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Atmosphere
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Satellite Remote Sensing Applied in Atmosphere
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Satellite Remote Sensing Applied in Atmosphere

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 (20 January 2023) | Viewed by 5782

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 an increasing number of potential applications in a wide range of atmospheric sciences because of the continuous improvement of modern satellite sensors, which provide high-quality data and products, and their capability to monitor even the most remote areas worldwide.

The atmosphere of the Earth is where weather and climate are created and evolve, whereas changes in 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 they therefore need to be efficiently and accurately monitored from space. The accuracy assessment of any type of satellite data and products, their spatiotemporal analyses in different topics of atmospheric sciences and meteorology, relative satellite-based applications, innovative techniques and methods that promote satellite remote sensing in an atmospheric environment, and weather events, are therefore, challenging research areas.

Studies dealing with these topics, based on remotely sensed data and products derived from satellites, are welcome to this Special Issue, to which authors are cordially 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 (4 papers)

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Research

11 pages, 1371 KiB  
Article
Simulation Study of the Applicability of the “Slice” Approach to Assessing the Water Content in Clouds from the Radar Return Signal
by Boris S. Yurchak
Atmosphere 2023, 14(1), 42; https://doi.org/10.3390/atmos14010042 - 26 Dec 2022
Viewed by 977
Abstract
By computer simulation, the main consequences of the “slice” approach to assessing the power of a radar return signal from an atmospheric water cloud were verified. The cloud was modeled as a set of slices, which are thin layers coinciding with the wavefront [...] Read more.
By computer simulation, the main consequences of the “slice” approach to assessing the power of a radar return signal from an atmospheric water cloud were verified. The cloud was modeled as a set of slices, which are thin layers coinciding with the wavefront of the probing radar radiation. The return signal was calculated as the convolution of the probe pulse with the cloud. The simulation was based on the previously derived “slice” radar equation, which determines the received power taking into account the statistical characteristics of fluctuations in the number of slice droplets and their water content. Estimates of the magnitude of the simulated return signal were compared with the theoretical formula for various statistical characteristics of the ensemble of droplets that make up the cloud model. The simulation result confirmed the validity of using the slice approach to interpret radar measurements of water content in clouds. Full article
(This article belongs to the Special Issue Satellite Remote Sensing Applied in Atmosphere)
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17 pages, 3917 KiB  
Article
Direct Detection of Severe Biomass Burning Aerosols from Satellite Data
by Makiko Nakata, Sonoyo Mukai and Toshiyuki Fujito
Atmosphere 2022, 13(11), 1913; https://doi.org/10.3390/atmos13111913 - 17 Nov 2022
Cited by 4 | Viewed by 1383
Abstract
The boundary between high-concentration aerosols (haze) and clouds is ambiguous and the mixing of aerosols and clouds is complex in terms of composition and structure. In particular, the contribution of biomass burning aerosols (BBAs) to global warming is a source of uncertainty in [...] Read more.
The boundary between high-concentration aerosols (haze) and clouds is ambiguous and the mixing of aerosols and clouds is complex in terms of composition and structure. In particular, the contribution of biomass burning aerosols (BBAs) to global warming is a source of uncertainty in the global radiation budget. In a previous study, we proposed a method to detect absorption aerosols such as BBAs and dust using a simple indicator based on the ratio of violet to near-ultraviolet wavelengths from the Global Change Observation Mission-Climate/Second-Generation Global Imager (GCOM-C/SGLI) satellite data. This study adds newly obtained SGLI data and proposes a method for the direct detection of severe biomass burning aerosols (SBBAs). Moreover, polarization data derived from polarization remote sensing was incorporated to improve the detection accuracy. This is possible because the SGLI is a multi-wavelength sensor consisting of 19 channels from 380 nm in the near-ultraviolet to thermal infrared, including red (674 nm) and near-infrared (869 nm) polarization channels. This method demonstrated fast SBBA detection directly from satellite data by using two types of wavelength ratio indices that take advantage of the characteristics of the SGLI data. The SBBA detection algorithm derived from the SGLI observation data was validated by using the polarized reflectance calculated by radiative transfer simulations and a regional numerical model—scalable computing for advanced library and environment (SCALE). Our algorithm can be applied to the detection of dust storms and high-concentration air pollution particles, and identifying the type of high-concentration aerosol facilitates the subsequent detailed characterization of the aerosol. This work demonstrates the usefulness of polarization remote sensing beyond the SGLI data. Full article
