Solar Storms Research Articles

Aurora Classification in All-Sky Images via CNN–Transformer

An aurora is a unique geophysical phenomenon with polar characteristics that can be directly observed with the naked eye. It is the most concentrated manifestation of solar–terrestrial physical processes (especially magnetospheric–ionospheric interactions) in polar regions and is also the best window for studying solar storms. Due to the rich morphological information in aurora images, people are paying more and more attention to studying aurora phenomena from the perspective of images. Recently, some machine learning and deep learning methods have been applied to this field and have achieved preliminary results. However, due to the limitations of these learning models, they still need to meet the requirements for the classification and prediction of auroral images regarding recognition accuracy. In order to solve this problem, this study introduces a convolutional neural network transformer solution based on vision transformers. Comparative experiments show that the proposed method can effectively improve the accuracy of aurora image classification, and its performance has exceeded that of state-of-the-art deep learning methods. The experimental results show that the algorithm presented in this study is an effective instrument for classifying auroral images and can provide practical assistance for related research.

VULNERABILITY OF ECONOMIC SECTORS TO GEOMAGNETIC STORMS AND MITIGATION STRATEGIES

Abstract. Solar storms and CMEs increase the amount of radiation that reaches the Earth and can perturb the magnetic shielding of the Earth. K and Kp indices quantify this perturbation by using magnetometers calibrated accordingly to the latitude of the geomagnetic observatory It is possible to contribute this measurement by establishing an observatory specialized in space weather studies. Many basic sectors are vulnerable against a strong solar storm unless necessary mitigation strategies are applied. Moreover, failure in taking an immediate precaution may have long-term effects which include economic destruction, irreversible health problems and weaknesses in national defence. In this work, the requirement such an observatory should have, the most 11 vulnerable sectors and corresponding mitigation strategies are discussed in detail. Turkey's seven geographical regions are examined to find the optimal location for the foundation of such a probable observatory. It is found that Central Anatolia is the ideal region and districts Konya/Karapinar and Konya/Çeltik are the possible candidates for such a location.

The Effect of Compression Induced Chorus Waves on 10–100 s eV Electron Precipitation

AbstractOn 7 January 2014, a solar storm erupted, which eventually compressed the Earth's magnetosphere leading to the generation of chorus waves. These waves enhanced local wave‐particle interactions and led to the precipitation of electrons from 10 s eV to 100 s keV. This paper shows observations of a low energy cutoff in the precipitation spectrum from Van Allen Probe B Helium Oxygen Proton Electron measurements. This low energy cutoff is well replicated by the predicted loss calculated from pitch angle diffusion coefficients from wave and plasma observations on Probe B. To our knowledge, this is the first time a single spacecraft has been used to demonstrate an accurate theoretical prediction for chorus wave‐induced precipitation and its low energy cutoff. The specific properties of the precipitating soft electron spectrum have implications for ionospheric activity, with the lowest energies mainly contributing to thermospheric and ionospheric upwelling, which influences satellite drag and ionospheric outflow.

Operational solar flare forecasting via video-based deep learning

Operational flare forecasting aims at providing predictions that can be used to make decisions, typically on a daily scale, about the space weather impacts of flare occurrence. This study shows that video-based deep learning can be used for operational purposes when the training and validation sets used for network optimization are generated while accounting for the periodicity of the solar cycle. Specifically, this article describes an algorithm that can be applied to build up sets of active regions that are balanced according to the flare class rates associated to a specific cycle phase. These sets are used to train and validate a long-term recurrent convolutional network made of a combination of a convolutional neural network and a long short-term memory network. The reliability of this approach is assessed in the case of two prediction windows containing the solar storms of March 2015, June 2015, and September 2017.

