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Atmosphere
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Variability and Predictability of Space Weather and the Ionosphere: Recent...
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Variability and Predictability of Space Weather and the Ionosphere: Recent Advances

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

Deadline for manuscript submissions: closed (15 May 2024) | Viewed by 6459

Special Issue Editor

Prof. Dr. Sandro Radicella

E-Mail Website
Guest Editor
Institute for Scientific Research, Boston College, Chestnut Hill, MA 02467, USA
Interests: ionospheric and space weather research and applications; GNSS science and technology
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Studying complex systems such as Space Weather and the Ionosphere is relevant to understanding the physical processes involved (Knowledge) and society’s use of such systems (Applications). New approaches, including the chaotic description of such systems and the development of advanced machine learning algorithms, make it possible to advance in the ability to model and predict the time evolution of their variables. It is of particular importance to have an updated view of these new approaches and their current or potential use to study and model Space Weather and Ionosphere variability and predictability. This Special Issue aims to provide a comprehensive view of the advancements in this field. Particular emphasis, though not exclusive, should be given to approaches that combine chaos theoretical descriptions and advanced machine learning algorithms when applied to Ionosphere and Space Weather basic modelling and society-oriented research. For this reason, we encourage colleagues to submit papers with this perspective to provide, in this Special Issue, an updated view on the subject.

Prof. Dr. Sandro Radicella
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.

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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

  • space weather modelling
  • Ionosphere modelling
  • chaos
  • machine learning

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Published Papers (6 papers)

