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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: closed (30 September 2024) | Viewed by 11346

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

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Related Special Issue

Published Papers (10 papers)

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Research

16 pages, 3251 KiB  
Article
Daily Rainfall Patterns During Storm “Daniel” Based on Different Satellite Data
by Stavros Kolios and Niki Papavasileiou
Atmosphere 2024, 15(11), 1277; https://doi.org/10.3390/atmos15111277 - 25 Oct 2024
Viewed by 526
Abstract
Extreme rainfall from a long-lived weather system called storm “Daniel” occurred from 4th to 11th September 2023 over the central and eastern Mediterranean, leading to many devastating flood events mainly in central Greece and the western coastal parts of Libya. This study analyzes [...] Read more.
Extreme rainfall from a long-lived weather system called storm “Daniel” occurred from 4th to 11th September 2023 over the central and eastern Mediterranean, leading to many devastating flood events mainly in central Greece and the western coastal parts of Libya. This study analyzes the daily rainfall amounts over all the affected geographical areas during storm “Daniel” by comparing three different satellite-based rainfall data products. Two of them are strictly related to Meteosat multispectral imagery, while the other one is based on the Global Precipitation Measurement (GPM) satellite mission. The satellite datasets depict extreme daily rainfall (up to 450 mm) for consecutive days in the same areas, with the spatial distribution of such rainfall amounts covering thousands of square kilometers almost during the whole period that the storm lasted. Moreover, the spatial extent of the heavy rainfall patterns was calculated on a daily basis. The convective nature of the rainfall, which was also recorded, characterizes the extremity of this weather system. Finally, the intercomparison of the datasets used highlights the satisfactory efficiency of the examined satellite datasets in capturing similar rainfall amounts in the same areas (daily mean error of 15 mm, mean absolute error of up to 35 mm and correlation coefficient ranging from 0.6 to 0.9 in most of the examined cases). This finding confirms the realistic detection and monitoring of the different satellite-based rainfall products, which should be used for early warning and decision-making regarding potential flood events. Full article
(This article belongs to the Special Issue Satellite Remote Sensing Applied in Atmosphere (2nd Edition))
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12 pages, 7913 KiB  
Article
SO2 Diffusion Features of the 2022 Hunga Tonga–Hunga Ha’apai Volcanic Eruptions from DSCOVR/EPIC Observations
by Yi Huang and Wentao Duan
Atmosphere 2024, 15(10), 1164; https://doi.org/10.3390/atmos15101164 - 29 Sep 2024
Viewed by 480
Abstract
Understanding the volcanic SO2 diffusive characteristics can enhance our knowledge of the impact of volcanic eruptions on climate change. In this study, the SO2 diffusion features of the Hunga Tonga–Hunga Ha’apai underwater volcano (HTHH) 2022 eruptions are investigated based on the [...] Read more.
Understanding the volcanic SO2 diffusive characteristics can enhance our knowledge of the impact of volcanic eruptions on climate change. In this study, the SO2 diffusion features of the Hunga Tonga–Hunga Ha’apai underwater volcano (HTHH) 2022 eruptions are investigated based on the Deep Space Climate Observatory (DSCOVR) Earth Polychromatic Imaging Camera (EPIC) dataset, which could provide longer term, more consistent, and higher temporal sampling rate observations to complement current low-orbit satellite-based research. SO2 plume major-direction profile analysis indicates that the SO2 diffusion extent of subaerial eruption initiating at 15:20/13 January 2022 was approximately 1500 km in the Southeast–Northwest major diffusive direction by 20:15/14 January 2022 (about 29 h after the HTHH subaerial eruption). All-direction SO2 plume analysis shows that the HTHH subaerial eruption-emitted SO2 plume could diffuse as far as 6242 km by 02:20/15 January 2022. Furthermore, these two analyses in terms of the HTHH major eruption initiating at 04:00/15 January 2022 imply that HTHH major eruption-emitted SO2 plume could diffuse as far as 8600 km in the Southeast–Northwest major diffusive direction by 02:24/18 January 2022 (about 70 h after the HTHH major eruption). It is also implied that HTHH major eruption-emitted SO2 plume could extend to approximately 14,729 km away from the crater by 13:12/18 January 2022. We believe that these findings could provide certain guidance for volcanic gas estimations, thus helping to deepen our understanding of volcanic impacts on climate change. Full article
(This article belongs to the Special Issue Satellite Remote Sensing Applied in Atmosphere (2nd Edition))
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17 pages, 4782 KiB  
Article
Long-Term Validation of Aeolus Level-2B Winds in the Brazilian Amazon
by Alexandre Calzavara Yoshida, Patricia Cristina Venturini, Fábio Juliano da Silva Lopes and Eduardo Landulfo
Atmosphere 2024, 15(9), 1026; https://doi.org/10.3390/atmos15091026 - 24 Aug 2024
Viewed by 699
Abstract
The Atmospheric Dynamics Mission ADM-Aeolus was successfully launched in August 2018 by the European Space Agency (ESA). The Aeolus mission carried a single instrument, the first-ever Doppler wind lidar (DWL) in space, called Atmospheric LAser Doppler INstrument (ALADIN). Aeolus circled the Earth, providing [...] Read more.
