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Remotely Sensed Data of Space Weather: New Observations, Approaches and...
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Remotely Sensed Data of Space Weather: New Observations, Approaches and Methods

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Atmospheric Remote Sensing".

Deadline for manuscript submissions: 30 December 2024 | Viewed by 5638

Special Issue Editors

Dr. Roberta Tozzi

E-Mail Website
Guest Editor
Istituto Nazionale di Geofisica e Vulcanologia (INGV), 00143 Rome, Italy
Interests: geomagnetism; data analysis; space weather
Dr. Tommaso Alberti

E-Mail Website
Guest Editor
Istituto Nazionale di Geofisica e Vulcanologia, Via di Vigna Murata 605, Rome, Italy
Interests: near-Earth electromagnetic environment (magnetosphere, ionosphere); extreme events in climate; sea level rise; turbulence in fluids and plasmas; theory of complex systems and chaos
Special Issues, Collections and Topics in MDPI journals
Dr. Giuseppe Consolini

E-Mail Website
Guest Editor
National Institute for Astrophysics-Institute for Space Astrophysics and Planetology (INAF-IAPS), 00133 Rome, Italy
Interests: complexity and turbulence in space plasmas; dynamical systems and information theory approaches to Sun-Earth relationships and Earth’s magnetospheric dynamics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Perturbations of solar origin can trigger a multitude of physical processes occurring in interplanetary space down to the Earth’s surface.

The study of these processes is of the utmost importance, both to understand the physical mechanisms that govern them and, ultimately, to try to mitigate the consequent hazards on technological systems and human exploration.

At present, these investigations rely on a huge amount of data and a large number of new mathematical tools that enable significant advances in the comprehension, modeling and forecasting of the long chain of phenomena occurring during space weather events. On the one hand, there is a large number of measurements from ground-based facilities (i.e., geomagnetic observatories, ionosondes and radars) and from many space-based missions. On the other hand, thanks to the increasing computing capabilities, we are witnessing a rapid development of new techniques (e.g., in the field of machine learning) able to cope with wide and complex datasets and extract information from them that would otherwise be unavailable.

This Special Issue hopes to publish studies combining the two above-mentioned aspects. We welcome the submission of papers focused on the application of novel techniques, as well as traditional techniques used in a novel way, to remotely sensed data for space weather purposes.

Contributions in the framework of space weather relevant to this Special Issue may include:

  • Modeling and forecasting of space-weather-relevant quantities through innovative mathematical techniques;
  • Novel approaches for extracting new information from historical databases;
  • New observations obtained from the analysis of recently issued either ground- or space-based measurements.

Dr. Roberta Tozzi
Dr. Tommaso Alberti
Dr. Giuseppe Consolini
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. Remote Sensing is an international peer-reviewed open access semimonthly 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 2700 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
  • ground-based observatories and LEO satellite measurements
  • modeling and forecasting
  • machine learning and advanced statistical analysis
  • sun–earth interaction
  • deterministic and stochastic approaches

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Published Papers (4 papers)

