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Atmosphere
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Satellite Remote Sensing Applied in Atmosphere (2nd Edition)
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Satellite Remote Sensing Applied in Atmosphere (2nd 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: 30 September 2024 | Viewed by 5064

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,

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

Related Special Issue

Published Papers (5 papers)

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Research

16 pages, 5439 KiB  
Article
Revisiting the Characteristics of Super Typhoon Saola (2023) Using GPM, Himawari-9 and FY-4B Satellite Data
by Yuanmou Wang, Baicheng Xia, Yanan Chen, Huan Chen and Jing Xie
Atmosphere 2024, 15(3), 290; https://doi.org/10.3390/atmos15030290 - 27 Feb 2024
Viewed by 764
Abstract
Typhoon Saola was the ninth typhoon that generated over the Western North Pacific (WNP) in 2023, and it caused severe storm impacts. However, its complex moving track and heavy intensity made it extremely difficult to forecast; therefore, detailed analysis is necessary. In this [...] Read more.
Typhoon Saola was the ninth typhoon that generated over the Western North Pacific (WNP) in 2023, and it caused severe storm impacts. However, its complex moving track and heavy intensity made it extremely difficult to forecast; therefore, detailed analysis is necessary. In this study, GPM, Himawari-9, and FY-4B satellite data were used to analyze the characteristics of the structure, brightness temperature, and precipitation of the typhoon cloud system. Our results showed that, in the 89 and 183 GHz channels of GPM-1CGMI, the brightness temperature of the typhoon eye was 80–90 K higher than that of the eye wall, and the strong convective areas below 200 K were clearer in these high-frequency channels. GPM-2ADPR estimated heavy rain (over 30 mm/h) area, storm height (5 km), and vertical precipitation rate (30–40 mm/h) more accurately than the GPM-2Aka and GPM-2Aku products. Himawari-9 satellite data showed that the brightness temperature of the eye wall and spiral cloud bands was 180–200 K, the typhoon eye was small and round, and strong convective activities were mostly located in the southwest side of the center. The FY-4B CLP and CLT products showed that, in the mature period of the typhoon, the percentage of supercooled and mixed clouds first stabilized and then rapidly decreased. The trends observed among the three types of ice-phase clouds were characterized by an initial increase, followed by a decrease, and then another increase, with percentages between 10% and 25%, 5% and 15%, and 15% and 30%, respectively. Full article
(This article belongs to the Special Issue Satellite Remote Sensing Applied in Atmosphere (2nd Edition))
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15 pages, 6680 KiB  
Article
A Radar Echo Extrapolation Model Based on a Dual-Branch Encoder–Decoder and Spatiotemporal GRU
by Yong Cheng, Haifeng Qu, Jun Wang, Kun Qian, Wei Li, Ling Yang, Xiaodong Han and Min Liu
Atmosphere 2024, 15(1), 104; https://doi.org/10.3390/atmos15010104 - 14 Jan 2024
Viewed by 1067
Abstract
Precipitation forecasting is an immensely significant aspect of meteorological prediction. Accurate weather predictions facilitate services in sectors such as transportation, agriculture, and tourism. In recent years, deep learning-based radar echo extrapolation techniques have found effective applications in precipitation forecasting. However, the ability of [...] Read more.
Precipitation forecasting is an immensely significant aspect of meteorological prediction. Accurate weather predictions facilitate services in sectors such as transportation, agriculture, and tourism. In recent years, deep learning-based radar echo extrapolation techniques have found effective applications in precipitation forecasting. However, the ability of existing methods to extract and characterize complex spatiotemporal features from radar echo images remains insufficient, resulting in suboptimal forecasting accuracy. This paper proposes a novel extrapolation algorithm based on a dual-branch encoder–decoder and spatiotemporal Gated Recurrent Unit. In this model, the dual-branch encoder–decoder structure independently encodes radar echo images in the temporal and spatial domains, thereby avoiding interference between spatiotemporal information. Additionally, we introduce a Multi-Scale Channel Attention Module (MSCAM) to learn global and local feature information from each encoder layer, thereby enhancing focus on radar image details. Furthermore, we propose a Spatiotemporal Attention Gated Recurrent Unit (STAGRU) that integrates attention mechanisms to handle temporal evolution and spatial relationships within radar data, enabling the extraction of spatiotemporal information from a broader receptive field. Experimental results demonstrate the model’s ability to accurately predict morphological changes and motion trajectories of radar images on real radar datasets, exhibiting superior performance compared to existing models in terms of various evaluation metrics. This study effectively improves the accuracy of precipitation forecasting in radar echo images, provides technical support for the short-range forecasting of precipitation, and has good application prospects. Full article
(This article belongs to the Special Issue Satellite Remote Sensing Applied in Atmosphere (2nd Edition))
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16 pages, 4954 KiB  
Article
A Regional Aerosol Model for the Middle Urals Based on CALIPSO Measurements
by Ekaterina S. Nagovitsyna, Sergey K. Dzholumbetov, Alexander A. Karasev and Vassily A. Poddubny
Atmosphere 2024, 15(1), 48; https://doi.org/10.3390/atmos15010048 - 30 Dec 2023
Viewed by 801
Abstract
The present work aims to develop a regional Middle Urals Aerosol model (MUrA model) based on the joint analysis of long-term ground-based photometric measurements of the Aerosol Robotic NETwork (AERONET) and the results of lidar measurements of the CALIPSO (Cloud-Aerosol Lidar and Infrared [...] Read more.
