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Atmosphere
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Satellite Remote Sensing Applied in Atmosphere (3rd Edition)
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Satellite Remote Sensing Applied in Atmosphere (3rd Edition)

A special issue of Atmosphere (ISSN 2073-4433). This special issue belongs to the section "Atmospheric Techniques, Instruments, and Modeling".

Deadline for manuscript submissions: 20 October 2025 | Viewed by 1511

Special Issue Editors

Dr. Stavros Kolios

E-Mail Website
Guest Editor
Department of Aerospace Science and Technology, National and Kapodistrian University of Athens, 10679 Athens, Greece
Interests: satellite remote sensing; satellite meteorology; satellite climatology; GIS analysis; atmospheric environment
Special Issues, Collections and Topics in MDPI journals
Dr. Nikos Hatzianastassiou

E-Mail Website
Guest Editor
Laboratory of Meteorology, Physics Department, University of Ioannina, 45110 Ioannina, Greece
Interests: aerosol physical properties; aerosol–cloud interactions; aerosol–radiation interactions; radiation and climate; shortwave and longwave radiation transfer and budgets
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue is a follow-up of the second Special Issue entitled “Satellite Remote Sensing Applied in Atmosphere (2nd Edition)” (https://www.mdpi.com/journal/atmosphere/special_issues/X0M0XHE546), published in Atmosphere in 2024, and will cover all aspects of the applications of satellite remote sensing in atmosphere.

Satellite remote sensing has increasing potential applications in a wide range of atmospheric sciences, thanks to continuous improvements in modern satellite sensors, which provide high-quality data and products and are capable of monitoring even the most remote areas across the world.

The Earth’s atmosphere is where weather and climate are created and evolve, and changes in the atmospheric composition modulate weather phenomena. More specifically, natural and anthropogenic sources of particulates and gases, as well as different cloud types, precipitation patterns and extreme weather events, are of great importance and can be efficiently monitored remotely. Aerosols have catalytic impacts on the solar radiation budget, cloud formation and microphysics, affecting the weather and climate worldwide, and therefore need to be efficiently and accurately monitored from space. The accuracy assessment of any type of satellite data and products, spatiotemporal analyses in different topics of atmospheric sciences and meteorology, relative satellite-based applications and innovative techniques and methods that promote satellite remote sensing in the atmosphere and for weather events are challenging research areas.

This Special Issue welcomes studies that address these topics, based on remotely sensed data and products derived from satellites, and authors are invited to submit and publish their research findings.

Dr. Stavros Kolios
Dr. Nikos Hatzianastassiou
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

  • satellite remote sensing
  • mapping and monitoring atmosphere
  • satellite meteorology
  • satellite climatology
  • remotely sensed data
  • satellite-based applications
  • satellites
  • aerosols
  • extreme weather events
  • storm activity

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Published Papers (3 papers)