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Research

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15 pages, 5121 KiB  
Article
Regional Spatial Mean of Ionospheric Irregularities Based on K-Means Clustering of ROTI Maps
by Yenca Migoya-Orué, Oladipo E. Abe and Sandro Radicella
Atmosphere 2024, 15(9), 1098; https://doi.org/10.3390/atmos15091098 - 9 Sep 2024
Viewed by 476
Abstract
In this paper, we investigate and propose the application of an unsupervised machine learning clustering method to characterize the spatial and temporal distribution of ionospheric plasma irregularities over the Western African equatorial region. The ordinary Kriging algorithm was used to interpolate the rate [...] Read more.
In this paper, we investigate and propose the application of an unsupervised machine learning clustering method to characterize the spatial and temporal distribution of ionospheric plasma irregularities over the Western African equatorial region. The ordinary Kriging algorithm was used to interpolate the rate of change of the total electron content (TEC) index (ROTI) over gridded 0.5° by 0.5° latitude and longitude regional maps in order to simulate the level of ionospheric plasma irregularities in a quasi-real-time scenario. K-means was used to obtain a spatial mean index through an optimal stratification of regional post-processed ROTI maps. The results obtained could be adapted by appropriate K-means algorithms to a real-time scenario, as has been performed for other applications. This method could allow us to monitor plasma irregularities in real time over the African region and, therefore, lead to the possibility of mitigating their effects on satellite-based location systems in the said region. Full article
(This article belongs to the Special Issue Variability and Predictability of Space Weather and the Ionosphere: Recent Advances)
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12 pages, 3681 KiB  
Article
The ap Prediction Tool Implemented by the A.Ne.Mo.S./NKUA Group
by Helen Mavromichalaki, Maria Livada, Argyris Stassinakis, Maria Gerontidou, Maria-Christina Papailiou, Line Drube and Aikaterini Karmi
Atmosphere 2024, 15(9), 1073; https://doi.org/10.3390/atmos15091073 - 5 Sep 2024
Cited by 1 | Viewed by 824
Abstract
A novel tool utilizing machine learning techniques was designed to forecast ap index values for the next three consecutive days (24 values). The tool employs time series data from the 3 h ap index of solar cycles 23 and 24 to train the [...] Read more.
A novel tool utilizing machine learning techniques was designed to forecast ap index values for the next three consecutive days (24 values). The tool employs time series data from the 3 h ap index of solar cycles 23 and 24 to train the Long Short-Term Memory (LSTM) model, predicting ap index values for the next 72 h at three-hour intervals. During periods of quiet geomagnetic activity, the LSTM model’s performance is sufficient to yield favorable outcomes. Nevertheless, during geomagnetically disturbed conditions, such as geomagnetic storms of different levels, the model needs to be adapted in order to provide accurate ap index results. In particular, when coronal mass ejections occur, the ap Prediction tool is modulated by inserting predominant features of coronal mass ejections such as the date of the event, the estimated time of arrival and the linear speed. In the present work, this tool is described thoroughly; moreover, results for G2 and G3 geomagnetic storms are presented. Full article
(This article belongs to the Special Issue Variability and Predictability of Space Weather and the Ionosphere: Recent Advances)
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21 pages, 1820 KiB  
Article
Enhanced Particle Classification in Water Cherenkov Detectors Using Machine Learning: Modeling and Validation with Monte Carlo Simulation Datasets
by Ticiano Jorge Torres Peralta, Maria Graciela Molina, Hernan Asorey, Ivan Sidelnik, Antonio Juan Rubio-Montero, Sergio Dasso, Rafael Mayo-Garcia, Alvaro Taboada, Luis Otiniano and for the LAGO Collaboration
Atmosphere 2024, 15(9), 1039; https://doi.org/10.3390/atmos15091039 - 28 Aug 2024
Cited by 1 | Viewed by 735
Abstract
The Latin American Giant Observatory (LAGO) is a ground-based extended cosmic rays observatory designed to study transient astrophysical events, the role of the atmosphere on the formation of secondary particles, and space-weather-related phenomena. With the use of a network of Water Cherenkov Detectors [...] Read more.
The Latin American Giant Observatory (LAGO) is a ground-based extended cosmic rays observatory designed to study transient astrophysical events, the role of the atmosphere on the formation of secondary particles, and space-weather-related phenomena. With the use of a network of Water Cherenkov Detectors (WCDs), LAGO measures the secondary particle flux, a consequence of the interaction of astroparticles impinging on the atmosphere of Earth. This flux can be grouped into three distinct basic constituents: electromagnetic, muonic, and hadronic components. When a particle enters a WCD, it generates a measurable signal characterized by unique features correlating to the particle’s type and the detector’s specific response. The resulting charge histograms from these signals provide valuable insights into the flux of primary astroparticles and their key characteristics. However, these data are insufficient to effectively distinguish between the contributions of different secondary particles. In this work, we extend our previous research by using detailed simulations of the expected atmospheric response to the primary flux and the corresponding response of our WCDs to atmospheric radiation. This dataset, which was created through the combination of the outputs of the ARTI and Meiga simulation frameworks, simulated the expected WCD signals produced by the flux of secondary particles during one day at the LAGO site in Bariloche, Argentina, situated at 865 m above sea level. This was achieved by analyzing the real-time magnetospheric and local atmospheric conditions for February and March of 2012, where the resultant atmospheric secondary-particle flux was integrated into a specific Meiga application featuring a comprehensive Geant4 model of the WCD at this LAGO location. The final output was modified for effective integration into our machine-learning pipeline. With an implementation of Ordering Points to Identify the Clustering Structure (OPTICS), a density-based clustering algorithm used to identify patterns in data collected by a single WCD, we have further refined our approach to implement a method that categorizes particle groups using advanced unsupervised machine learning techniques. This allowed for the differentiation among particle types and utilized the detector’s nuanced response to each, thus pinpointing the principal contributors within each group. Our analysis has demonstrated that applying our enhanced methodology can accurately identify the originating particles with a high degree of confidence on a single-pulse basis, highlighting its precision and reliability. These promising results suggest the feasibility of future implementations of machine-leaning-based models throughout LAGO’s distributed detection network and other astroparticle observatories for semi-automated, onboard and real-time data analysis. Full article
(This article belongs to the Special Issue Variability and Predictability of Space Weather and the Ionosphere: Recent Advances)
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17 pages, 5975 KiB  
Article
Unusual Forbush Decreases and Geomagnetic Storms on 24 March, 2024 and 11 May, 2024
by Helen Mavromichalaki, Maria-Christina Papailiou, Maria Livada, Maria Gerontidou, Pavlos Paschalis, Argyris Stassinakis, Maria Abunina, Nataly Shlyk, Artem Abunin, Anatoly Belov, Victor Yanke, Norma Crosby, Mark Dierckxsens and Line Drube
Atmosphere 2024, 15(9), 1033; https://doi.org/10.3390/atmos15091033 - 26 Aug 2024
Viewed by 2071
Abstract
As the current solar cycle 25 progresses and moves towards solar maxima, solar activity is increasing and extreme space weather events are taking place. Two severe geomagnetic storms accompanied by two large Forbush decreases in galactic cosmic ray intensity were recorded in March [...] Read more.
As the current solar cycle 25 progresses and moves towards solar maxima, solar activity is increasing and extreme space weather events are taking place. Two severe geomagnetic storms accompanied by two large Forbush decreases in galactic cosmic ray intensity were recorded in March and May, 2024. More precisely, on 24 March 2024, a G4 (according to the NOAA Space Weather Scale for Geomagnetic Storms) geomagnetic storm was registered, with the corresponding geomagnetic indices Kp and Dst equal to 8 and −130 nT, respectively. On the same day, the majority of ground-based neutron monitor stations recorded an unusual Forbush decrease. This event stands out from a typical Forbush decrease because of its high amplitude decrease phase and rapid recovery phase, i.e., 15% decrease and an extremely rapid recovery of 10% within 1.5 h, as recorded at the Oulu neutron monitor station. Furthermore, on 10–13 May 2024, an unusual G5 geomagnetic storm (geomagnetic indices Kp = 9 and Dst = −412 nT) was registered (the last G5 storm had been observed in 2003). In addition, the polar neutron monitor stations recorded a Ground Level Enhancement (GLE74) during the recovery phase of a large Forbush decrease of 15%, which started on 10 May 2024. In this study, a detailed analysis of these two severe events in regard to the accompanying solar activity, interplanetary conditions and solar energetic particle events is provided. Moreover, the results of the NKUA “GLE Alert++ system”, the NKUA/IZMIRAN “FD Precursory Signals” method and the NKUA “ap Prediction tool” concerning these events are presented. Full article
(This article belongs to the Special Issue Variability and Predictability of Space Weather and the Ionosphere: Recent Advances)
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17 pages, 2058 KiB  
Article
Complexity and Nonlinear Dependence of Ionospheric Electron Content and Doppler Frequency Shifts in Propagating HF Radio Signals within Equatorial Regions
by Aderonke Akerele, Babatunde Rabiu, Samuel Ogunjo, Daniel Okoh, Anton Kascheyev, Bruno Nava, Olawale Bolaji, Ibiyinka Fuwape, Elijah Oyeyemi, Busola Olugbon, Jacob Akinpelu and Olumide Ajani
Atmosphere 2024, 15(6), 654; https://doi.org/10.3390/atmos15060654 - 30 May 2024
Viewed by 669
Abstract
The abundance of ions within the ionosphere makes it an important region for both long range and satellite communication systems. However, characterizing the complexity in the ionosphere within the equatorial region of Abuja, with geographic coordinates of 8.99° N and 7.39° E and [...] Read more.
The abundance of ions within the ionosphere makes it an important region for both long range and satellite communication systems. However, characterizing the complexity in the ionosphere within the equatorial region of Abuja, with geographic coordinates of 8.99° N and 7.39° E and a geomagnetic latitude of −1.60, and Lagos, with geographic coordinates of 3.27° E and 6.48° N and a dip latitude of −1.72°, is a challenging and daunting task due to the intrinsic and external forces involved. In this study, chaos theory was applied on data from both an HF Doppler sounding system and the Global Navigation Satellite System (GNSS) for the characterization of the ionosphere over these two tropical locations during 2020–2021 with respect to the quality of high-frequency radio signals between the two locations. Our results suggest that the ionosphere at the two locations is chaotic, with its largest Lyapunov exponent values being greater than 0 (0.011≤λ≤0.041) and its correlation dimension being in the range of 1.388≤D2≤1.775. Furthermore, it was revealed that there exists a negative correlation between the state of the ionosphere and signal quality at the two locations. Using transfer entropy, it was confirmed that the ionosphere interfered more with signals during 2020, a year of lower solar activity (sunspot number, 8.8) compared to 2021 (sunspot number, 29.6). On a monthly scale, the influence of the ionosphere on signal quality was found to be complicated. The results obtained in this study will be useful in communication systems design, modelling, and prediction. Full article
(This article belongs to the Special Issue Variability and Predictability of Space Weather and the Ionosphere: Recent Advances)
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Review