The present work aims to develop a regional Middle Urals Aerosol model (MUrA model) based on the joint analysis of long-term ground-based photometric measurements of the Aerosol Robotic NETwork (AERONET) and the results of lidar measurements of the CALIPSO (Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation) satellite relying on information on the air trajectories at different altitudes calculated using the HYSPLIT (Hybrid Single Particle Lagrangian Integrated Trajectory model) software package. The MUrA model contains parameters of normalized volume size distributions (NVSDs) characterizing the tropospheric aerosol subtypes detected by the CALIPSO satellite. When comparing the MUrA model with the global CALIPSO Aerosol Model (CAMel), we found significant differences in NVSDs for elevated smoke and clean continental aerosol types. NVSDs for dust and polluted continental/smoke aerosol types in the global and regional models differ much less. The total volumes of aerosol particles along the atmospheric column reconstructed from satellite measurements of the attenuation coefficient at a wavelength of 532 nm based on the regional MUrA model and global CAMel are compared with the AERONET inversion data. The mean bias error for the regional model is 0.016 μm3/μm2, and 0.043 μm3/μm2 for the global model. Full article
(This article belongs to the Special Issue Satellite Remote Sensing Applied in Atmosphere (2nd Edition))
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20 pages, 7330 KiB  
Article
South Asian High Identification and Rainstorm Monitoring Using Fengyun-4-Derived Atmospheric Motion Vectors
by Suling Ren, Danyu Qin, Ning Niu and Bingyun Yang
Atmosphere 2023, 14(11), 1606; https://doi.org/10.3390/atmos14111606 - 26 Oct 2023
Viewed by 764
Abstract
Based on atmospheric motion vectors (AMVs) derived from the Fengyun-4 meteorological satellite (FY-4), in this paper, integrated multi-satellite retrievals for GPM precipitation and reanalysis datasets and the vertical distribution characteristics of FY-4 AMVs, their application in the identification of the South Asian high [...] Read more.
Based on atmospheric motion vectors (AMVs) derived from the Fengyun-4 meteorological satellite (FY-4), in this paper, integrated multi-satellite retrievals for GPM precipitation and reanalysis datasets and the vertical distribution characteristics of FY-4 AMVs, their application in the identification of the South Asian high (SAH) anticyclone and their application in the real-time monitoring of rainstorm disasters are studied. The results show that the AMVs’ vertical distribution characteristics are different across regions and seasons. AMVs from 150 to 350 hPa can be chosen as the upper troposphere wind (the total number accounts for about 77.2% on average). The center and shape of the upper tropospheric anticyclone obtained from AMVs are close to or slightly southward compared with those of the SAH at 200 hPa obtained from the ERA5 geopotential height. The SAH ridge line identified using the upper troposphere AMV zonal wind (the zonal wind is equal to zero) is slightly southward by about 1–2 degrees of latitude from that identified using ERA5 at 200 hPa but with a similar seasonal advance. The upper troposphere AMV can be used to monitor the location of the SAH and the evolution of its ridge line. The abnormally strong precipitation in South China is related to the location of the SAH and its ridge line. When the precipitation is abnormally strong/weak, the upper troposphere AMV deviation airflow shows divergence/convergence. During the “Dragon Boat Water” period in South China in 2022, strong precipitation occurred in the strong westerly winds or divergent flow on the northeast side of the upper troposphere anticyclone obtained from AMVs, and the precipitation intensity was the strongest when the divergence reached its peak, but this is not shown clearly in the EAR5 dataset. Full article
(This article belongs to the Special Issue Satellite Remote Sensing Applied in Atmosphere (2nd Edition))
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16 pages, 2524 KiB  
Article
All-Day Cloud Classification via a Random Forest Algorithm Based on Satellite Data from CloudSat and Himawari-8
by Yuanmou Wang, Chunmei Hu, Zhi Ding, Zhiyi Wang and Xuguang Tang
Atmosphere 2023, 14(9), 1410; https://doi.org/10.3390/atmos14091410 - 07 Sep 2023
Cited by 1 | Viewed by 1090
Abstract
It remains challenging to accurately classify complicated clouds owing to the various types of clouds and their distribution on multiple layers. In this paper, multi-band radiation information from the geostationary satellite Himawari-8 and the cloud classification product of the polar orbit satellite CloudSat [...] Read more.
It remains challenging to accurately classify complicated clouds owing to the various types of clouds and their distribution on multiple layers. In this paper, multi-band radiation information from the geostationary satellite Himawari-8 and the cloud classification product of the polar orbit satellite CloudSat from June to September 2018 are investigated. Based on sample sets matched by two types of satellite data, a random forest (RF) algorithm was applied to train a model, and a retrieval method was developed for cloud classification. With the use of this method, the sample sets were inverted and classified as clear sky, low clouds, middle clouds, thin cirrus, thick cirrus, multi-layer clouds and deep convection (cumulonimbus) clouds. The results indicate that the average accuracy for all cloud types during the day is 88.4%, and misclassifications mainly occur between low and middle clouds, thick cirrus clouds and cumulonimbus clouds. The average accuracy is 79.1% at night, with more misclassifications occurring between middle clouds, multi-layer clouds and cumulonimbus clouds. Moreover, Typhoon Muifa from 2022 was selected as a sample case, and the cloud type (CLT) product of an FY-4A satellite was used to examine the classification method. In the cloud system of Typhoon Muifa, a cumulonimbus area classified using the method corresponded well with a mesoscale convective system (MCS). Compared to the FY-4A CLT product, the classifications of ice-type (thick cirrus) and multi-layer clouds are effective, and the location, shape and size of these two varieties of cloud are similar. Full article
(This article belongs to the Special Issue Satellite Remote Sensing Applied in Atmosphere (2nd Edition))
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