, strong wind shear, and northerly winds; Type II, the process of particles with a size range of 130–272 nm showing hygroscopic growth into larger particles (e.g., 226–272 nm) under high humidity conditions (RH > 85%), with a maximum vertical change rate of about −1653 # cm⁻³ km⁻¹ for N_130–272 and about 3098 # cm⁻³ km⁻¹ for N_272–570; and Type III, in which during the occurrence of a surface low-pressure center and an 850 hPa low-vortex circulation in the Sichuan Basin, polluting air masses originating from urban agglomeration were transported to the ESTP region, resulting in an observed increase in the PNC below 600 nm. Overall, this study sheds light on the various factors affecting the vertical profiles of PNSD in the ESTP region, including regional transport, meteorological conditions, and particle growth processes, helping us to further understand the various features of the aerosol and atmospheric physical character in this key region. Full article

(This article belongs to the Special Issue Remote Sensing of Aerosols, Planetary Boundary Layer, and Clouds)

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14 pages, 6843 KiB

Open AccessArticle

Analysis of Daytime and Night-Time Aerosol Optical Depth from Solar and Lunar Photometry in Valladolid (Spain)

Celia Herrero del Barrio

Daniel González-Fernández

Abel Calle

Carlos Toledano

Victoria Eugenia Cachorro

and

Ángel Máximo De Frutos Baraja

Remote Sens. 2023, 15(22), 5362; https://doi.org/10.3390/rs15225362 - 15 Nov 2023

Viewed by 315

Abstract

Aerosol optical depth (AOD) at night-time has become a hot topic in recent years due to the development of new instruments recording accurate ground-based lunar irradiance measurements, and the development of calibration methods and extraterrestrial irradiance models adapted to lunar photometry. This study uses all daytime and night-time AOD data available at Valladolid (Spain) from October 2016 to March 2022 in order to analyze its behavior and the added contribution of night data. The annual, monthly and daily AOD evolution is studied comparing daytime and night-time values and checking the correlation between them. For this purpose, the daily averages are computed, showing an annual pattern, with low AOD values throughout the year (mean value of AOD at 440 nm: 0.122), where winter months have the lower and summer the higher values, as observed in previous studies. All these AOD values are modulated by frequent desert dust events over the Iberian Peninsula, with a strong influence on daily and monthly mean values in February and March, where the strongest desert outbreaks occurred. The added new data confirm these results and the good correlation between daytime and night-time data. Also, a complete daily evolution is shown, observing that AOD and Ångström exponent (AE) mean values vary by only ±0.02 in 24 h, with a maximum value at 06:00 UTC and minimum at 18:00 UTC for both parameters. Full article

(This article belongs to the Special Issue Remote Sensing of Aerosols, Planetary Boundary Layer, and Clouds)

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21 pages, 4281 KiB

Open AccessArticle

Insighting Drivers of Population Exposure to Ambient Ozone (O₃) Concentrations across China Using a Spatiotemporal Causal Inference Method

and

Remote Sens. 2023, 15(19), 4871; https://doi.org/10.3390/rs15194871 - 08 Oct 2023

Viewed by 466

Abstract

Ground-level ozone (

O_{3}

) is a well-known atmospheric pollutant aside from particulate matter. China as a global populous country is facing serious surface

O_{3}

pollution. To detect the complex spatiotemporal transformation of the population exposure to ambient

O_{3}

pollution [...] Read more.

Ground-level ozone (

O_{3}

) is a well-known atmospheric pollutant aside from particulate matter. China as a global populous country is facing serious surface

O_{3}

pollution. To detect the complex spatiotemporal transformation of the population exposure to ambient

O_{3}

pollution in China from 2005 to 2019, the Bayesian multi-stage spatiotemporal evolution hierarchy model was employed. To insight the drivers of the population exposure to ambient

O_{3}

pollution in China, a Bayesian spatiotemporal LASSO regression model (BST-LASSO-RM) and a spatiotemporal propensity score matching (STPSM) were firstly applied; then, a spatiotemporal causal inference method integrating the BST-LASSO-RM and STPSM was presented. The results show that the spatial pattern of the annual population-weighted ground-level

O_{3}

(

P W G L O_{3}

) concentrations, representing population exposure to ambient

O_{3}

, in China has transformed since 2014. Most regions (72.2%) experienced a decreasing trend in

P W G L O_{3}

pollution in the early stage, but in the late stage, most areas (79.3%) underwent an increasing trend. Some drivers on

P W G L O_{3}

concentrations have partial spatial spillover effects. The

P W G L O_{3}

concentrations in a region can be driven by this region’s surrounding areas’ economic factors, wind speed, and