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42 pages, 25177 KiB  
Review
Climatology of the Nonmigrating Tides Based on Long-Term SABER/TIMED Measurements and Their Impact on the Longitudinal Structures Observed in the Ionosphere
by Dora Pancheva, Plamen Mukhtarov and Rumiana Bojilova
Atmosphere 2024, 15(4), 478; https://doi.org/10.3390/atmos15040478 - 12 Apr 2024
Viewed by 955
Abstract
This paper presents climatological features of the longitudinal structures WN4, WN3, and WN2 and their drivers observed in the lower thermospheric temperatures and in the ionospheric TEC. For this purpose, two long-term data sets are utilized: the satellite SABER/TIMED temperature measurements, and the [...] Read more.
This paper presents climatological features of the longitudinal structures WN4, WN3, and WN2 and their drivers observed in the lower thermospheric temperatures and in the ionospheric TEC. For this purpose, two long-term data sets are utilized: the satellite SABER/TIMED temperature measurements, and the global TEC maps generated with the NASA JPL for the interval of 2002–2022. As the main drivers of the longitudinal structures are mainly nonmigrating tides, this study first investigates the climatology of those nonmigrating tides, which are the main contributors of the considered longitudinal structures; these are nonmigrating diurnal DE3, DE2, and DW2, and semidiurnal SW4 and SE2 tides. The climatology of WN4, WN3, and WN2 structures in the lower thermosphere reveals that WN4 is the strongest one with a magnitude of ~20 K observed at 10° S in August, followed by WN2 with ~13.9 K at 10° S in February, and the weakest is WN3 with ~12.4 K observed over the equator in July. In the ionosphere, WN3 is the strongest structure with a magnitude of 5.9 TECU located at −30° modip latitude in October, followed by WN2 with 5.4 TECU at 30 modip in March, and the last is WN4 with 3.7 TECU at −30 modip in August. Both the climatology of the WSA and the features of its drivers are investigated as well. Full article
(This article belongs to the Special Issue Variability and Predictability of Space Weather and the Ionosphere: Recent Advances)
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