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Research

14 pages, 3709 KiB  
Article
Microphysical Characteristics of Summer Precipitation over the Taklamakan Desert Based on GPM-DPR Data from 2014 to 2023
by Wentao Zhang, Guiling Ye, Jeremy Cheuk-Hin Leung and Banglin Zhang
Atmosphere 2025, 16(4), 354; https://doi.org/10.3390/atmos16040354 - 21 Mar 2025
Viewed by 176
Abstract
Precipitation events have been occurring more frequently in the hyper-arid region of the Taklamakan Desert (TD) under recent climate change. However, in this water-limited environment, the microphysical characteristics of precipitation, as well as their link to rainfall intensity, remain unclear. To address this, [...] Read more.
Precipitation events have been occurring more frequently in the hyper-arid region of the Taklamakan Desert (TD) under recent climate change. However, in this water-limited environment, the microphysical characteristics of precipitation, as well as their link to rainfall intensity, remain unclear. To address this, this study utilizes dual-frequency precipitation radar (DPR) data of the Global Precipitation Measurement (GPM) satellite from 2014 to 2023 to analyze the microphysical characteristics of different precipitation types (stratiform and convective) in the TD during the summer. The results show that liquid water path (LWP) is a key factor influencing precipitation type: when LWP is insufficient, stratiform precipitation is more likely to occur (84.1%), while convective precipitation is difficult to occur (15.9%). Microphysical process analysis indicates that in convective precipitation, abundant low-level moisture leads to the growth of liquid particles primarily through the collision–coalescence process (59.7%), resulting in larger raindrop diameters (1.7 mm) and lower concentrations (31.9 mm−1 m−3). In contrast, stratiform precipitation, with limited LWP, primarily involves the melting and breaking-up of high-level ice-phase particles, leading to smaller raindrop diameters (1.2 mm) and higher concentrations (34.3 mm−1 m−3). The warm rain process plays a significant role in raindrop formation in both types of precipitation. The greater (lesser) the amount of LWP, the larger (smaller) the contribution of collision–coalescence (break-up) processes, and the larger (smaller) the raindrop diameter and precipitation intensity. Full article
(This article belongs to the Special Issue Satellite Remote Sensing Applied in Atmosphere (3rd Edition))
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14 pages, 2671 KiB  
Article
Analysis of Cross-Polarization Discrimination Due to Rain for Earth–Space Satellite Links Operating at Millimetre-Wave Frequencies in Pretoria, South Africa
by Yusuf Babatunde Lawal, Pius Adewale Owolawi, Chunling Tu, Etienne Van Wyk and Joseph Sunday Ojo
Atmosphere 2025, 16(3), 256; https://doi.org/10.3390/atmos16030256 - 24 Feb 2025
Viewed by 500
Abstract
This study investigates the impact of rain-induced attenuation on cross-polarization discrimination (XPD) in Earth–space satellite links operating at millimeter-wave frequencies in Pretoria, South Africa. The traditional method of computing XPD employs a constant annual mean rain height and annual mean co-polar attenuation (CPA) [...] Read more.
This study investigates the impact of rain-induced attenuation on cross-polarization discrimination (XPD) in Earth–space satellite links operating at millimeter-wave frequencies in Pretoria, South Africa. The traditional method of computing XPD employs a constant annual mean rain height and annual mean co-polar attenuation (CPA) over a certain location. This research utilized seasonal rain height data obtained from a recent study and the latest ITU-R P.618-14 guidelines, to compute and analyze XPD variations across six selected frequencies (11.7 GHz to 35 GHz) for different percentages of time exceedance in Pretoria. The study reveals significant seasonal dependencies of rain heights, with XPD reaching its maximum during winter due to lower rain height, and lower rain-induced attenuation and its minimum during summer, characterized by intense convective rainfall and maximum rain height. For instance, the estimated XPD for a 35 GHz signal at 0.01% of the time in the summer, spring, winter, and autumn are 13, 14, 15, and 14 dB, respectively. This implies that radio signals suffer severe attenuation caused by low XPD in the summer. The relationship between CPA and XPD highlights the need for increased XPD margins at higher frequencies to mitigate signal degradation caused by rain depolarization. Practical recommendations include the adoption of adaptive modulation and coding schemes to maintain link reliability during adverse weather conditions, particularly in summer. This research highlights the significance of incorporating frequency-dependent parameters and rain height variability in XPD estimation to enhance the design of satellite communication systems, ensuring optimized performance and reliable operation in a tropical climate. Full article
(This article belongs to the Special Issue Satellite Remote Sensing Applied in Atmosphere (3rd Edition))
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21 pages, 6622 KiB  
Article
Random Forest-Based Retrieval of XCO2 Concentration from Satellite-Borne Shortwave Infrared Hyperspectral
by Wenhao Zhang, Zhengyong Wang, Tong Li, Bo Li, Yao Li and Zhihua Han
Atmosphere 2025, 16(3), 238; https://doi.org/10.3390/atmos16030238 - 20 Feb 2025
Viewed by 406
Abstract
As carbon dioxide (CO2) concentrations continue to rise, climate change, characterized by global warming, presents a significant challenge to global sustainable development. Currently, most global shortwave infrared CO2 retrievals rely on fully physical retrieval algorithms, for which complex calculations are [...] Read more.
As carbon dioxide (CO2) concentrations continue to rise, climate change, characterized by global warming, presents a significant challenge to global sustainable development. Currently, most global shortwave infrared CO2 retrievals rely on fully physical retrieval algorithms, for which complex calculations are necessary. This paper proposes a method to predict the concentration of column-averaged CO2 (XCO2) from shortwave infrared hyperspectral satellite data, using machine learning to avoid the iterative computations of the physical method. The training dataset is constructed using the Orbiting Carbon Observatory-2 (OCO-2) spectral data, XCO2 retrievals from OCO-2, surface albedo data, and aerosol optical depth (AOD) measurements for 2019. This study employed a variety of machine learning algorithms, including Random Forest, XGBoost, and LightGBM, for the analysis. The results showed that Random Forest outperforms the other models, achieving a correlation of 0.933 with satellite products, a mean absolute error (MAE) of 0.713 ppm, and a root mean square error (RMSE) of 1.147 ppm. This model was then applied to retrieve CO2 column concentrations for 2020. The results showed a correlation of 0.760 with Total Carbon Column Observing Network (TCCON) measurements, which is higher than the correlation of 0.739 with satellite product data, verifying the effectiveness of the retrieval method. Full article
(This article belongs to the Special Issue Satellite Remote Sensing Applied in Atmosphere (3rd Edition))
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