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Research

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18 pages, 3160 KiB  
Article
A Comparative Analysis of the Effect of Orbital Geometry and Signal Frequency on the Ionospheric Scintillations over a Low Latitude Indian Station: First Results from the 25th Solar Cycle
by Ramkumar Vankadara, Nirvikar Dashora, Sampad Kumar Panda and Jyothi Ravi Kiran Kumar Dabbakuti
Remote Sens. 2024, 16(10), 1698; https://doi.org/10.3390/rs16101698 - 10 May 2024
Cited by 1 | Viewed by 1615
Abstract
The equatorial post-sunset ionospheric irregularities induce rapid fluctuations in the phase and amplitude of global navigation satellite system (GNSS) signals which may lead to the loss of lock and can potentially degrade the position accuracy. This study presents a new analysis of L-band [...] Read more.
The equatorial post-sunset ionospheric irregularities induce rapid fluctuations in the phase and amplitude of global navigation satellite system (GNSS) signals which may lead to the loss of lock and can potentially degrade the position accuracy. This study presents a new analysis of L-band scintillation from a low latitude station at Guntur (Geographic 16.44°N, 80.62°E, dip 22.18°), India, for the period of 18 months from August 2021 to January 2023. The observations are categorized either in the medium Earth-orbiting (MEO) or geosynchronous orbiting (GSO) satellites (GSO is considered as a set of the geostationary and inclined geosynchronous satellites) for L1, L2, and L5 signals. The results show a higher occurrence of moderate (0.5 < S4 ≤ 0.8) and strong (S4 > 0.8) scintillations on different signals from the MEO compared to the GSO satellites. Statistically, the average of peak S4 values provides a higher confidence in the severity of scintillations on a given night, which is found to be in-line with the scintillation occurrences. The percentage occurrence of scintillation-affected satellites is found to be higher on L1 compared to other signals, wherein a contrasting higher percentage of affected satellites over GSO than MEO is observed. While a clear demarcation between the L2/L5 signals and L1 is found over the MEO, in the case of GSO, the CCDF over L5 is found to match mostly with the L1 signal. This could possibly originate from the space diversity gain effect known to impact the closely spaced geostationary satellite links. Another major difference of higher slopes and less scatter of S4 values corresponding to L1 versus L2/L5 from the GSO satellite is found compared to mostly non-linear highly scattered relations from the MEO. The distribution of the percentage of scintillation-affected satellites on L1 shows a close match between MEO and GSO in a total number of minutes up to ~60%. However, such a number of minutes corresponding to higher than 60% is found to be larger for GSO. Thus, the results indicate the possibility of homogeneous spatial patterns in a scintillation distribution over a low latitude site, which could originate from the closely spaced GSO links and highlight the role of the number of available satellites with the geometry of the links, being the deciding factors. This helps the ionospheric community to develop inter-GNSS (MEO and GSO) operability models for achieving highly accurate positioning solutions during adverse ionospheric weather conditions. Full article
(This article belongs to the Special Issue Remotely Sensed Data of Space Weather: New Observations, Approaches and Methods)
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20 pages, 21662 KiB  
Article
Polar Cap Patches Scaling Properties: Insights from Swarm Data
by Roberta Tozzi, Paola De Michelis, Giulia Lovati, Giuseppe Consolini, Alessio Pignalberi, Michael Pezzopane, Igino Coco, Fabio Giannattasio and Maria Federica Marcucci
Remote Sens. 2023, 15(17), 4320; https://doi.org/10.3390/rs15174320 - 1 Sep 2023
Viewed by 795
Abstract
Among the effects of space weather, the degradation of air traffic communications and satellite-based navigation systems are the most notable. For this reason, it is of uttermost importance to understand the nature and origin of ionospheric irregularities that are at the base of [...] Read more.
Among the effects of space weather, the degradation of air traffic communications and satellite-based navigation systems are the most notable. For this reason, it is of uttermost importance to understand the nature and origin of ionospheric irregularities that are at the base of the observed communication outages. Here we focus on polar cap patches (PCPs) that constitute a special class of ionospheric irregularities observed at very high latitudes in the F region. To this purpose we use the so-called PCP flag, a Swarm Level 2 product, that allows for identifying PCPs. We relate the presence of PCPs to the values of the first- and second-order scaling exponents and intermittency estimated from Swarm A electron density fluctuations and to the values of the Rate Of change of electron Density Index (RODI) for two different levels of geomagnetic activity, over a time span of approximately 3.5 