The Atmospheric Dynamics Mission ADM-Aeolus was successfully launched in August 2018 by the European Space Agency (ESA). The Aeolus mission carried a single instrument, the first-ever Doppler wind lidar (DWL) in space, called Atmospheric LAser Doppler INstrument (ALADIN). Aeolus circled the Earth, providing vertical profiles of horizontal line-of-sight (HLOS) winds on a global scale. The Aeolus satellite’s measurements filled critical gaps in existing wind observations, particularly in remote regions such as the Brazilian Amazon. This area, characterized by dense rainforests and rich biodiversity, is essential for global climate dynamics. The weather patterns of the Amazon are influenced by atmospheric circulation driven by Hadley cells and the Intertropical Convergence Zone (ITCZ), which are crucial for the distribution of moisture and heat from the equator to the subtropics. The data provided by Aeolus can significantly enhance our understanding of these complex atmospheric processes. In this long-term validation study, we used radiosonde data collected from three stations in the Brazilian Amazon (Cruzeiro do Sul, Porto Velho, and Rio Branco) as a reference to assess the accuracy of the Level 2B (L2B) Rayleigh-clear and Mie-cloudy wind products. Statistical validation was conducted by comparing Aeolus L2B wind products and radiosonde data covering the period from October 2018 to March 2023 for Cruzeiro do Sul and Porto Velho, and from October 2018 to December 2022 for Rio Branco. Considering all available collocated winds, including all stations, a Pearson’s coefficient (r) of 0.73 was observed in Rayleigh-clear and 0.85 in Mie-cloudy wind products, revealing a strong correlation between Aeolus and radiosonde winds, suggesting that Aeolus wind products are reliable for capturing wind profiles in the studied region. The observed biases were −0.14 m/s for Rayleigh-clear and −0.40 m/s for Mie-cloudy, fulfilling the mission requirement of having absolute biases below 0.7 m/s. However, when analyzed annually, in 2022, the bias for Rayleigh-clear was −0.95 m/s, which did not meet the mission requirements. Full article
(This article belongs to the Special Issue Satellite Remote Sensing Applied in Atmosphere (2nd Edition))
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17 pages, 5526 KiB  
Article
Generating Daily High-Resolution Regional XCO2 by Deep Neural Network and Multi-Source Data
by Wenjie Tian, Lili Zhang, Tao Yu, Dong Yao, Wenhao Zhang and Chunmei Wang
Atmosphere 2024, 15(8), 985; https://doi.org/10.3390/atmos15080985 - 16 Aug 2024
Viewed by 578
Abstract
CO2 is one of the primary greenhouse gases impacting global climate change, making it crucial to understand the spatiotemporal variations of CO2. Currently, commonly used satellites serve as the primary means of CO2 observation, but they often suffer from [...] Read more.
CO2 is one of the primary greenhouse gases impacting global climate change, making it crucial to understand the spatiotemporal variations of CO2. Currently, commonly used satellites serve as the primary means of CO2 observation, but they often suffer from striping issues and fail to achieve complete coverage. This paper proposes a method for constructing a comprehensive high-spatiotemporal-resolution XCO2 dataset based on multiple auxiliary data sources and satellite observations, utilizing multiple simple deep neural network (DNN) models. Global validation results against ground-based TCCON data demonstrate the excellent accuracy of the constructed XCO2 dataset (R is 0.94, RMSE is 0.98 ppm). Using this method, we analyze the spatiotemporal variations of CO2 in China and its surroundings (region: 0°–60° N, 70°–140° E) from 2019 to 2020. The gapless and fine-scale CO2 generation method enhances people’s understanding of CO2 spatiotemporal variations, supporting carbon-related research. Full article
(This article belongs to the Special Issue Satellite Remote Sensing Applied in Atmosphere (2nd Edition))
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13 pages, 7768 KiB  
Article
Development of X-Band Geophysical Model Function for Sea Surface Wind Speed Retrieval with ASNARO-2
by Yuko Takeyama and Shota Kurokawa
Atmosphere 2024, 15(6), 686; https://doi.org/10.3390/atmos15060686 - 4 Jun 2024
Viewed by 598
Abstract
In the present study, a new geophysical model function (GMF) is developed for the X-band synthetic aperture radar (SAR) on board the Advanced Satellite with New System Architecture for Observation-2 (ASNARO-2) to retrieve accurate offshore wind speeds. Equivalent neutral wind speeds based on [...] Read more.
In the present study, a new geophysical model function (GMF) is developed for the X-band synthetic aperture radar (SAR) on board the Advanced Satellite with New System Architecture for Observation-2 (ASNARO-2) to retrieve accurate offshore wind speeds. Equivalent neutral wind speeds based on the local forecast model (LFM) are employed as reference wind vectors, and 12,259 matching points from 502 SAR images obtained with horizontal transmitting, horizontal receiving polarization around Japan are collected. To ensure convergence of the calculation, 8129 points are selected from the matching points to determine the basic formula for the GMF and 23 coefficients based on the relationships among the normalized radar cross section, wind speed, incidence angle, and relative wind direction. Compared with the reference wind speeds, the GMF wind speeds showed reproducibility with a bias of −0.10 m/s and an RMSD of 1.37 m/s. Additionally, it can be confirmed that the retrieved wind speed has the bias of 0.03 and the RMSD of 1.68 m/s when compared to the in situ wind speed from the Kuroshio Extension Observatory (KEO) buoy. The accuracy of these retrieved wind speeds is comparable to previous studies, and it is indicated that the developed GMF can be used to retrieve offshore winds from ASNARO-2 images. Full article
(This article belongs to the Special Issue Satellite Remote Sensing Applied in Atmosphere (2nd Edition))
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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
Cited by 2 | Viewed by 1772
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 1608
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 1246
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
Cited by 1 | Viewed by 1142
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 - 7 Sep 2023
Cited by 3 | Viewed by 1607
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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