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Atmosphere
Special Issues
Monitoring and Forecasting of Ionospheric Space Weather
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Monitoring and Forecasting of Ionospheric Space Weather

A special issue of Atmosphere (ISSN 2073-4433). This special issue belongs to the section "Upper Atmosphere".

Deadline for manuscript submissions: closed (25 October 2022) | Viewed by 6166

Special Issue Editor

Dr. Biqiang Zhao

E-Mail Website
Guest Editor
Key Laboratory of Earth and Planetary Physics, Institute of Geology and Geophysics, Chinese Academy of Sciences, Beijing 100029, China
Interests: ionospheric storm; GNSS-TEC; equatorial ionosphere; ionospheric radar technique

Special Issue Information

Dear Colleagues,

This issue aims to report progress in monitoring and forecasting ionospheric space weather, mainly the ionospheric storm and irregularity based on different observations, including ground- and space-based measurements. New techniques from both theoretical and mathematical aspects such as the coupling model and latest deep learning method and observational results from recent well-known ionospheric exploration missions are encouraged to be introduced. This issue will pay more attention on the regional difference of ionospheric variation both in the geomagnetic quiet and disturbed condition, with the aim to reveal the properties of mid-small scale ionospheric spatial and temporal variations and the physical mechanisms behind them.  The research can also extend to other planets' ionosphere.

Dr. Biqiang Zhao
Guest Editor

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

  • forecasting
  • ionospheric storm
  • ionospheric irregularity
  • ionospheric scintillation
  • GNSS-TEC
  • radar

Published Papers (4 papers)