Examining the Economic Costs of the 2003 Halloween Storm Effects on the North Hemisphere Aviation Using Flight Data in 2019

AbstractSpace weather can impede normal aviation operations through communication blackouts, GNSS‐based navigation and surveillance failures, and elevated cosmic radiation, consequently resulting in necessary flight plan adjustments and considerable economic costs. Although space weather effects have been heavily emphasized, the literature on the economic effects on aviation is limited. In this study, we estimate the economic impacts from the perspective of air traffic management, assuming an extremely strong space weather event like the 2003 Halloween solar storm would occur in 2019 with a booming air transport industry in recent years. We find that (a) as the high‐frequency communication blackouts may lead to polar flight rerouting and cancellations, possible daily economic costs could range from €0.21 million to €2.20 million per day; (b) during the satellite navigation failure period in the continental United States, as aircraft utilizes ground navigation aids as a backup, the increased flying time and disrupted descent approach operations may lead to additional cost of €2.43 million; (c) a surveillance failure can reduce airspace capacity and increase the workload of air traffic controllers, resulting in fatigue and perhaps risking flight safety; (d) to prevent massive cosmic radiation exposure, the economic costs of flight cancellations can be from €2.77 million to €48.97 million, depending on the cosmic radiation dose limits for a given plan. Our study indicates that severe space weather events may briefly disrupt normal aviation operations and cause substantial economic losses if future aviation equipment and technology are fragile to its effects.

Linda Hogan’s Solar Storms: The Journey for the Search of Identity and the Natural Environment

Solar Storms is a persuasive work that conveys Linda Hogan’s philosophy on the relationship between man and nature. Hogan believes that man and nature are interdependent as well as interconnected. The western Christian belief which separates heaven and earth leads whites to neglect the natural environment. In the novel, Angela is the victim of the dominant white culture and their history. Her return to the tribal community and the subsequent journey to meet her mother vitalizes her Native American identity and historical consciousness. The hazardous journey with three old women and the effects of their storytelling offers Angela an opportunity to open her eyes to the significance of both social and environmental justice. Angela’s experience of the tribal tradition of mother-daughter relationships and her reconciliation with her mother awakens her identity through a new perspective of history in which she actively participates in the protest against the building of the dams and river diversions in order to save the lives of indigenous people as well as the natural environment.

Additional flight delays and magnetospheric–ionospheric disturbances during solar storms

Although the sun is really far away from us, some solar activities could still influence the performance and reliability of space-borne and ground-based technological systems on Earth. Those time-varying conditions in space caused by the sun are also called solar storm or space weather. It is known that aviation activities can be affected during solar storms, but the exact effects of space weather on aviation are still unclear. Especially how the flight delays, the top topic concerned by most people, will be affected by space weather has never been thoroughly researched. By analyzing huge amount of flight data (~ 4 × 106 records), for the first time, we quantitatively investigate the flight delays during space weather events. It is found that compared to the quiet periods, the average arrival delay time and 30-min delay rate during space weather events are significantly increased by 81.34% and 21.45% respectively. The evident negative correlation between the yearly flight regularity rate and the yearly mean total sunspot number during 22 years also confirms such correlation. Further studies show that the flight delay time and delay rate will monotonically increase with the geomagnetic field fluctuations and ionospheric disturbances. These results indicate that the interferences in communication and navigation during space weather events may be the most probable reason accounting for the increased flight delays. The above analyses expand the traditional field of space weather research and could also provide us with brand new views for improving the flight delay predications.

Short-term effects of solar storms in phytoplankton photosynthesis

The potential short-term influence of a solar radiation storm on microalgal photosynthesis is investigated. We focus on muons from the secondary cosmic rays at sea level, given their high penetrating power. According to NOAA’s classification of solar radiation storms, two kinds of solar storms are considered: moderate/strong and extreme. An exponential decay of muon fluxes down the water column and a direct proportionality between their penetrating power and energy are assumed. This allows obtaining a function of ionizing radiation to be embedded in a physical-mathematical model for photosynthesis previously modified by some of us to include particulate ionizing radiation. It is shown that moderate/strong solar radiation storms can cause a short-term depletion of photosynthesis of up to 61%, while this figure scales to 75% for extreme storms.