P W G L O_{3}

concentrations. The major drivers with six local factors in 2005–2014 changed to five local factors and one spatial adjacent factor in 2015–2019. The driving of the traffic and green factors have no spatial spillover effects. Three traffic factors showed a negative driving effect in the early stage, but only one, bus ridership per capita (BRPC), retains the negative driving effect in the late stage. The factor with the maximum driving contribution is BRPC in the early stage, but

P M_{2.5}

pollution in the late stage, and the corresponding driving contribution is 17.57%. Green area per capita and urban green coverage rates have positive driving effects. The driving effects of the climate factors intensified from the early to the later stage. Full article

(This article belongs to the Special Issue Remote Sensing of Aerosols, Planetary Boundary Layer, and Clouds)

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25 pages, 9245 KiB

Open AccessArticle

Spatiotemporal Variations of Aerosol Optical Depth and the Spatial Heterogeneity Relationship of Potential Factors Based on the Multi-Scale Geographically Weighted Regression Model in Chinese National-Level Urban Agglomerations

and

Remote Sens. 2023, 15(18), 4613; https://doi.org/10.3390/rs15184613 - 20 Sep 2023

Cited by 1 | Viewed by 667

Abstract

Investigating the spatiotemporal variation characteristics of aerosol optical depth (AOD) and its driving factors is essential for assessing atmospheric environmental quality and alleviating air pollution. Based on a 22-year high-resolution AOD dataset, the spatiotemporal variations of AOD in mainland China and ten national urban agglomerations were explored based on the Mann–Kendall trend test and Theil–Sen median method. Random forest (RF) and multiscale geographically weighted regression (MGWR) were combined to identify the main driving factors of AOD in urban agglomerations and to reveal the spatial heterogeneity of influencing factors. The results showed that areas with high annual average AOD concentrations were mainly concentrated in the Chengdu–Chongqing, Central Plains, Shandong Peninsula, and Middle Yangtze River urban agglomerations. Southern Beijing–Tianjin–Hebei and its surrounding areas revealed the highest AOD pollution during summer, whereas the worst pollution during the remaining three seasons occurred in the Chengdu–Chongqing urban agglomeration. Temporally, except for the Ha-Chang and Mid-Southern Liaoning urban agglomerations, where the average annual AOD increased, the other urban agglomerations showed a decreasing trend. Among them, the Central Plains, Middle Yangtze River, Guanzhong Plain, and Yangtze River Delta urban agglomerations all exhibited a decline greater than 20%. According to the spatial trends, most urban agglomerations encompassed much larger areas of decreasing AOD values than areas of increasing AOD values, indicating that the air quality in most areas has recently improved. RF analysis revealed that PM_2.5 was the dominant factor in most urban clusters, followed by meteorological factors. MGWR results show that the influencing factors have different spatial scale effects on AOD in urban agglomerations. The socioeconomic factors and PM_2.5 showed strong spatial non-stationarity with regard to the spatial distribution of AOD. This study can provide a comprehensive understanding of AOD differences among urban agglomerations, and it has important theoretical and practical implications for improving the ecological environment and promoting sustainable development. Full article

(This article belongs to the Special Issue Remote Sensing of Aerosols, Planetary Boundary Layer, and Clouds)

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Other

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13 pages, 3561 KiB

Open AccessTechnical Note

A Satellite Observational Study of Topographical Effects on Daytime Shallow Convective Clouds

and

Remote Sens. 2023, 15(23), 5542; https://doi.org/10.3390/rs15235542 - 28 Nov 2023

Viewed by 414

Abstract

Shallow convective clouds (SCCs) frequently occur over mountainous terrain. However, previous studies have mostly focused on SCCs over flat surfaces. Here, the effects of mountainous terrains on the cloud size distributions (CSDs) and spatial distributions of SCCs are investigated using data obtained from the Landsat-8 satellite. We find that the CSDs are well-described by double power laws separated by scale breaks. The CSDs are controlled by two parameters, i.e., the scale breaks and the number of clouds with sizes between 0.2 and 1 times the scale breaks. We also find that the number of clouds generally increases with the elevation. In particular, the number of clouds larger than the scale breaks increases faster than that of the smaller clouds. The sizes of the larger clouds (the 90th and 95th percentiles) increase with the elevation, while the sizes of the smaller clouds are not sensitive to the elevation. It is suggested that the variations of cloud numbers and sizes with elevation should be used together with the CSDs to describe the cloud fields over mountainous terrains. Full article

(This article belongs to the Special Issue Remote Sensing of Aerosols, Planetary Boundary Layer, and Clouds)

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