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Research

12 pages, 7108 KiB  
Article
Ionospheric Electron Density Model by Electron Density Grid Deep Neural Network (EDG-DNN)
by Zhou Chen, Bokun An, Wenti Liao, Yungang Wang, Rongxin Tang, Jingsong Wang and Xiaohua Deng
Atmosphere 2023, 14(5), 810; https://doi.org/10.3390/atmos14050810 - 29 Apr 2023
Cited by 1 | Viewed by 1620
Abstract
Electron density (or electron concentration) is a critical metric for characterizing the ionosphere’s mobility. Shortwave technologies, remote sensing systems, and satellite communications—all rely on precise estimations of electron density in the ionosphere. Using electron density profiles from FORMOSAT-3/COSMIC (Constellation Observation System for Meteorology, [...] Read more.
Electron density (or electron concentration) is a critical metric for characterizing the ionosphere’s mobility. Shortwave technologies, remote sensing systems, and satellite communications—all rely on precise estimations of electron density in the ionosphere. Using electron density profiles from FORMOSAT-3/COSMIC (Constellation Observation System for Meteorology, Ionosphere, and Climate) from 2006 to 2013, a four-dimensional physical grid model of ionospheric electron density was created in this study. The model, known as EDG-DNN, utilizes a DNN (deep neural network), and its output is the electron density displayed as a physical grid. The preprocessed electron density data are used to construct training, validation, and test sets. The International Reference Ionosphere model (IRI) was chosen as the reference model for the validation procedure since it predicts electron density well. This work used the IRI-2016 version. IRI-2016 produced more precise results of electron density when time and location parameters were input. This study compares the electron density provided by IRI-2016 to the EDG-DNN to assess the merits of the latter. The final results reveal that EDG-DNN has low-error and strong stability, can represent the global distribution structure of electron density, has some distinctive features of ionospheric electron density distribution, and predicts electron density well during quiet periods. Full article
(This article belongs to the Special Issue Monitoring and Forecasting of Ionospheric Space Weather)
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23 pages, 6620 KiB  
Article
Tomographic Inversion of the Ionosphere by Rejecting Abnormal Corrections and Rays
by Jianmin Zhang, Jieqing Yu, Chenyi Jia, Yuchen Dai, Yanyu Zhu, Yingqi Huang and Lixin Wu
Atmosphere 2022, 13(12), 1954; https://doi.org/10.3390/atmos13121954 - 23 Nov 2022
Viewed by 1430
Abstract
The errors contained in slant total electron content (STEC) have a strong impact on the image generated by ionosphere tomography. This paper presents a method that rejects abnormal corrections and rays (RACR) in the multiplicative algebraic reconstruction technique (MART) algorithm by applying a [...] Read more.
The errors contained in slant total electron content (STEC) have a strong impact on the image generated by ionosphere tomography. This paper presents a method that rejects abnormal corrections and rays (RACR) in the multiplicative algebraic reconstruction technique (MART) algorithm by applying a correction threshold and a rejecting ratio threshold. The RACR algorithm was validated using ionosonde observations, Swarm satellite measurements, independent STEC observations and a vertical total electron content (TEC) map. Its performance was compared with the MART algorithm on both geomagnetically quiet days and disturbed days. The results show that the RACA algorithm is able to capture the main phase and the recovery phase of a storm and outperforms the MART algorithm under both geomagnetic conditions. The average improvements over the MART algorithm are 36.01%, 36.56%, 6.18%, 22.10% and 6.03% in the validation tests of the peak density of F2 layer, peak height of F2 layer, the electron density of the topside ionosphere, STEC and VTEC, respectively. The quality of the image produced by the RACR algorithm was controlled by the correction threshold and the rejection threshold. Smaller threshold values tend to make the image smoother. The RACR algorithm provides not only a way to produce a better tomographic image but also a means to detect abnormal rays. Full article
(This article belongs to the Special Issue Monitoring and Forecasting of Ionospheric Space Weather)
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15 pages, 2763 KiB  
Article
Variability of Equatorial Ionospheric Bubbles over Planetary Scale: Assessment of Terrestrial Drivers
by Lalit Mohan Joshi, Lung-Chih Tsai, Shin-Yi Su and Abhijit Dey
Atmosphere 2022, 13(9), 1517; https://doi.org/10.3390/atmos13091517 - 17 Sep 2022
Viewed by 1546
Abstract
Nighttime F-region field-aligned irregularities (FAIs) associated with equatorial plasma bubbles (EPBs) are impacted by terrestrial factors, such as solar irradiance and geomagnetic activity. This paper examines the impact of the planetary-scale periodic variability of terrestrial processes on EPB activity. Continual observations of the [...] Read more.
Nighttime F-region field-aligned irregularities (FAIs) associated with equatorial plasma bubbles (EPBs) are impacted by terrestrial factors, such as solar irradiance and geomagnetic activity. This paper examines the impact of the planetary-scale periodic variability of terrestrial processes on EPB activity. Continual observations of the Equatorial Atmosphere Radar (EAR) have been utilized to derive the intra-seasonal variability of nighttime F-region FAIs in the context of the terrestrial factors mentioned above. A periodicity analysis using wavelet and Lomb–Scargle (LS) spectral analysis indicated significant amplitudes of the long-period planetary-scale variability in the F-region FAI signal-to-noise ratio (SNR), 10.7 cm flux, and geomagnetic indices, as well as a shorter period of variability. Interestingly, a careful inspection of the time series indicated the planetary-scale variability of F-region FAIs to be reasonably out of phase with the periodic geomagnetic variability. EPB occurrence and the FAI signal-to-noise ratio presented a systematic decrease with an increase in the level of geomagnetic activity. Non-transient quiet-time geomagnetic activity has been found to suppress both the occurrence as well as the strength of F-region FAIs. The impacts of planetary-scale geomagnetic activity appear to be non-identical in the summer and equinoctial EPBs. The results highlight the importance of periodic terrestrial processes in driving the planetary-scale variability of EPBs. Full article
(This article belongs to the Special Issue Monitoring and Forecasting of Ionospheric Space Weather)
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17 pages, 2771 KiB  
Article
North–South IMF Disturbance Detection via an Adaptive Filter Approach
by Erik Schmölter and Jens Berdermann
Atmosphere 2022, 13(9), 1482; https://doi.org/10.3390/atmos13091482 - 13 Sep 2022
Viewed by 924
Abstract
Geomagnetic storm-detection algorithms are important for space-weather-warning services to provide reliable warnings (e.g., ionospheric disturbances). For that reason, a new approach using an adaptive filter (least mean squares algorithm) for the detection of geomagnetic storms based on the volatility of the north–south interplanetary [...] Read more.
Geomagnetic storm-detection algorithms are important for space-weather-warning services to provide reliable warnings (e.g., ionospheric disturbances). For that reason, a new approach using an adaptive filter (least mean squares algorithm) for the detection of geomagnetic storms based on the volatility of the north–south interplanetary magnetic field Bz is presented. The adaptive filter is not dependent on solar wind plasma measurements, which are more frequently affected by data gaps than Bz, and is less dependent on the magnitude of Bz disturbances compared with other detection algorithms (e.g., static thresholds). The configuration of the filter is discussed in detail with three geomagnetic storm events, and required optimization as well as possible extensions are discussed. However, the proposed configuration performs satisfactorily without further improvements, and good correlations are observed with geomagnetic indices. Long-term changes are also reflected by the filter (solar cycles 23 and 24), and thus the performance is not affected by different solar wind conditions during the solar minimum and maximum. Conclusively, the proposed filter provides a good solution when more complex approaches (e.g., solar-wind–magnetosphere coupling functions) that rely on solar wind plasma measurements are not available. Full article
(This article belongs to the Special Issue Monitoring and Forecasting of Ionospheric Space Weather)
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