Solar Active Region Magnetogram Generation by Attention Generative Adversarial Networks

Learning the mapping of magnetograms and EUV images is important for understanding the solar eruption mechanism and space weather forecasting. Previous works are mainly based on the pix2pix model for full-disk magnetograms generation and obtain good performance. However, in general, we are more concerned with the magnetic field distribution in the active regions where various solar storms such as the solar flare and coronal mass ejection happen. In this paper, we fuse the self-attention mechanism with the pix2pix model which allows more computation resource and greater weight for strong magnetic regions. In addition, the attention features are concatenated by the Residual Hadamard Production (RHP) with the abstracted features after the encoder. We named our model as RHP-attention pix2pix. From the experiments, we can find that the proposed model can generate magnetograms with finer strong magnetic structures, such as sunspots. In addition, the polarity distribution of generated magnetograms at strong magnetic regions is more consistent with observed ones.

Storm time polar cap expansion: interplanetary magnetic field clock angle dependence

Abstract. It is well known that the polar cap, delineated by the open–closed field line boundary (OCB), responds to changes in the interplanetary magnetic field (IMF). In general, the boundary moves equatorward when the IMF turns southward and contracts poleward when the IMF turns northward. However, observations of the OCB are spotty and limited in local time, making more detailed studies of its IMF dependence difficult. Here, we simulate five solar storm periods with the coupled model consisting of the Open Geospace General Circulation Model (OpenGGCM) coupled with the Coupled Thermosphere Ionosphere Model (CTIM) and the Rice Convection Model (RCM), i.e., the OpenGGCM-CTIM-RCM, to estimate the location and dynamics of the OCB. For these events, polar cap boundary location observations are also obtained from Defense Meteorological Satellite Program (DMSP) precipitation spectrograms and compared with the model output. There is a large scatter in the DMSP observations and in the model output. Although the model does not predict the OCB with high fidelity for every observation, it does reproduce the general trend as a function of IMF clock angle. On average, the model overestimates the latitude of the open–closed field line boundary by 1.61∘. Additional analysis of the simulated polar cap boundary dynamics across all local times shows that the MLT of the largest polar cap expansion closely correlates with the IMF clock angle, that the strongest correlation occurs when the IMF is southward, that during strong southward IMF the polar cap shifts sunward, and that the polar cap rapidly contracts at all local times when the IMF turns northward.

Проявления геомагнитной активности (солнечные вспышки и магнитные бури) в изменении электротеллурических потенциалов по данным измерений на Южно-Сахалинском геофизическом полигоне

The results of the analysis of changes in electrotelluric potentials (ETP) during the observation of intense solar flare events and intense magnetic storms on Sakhalin are presented. The data were studied in the period from July 20 to October 12, 2023. The absence of characteristic changes in the ETP (integral amplification or attenuation of noise in the low-frequency region) depending on the presence or absence of a solar flare event is shown. At the same time, in some cases, the strongest flashes were found to coincide with the appearance of signals of the GUV type (Geyser type ULF Variation). For almost three months of observations, five cases of quasi-periodic GUV series have been identified, four of which coincide completely or partially with the times of solar flares and magnetic storms. It should be noted that earlier in the literature, the appearance of these signals was not correlated with any physical process. At the same time, the identification of such patterns is an integral part of extensive work on identifying predictive signs of earthquake preparation in the ETP.

Deriving Large Coronal Magnetic Loop Parameters Using LOFAR J Burst Observations

Large coronal loops around one solar radius in altitude are an important connection between the solar wind and the low solar corona. However, their plasma properties are ill-defined, as standard X-ray and UV techniques are not suited to these low-density environments. Diagnostics from type J solar radio bursts at frequencies above 10 MHz are ideally suited to understand these coronal loops. Despite this, J-bursts are less frequently studied than their type III cousins, in part because the curvature of the coronal loop makes them unsuited for using standard coronal density models. We used LOw-Frequency-ARray (LOFAR) and Parker Solar Probe (PSP) solar radio dynamic spectrum to identify 27 type III bursts and 27 J-bursts during a solar radio noise storm observed on 10 April 2019. We found that their exciter velocities were similar, implying a common acceleration region that injects electrons along open and closed magnetic structures. We describe a novel technique to estimate the density model in coronal loops from J-burst dynamic spectra, finding typical loop apex altitudes around 1.3 solar radius. At this altitude, the average scale heights were 0.36 solar radius, the average temperature was around 1 MK, the average pressure was 0.7~text{mdyn},text{cm}^{-2}, and the average minimum magnetic field strength was 0.13 G. We discuss how these parameters compare with much smaller coronal loops.