(This article belongs to the Special Issue Satellite Remote Sensing Applied in Atmosphere)
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13 pages, 2200 KiB  
Article
Atmospheric Density Response to a Severe Magnetic Storm Detected by the 520 km Altitude Spherical Satellite
by Xinyue Wang, Yujiao Jin, Xiangguang Meng, Dan Du, Aibing Zhang, Xinchun Tang, Feng Yan, Yueqiang Sun, Xianguo Zhang, Bowen Wang and Yuerong Cai
Atmosphere 2022, 13(11), 1891; https://doi.org/10.3390/atmos13111891 - 12 Nov 2022
Cited by 4 | Viewed by 1277
Abstract
The polar-orbiting spherical experimental satellite of China for atmospheric density detection with an altitude of ~520 km was successfully launched on 14 October 2021. Based on the dynamic inversion method for atmospheric density and the precise orbit determination data obtained by its GNSS, [...] Read more.
The polar-orbiting spherical experimental satellite of China for atmospheric density detection with an altitude of ~520 km was successfully launched on 14 October 2021. Based on the dynamic inversion method for atmospheric density and the precise orbit determination data obtained by its GNSS, we inverted the orbital atmospheric density during the severe geomagnetic storm in early November 2021. In this paper, we compared the atmospheric density data obtained by the spherical satellite with the simulations of the MSISE00 and the DTM, evaluated their error distribution, and analyzed the response of the atmospheric density during the severe geomagnetic storm in the dawn–dusk orbit of 520 km altitude. The properties and the physical processes for the atmospheric density of the time evolutions in different latitudes and the global distributions during the severe geomagnetic storm were obtained. We found that the substantial disturbance enhancement and recovery of the atmospheric density of the dawn–dusk orbit have a close correlation with the geomagnetic indexes Kp and Dst. The elevation extends from the poles to the equator, and the relative variation in two hemispheres demonstrates a bimodal nearly symmetric growth structure. The maximum relative variation of the two hemispheres both occurred in the middle latitude, and, for this case, the enhancement of atmospheric density in the mid-latitude region accounted for a larger proportion. The asymmetry between the northern and southern hemispheres is demonstrated by the fact that the absolute value and absolute change in the southern hemisphere in summer are larger than those in the northern hemisphere, and the bimodal structure of the relative variation is inclined to the northern hemisphere. Full article
(This article belongs to the Special Issue Satellite Remote Sensing Applied in Atmosphere)
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17 pages, 2962 KiB  
Article
Accuracy Assessment of a Satellite-Based Rain Estimation Algorithm Using a Network of Meteorological Stations over Epirus Region, Greece
by Stavros Kolios, Nikos Hatzianastassiou, Christos J. Lolis and Aristides Bartzokas
Atmosphere 2022, 13(8), 1286; https://doi.org/10.3390/atmos13081286 - 12 Aug 2022
Cited by 1 | Viewed by 1212
Abstract
The study concerns the quantitative evaluation of a satellite-based rain rate (RR) estimation algorithm using measurements from a network of ground-based meteorological stations across the Epirus Region, Greece, an area that receives among the maximum precipitation amounts over the country. The utilized version [...] Read more.
The study concerns the quantitative evaluation of a satellite-based rain rate (RR) estimation algorithm using measurements from a network of ground-based meteorological stations across the Epirus Region, Greece, an area that receives among the maximum precipitation amounts over the country. The utilized version of the rain estimation algorithm uses the Meteosat-11 Brightness Temperature in five spectral regions ranging from 6.0 to 12.0 μm (channels 5–7, 9 and 10) to estimate the rain intensity on a pixel basis, after discriminating the rain/non-rain pixels with a simple thresholding method. The rain recordings of the meteorological stations’ network were spatiotemporally correlated with the satellite-based rain estimations, leading to a dataset of 2586 pairs of matched values. A statistical analysis of these pairs of values was conducted, revealing a Mean Error (ME) of −0.13 mm/h and a correlation coefficient (CC) of 0.52. The optimal computed Probability of False Detection (POFD), Probability of Detection (POD), the False Alarm Ratio (FAR) and the bias score (BIAS) are equal to 0.32, 0.88, 0.12 and 0.94, respectively. The study of the extreme values of the RR (the highest 10%) also shows satisfactory results (i.e., ME of 1.92 mm/h and CC of 0.75). The evaluation statistics are promising for operationally using this algorithm for rain estimation on a real-time basis. Full article
(This article belongs to the Special Issue Satellite Remote Sensing Applied in Atmosphere)
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