years starting on 16 July 2014. Our findings show that values of RODI, first- and second-order scaling exponents and intermittency corresponding to measurements taken inside PCPs differ from those corresponding to measurements taken outside PCPs. Additionally, the values of the first- and second-order scaling exponents and of intermittency indicate that PCPs are in a turbulent state. Investigation of the coincidence of loss of lock (LoL) events with PCPs displayed that approximately 57.4% of LoLs in the Northern hemisphere and 45.7% in the Southern hemisphere occur in coincidence of PCPs when disturbed geomagnetic activity is considered. During quiet geomagnetic conditions these percentages decrease to 51.4% in the Northern hemisphere and to 20.1% in the Southern hemisphere. Full article
(This article belongs to the Special Issue Remotely Sensed Data of Space Weather: New Observations, Approaches and Methods)
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19 pages, 8738 KiB  
Article
Tracking Geomagnetic Storms with Dynamical System Approach: Ground-Based Observations
by Tommaso Alberti, Paola De Michelis, Lucia Santarelli, Davide Faranda, Giuseppe Consolini and Maria Federica Marcucci
Remote Sens. 2023, 15(12), 3031; https://doi.org/10.3390/rs15123031 - 9 Jun 2023
Cited by 2 | Viewed by 1439
Abstract
Using a dynamical systems approach, we examine the persistence and predictability of geomagnetic perturbations across a range of different latitudes and levels of geomagnetic activity. We look at the horizontal components of the magnetic field measured on the ground between 13 and 24 [...] Read more.
Using a dynamical systems approach, we examine the persistence and predictability of geomagnetic perturbations across a range of different latitudes and levels of geomagnetic activity. We look at the horizontal components of the magnetic field measured on the ground between 13 and 24 March 2015, at approximately 40 observatories in the Northern Hemisphere. We introduced two dynamical indicators: the extremal index θ, which quantifies the persistence of the system in a particular state and the instantaneous dimension d, which measures the active number of degrees of freedom of the system. The analysis revealed that during disturbed periods, the instantaneous dimension of the horizontal strength of the magnetic field, which depends on latitude, increases, indicating that the geomagnetic response is externally driven. Furthermore, during quiet times, the instantaneous dimension values fluctuate around the state-space dimension, indicating a more stochastic and thus less predictable nature system. Full article
(This article belongs to the Special Issue Remotely Sensed Data of Space Weather: New Observations, Approaches and Methods)
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14 pages, 6446 KiB  
Technical Note
A Prediction Model of Ionospheric Total Electron Content Based on Grid-Optimized Support Vector Regression
by Qiao Yu, Xiaobin Men and Jian Wang
Remote Sens. 2024, 16(15), 2701; https://doi.org/10.3390/rs16152701 - 23 Jul 2024
Viewed by 600
Abstract
Evaluating and mitigating the adverse effects of the ionosphere on communication, navigation, and other services, as well as fully utilizing the ionosphere, have become increasingly prominent topics in the academic community. To quantify the dynamical changes and improve the prediction accuracy of the [...] Read more.
Evaluating and mitigating the adverse effects of the ionosphere on communication, navigation, and other services, as well as fully utilizing the ionosphere, have become increasingly prominent topics in the academic community. To quantify the dynamical changes and improve the prediction accuracy of the ionospheric Total Electron Content (TEC), we propose a prediction model based on grid-optimized Support Vector Regression (SVR). This modeling processes include three steps: (1) dividing the dataset for training, validation, and testing; (2) determining the hyperparameters C and g by the grid search method through cross-validation using training and validation data; and (3) testing the trained model using the test data. Taking the Gakona station as an example, we compared the proposed model with the International Reference Ionosphere (IRI) model and a TEC prediction model based on Statistical Machine Learning (SML). The performance of the models was evaluated using the metrics of mean absolute error (MAE) and root mean square error (RMSE). The specific results are as follows: the MAE of the CCIR, URSI, SML, and SVR models compared to the observations are 1.06 TECU, 1.41 TECU, 0.7 TECU, and 0.54 TECU, respectively; the RMSE are 1.36 TECU, 1.62 TECU, 0.92 TECU, and 0.68 TECU, respectively. These results indicate that the SVR model has the most minor prediction error and the highest accuracy for predicting TEC. This method also provides a new approach for predicting other ionospheric parameters. Full article
(This article belongs to the Special Issue Remotely Sensed Data of Space Weather: New Observations, Approaches and Methods)
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