Assessment of the radiation risk at flight altitudes for an extreme solar particle storm of 774 AD

Intense solar activity can lead to an acceleration of solar energetic particles and accordingly increase in the complex radiation field at commercial aviation flight altitudes. We considered here the strongest ever reported event, namely that of 774 AD registered on the basis of cosmogenic-isotope measurements, and computed the ambient dose at aviation altitude(s). Since the spectrum of solar protons during the 774 AD event cannot be directly obtained, as a first step, we derived the spectra of the solar protons during the ground level enhancement (GLE) #5 on 23 February 1956, the strongest event observed by direct measurements, which was subsequently scaled to the size of the 774 AD event and eventually used as input to the corresponding radiation model. The GLE #5 was considered a conservative approach because it revealed the hardest-ever derived energy spectrum. The global map of the ambient dose was computed under realistic data-based reconstruction of the geomagnetic field during the 774 AD epoch, based on paleomagnetic measurements. A realistic approach on the basis of a GLE #45 on 24 October 1989 was also considered, that is by scaling an event with softer spectra and lower particle fluxes compared to the GLE #5. The altitude dependence of the event-integrated dose at altitudes from 30 kft to 50 kft (9.1–15.2 km) was also computed for both scenarios. Our study of the radiation effects during the extreme event of 774 AD gives the necessary basis to be used as a reference to assess the worst-case scenario for a specific threat, that is radiation dose at flight altitudes.

Optical Design of a High Flux Setup in the Extreme Ultraviolet

Extreme ultraviolet (XUV) light applications are still a very promising field that was heavily enlivened by the definition of the new wavelength for semiconductor lithography within the XUV range. But the detection of XUV light is also important for the exploration in the field of space science (i.e., monitoring the formation and evolution of solar storms) and high-energy physics (i.e., dark matter detection). The advancement of this technology mainly depends on the performance optimization of XUV sources, optical systems and related photodetectors. In this work, the optical design of a high flux XUV setup was simulated and defined to optimise the beam path which is the backbone of the initial evaluation process for the characterisation of luminescent materials under XUV irradiation. Additionally, the paper focused on the conceptualisation and realisation of the experimental setup as well as the alignment of the optical components and the detector calibration.

No signature of extreme solar energetic particle events in high-precision 14C data from the Alaskan tree for 1844–1876 CE

Cosmogenic nuclides – 14C from tree rings and 10Be and 36Cl from ice cores serve as an effective proxy for past extreme solar energetic particle (SEP) events. After identifying the first signature of an extreme SEP event in 774 CE, several candidates have been found in these proxy archives, such as 993 CE, 660 BCE, and 7176 BCE. Their magnitudes have been estimated to be tens of times larger than that of the largest SEP event ever observed since the 1950s. Although a detailed survey of such extreme SEP events is ongoing, the detection of intermediate-sized SEP events that bridge the gap between modern observations and extreme events detected in cosmogenic nuclides has not progressed sufficiently, primarily because of the uncertainties in cosmogenic nuclide data. In this study, we measured 14C concentrations in tree rings in the 19th century (1844–1876 CE) to search for any increases in 14C concentrations corresponding to intermediate-size extreme SEP events. We utilized Alaskan tree-ring samples cut into early and latewoods to suppress the potential seasonal variations in intra-annual 14C data. Notably, no significant 14C variations were observed between early and latewoods (0.0 ± 0.3‰), and the annual resolution 14C data series displayed an error of ~ 0.8‰. Over the entire study period, no significant increase in 14C concentrations characterized by other candidates of extreme SEP events such as the 774 CE event was detected in the annual 14C data. The present result imposes a constraint on the SEP fluence when the largest class of recorded solar storms occurred (especially those in 1859 CE and 1872 CE).

Influence of cosmic rays on environmental ecosystems

AbstractIt is presented a model to consider the effect of high energy cosmic rays on three types of terrestrial ecosystem: coastal macroalgae, lakes, and forests. The model includes insights into muon dosimetry based in the stopping power of muons while traveling in water and soft biological tissues. It is shown that a muon from secondary cosmic rays could deposit in cells an energy much higher than that deposited by ultraviolet photons coming from the Sun. Consequently, drastic short‐term reductions of photosynthesis are obtained if energetic astrophysical phenomena such as gamma ray bursts and neutron star mergers happen in our astronomical neighborhood, while radiational solar storms also cause considerable inhibition of photosynthesis.

Analysis of Proton Flux with Solar Wind Parameters, Symmetric (SYM) and Asymmetric (ASY) H-indices

After significant solar eruptions, protons are fired at extremely high speeds, sometimes reaching several thousand kilometer per second (km/s), resulting in solar radiation storms. Solar particle events can be found all around the heliosphere. The cross-correlation analysis along with time series analysis is used to look at how the solar wind and proton flux are related. The parameters used in this work are flux of Solar Energetic Protons (SEP) ranging from >10 MeV to >60 MeV along with speed, density, and pressure of the solar wind parameters. The findings indicate that proton flux (E>10 MeV) shows significant changes just before the storm while flux (E>30 MeV) and (E>60 MeV) doesn’t correlate with solar parameters although during quiet day no significant changes were observed. These finding suggested that SEP can be used as precursor of CME driven storms.

Physical effects of geocosmic storms. Part 3

A review of the results of observing the effects of the most interesting ionospheric and magnetic storms that took place in 2018–2019 is presented. Ionospheric and magnetic storms are considered as components of geocosmic storms. In accordance with the systemic paradigm of L.F. Chernogor, a geocosmic storm is a complex of processes caused by a solar storm, reduced to synergistically interacting storms in a magnetic field (magnetic storm), ionosphere (ionospheric storm), atmosphere (atmospheric storm) and electric field atmospheric-ionospheric-magnetospheric origin (electric storm). Using the analysis of specific magnetic and ionospheric storms as an example, it is shown that, in addition to general patterns, each storm has its own individual characteristics. The expediency of a comprehensive study of the manifestations of each new geocosmic storm has been confirmed. A wide variety of ionospheric and magnetic storms have been confirmed. A weak magnetic storm is not necessarily accompanied by a weak ionospheric storm, while a strong one is accompanied by a strong one. An ionospheric storm can be single-phase (positive or negative) or multi-phase, when the phases alternate. Storms can last from several hours to many days. Storms are accompanied by both aperiodic and quasi-periodic variations. The latter are caused by atmospheric gravity waves (periods 10–180 min) and infrasound (period less than 5 min). Of all the components of a geocosmic storm, except for academic ones, ionospheric storms are of the greatest practical interest. It is these storms that limit the potential characteristics of radio systems for various purposes (telecommunications, radio navigation, radar, remote radio sounding, radio astronomy). It is shown that ionospheric storms are accompanied by the appearance of multipath, broadening of the Doppler spectra (and even their collapse), significant aperiodic and quasi-periodic variations in the Doppler frequency shift, the amplitude of radio signals, and the trajectories of radio waves in the decameter range. In particular, the height of reflection of radio waves changed during ionospheric storms by 50–180 km. The electron concentration during moderate negative ionospheric storms decreased up to 2 times, and during moderate positive ionospheric storms it increased up to 3 times. Under the action of atmospheric gravity waves, the relative amplitude of quasi-periodic variations in the electron concentration reached 50%, and due to low-frequency infrasound, it did not exceed 0.7%.

Online Monitoring of Magnetometer Signals for Estimating Induced Electric Fields in Power Systems

Powerful solar storms emit plasma that may travel towards the earth. Interactions between the plasma and the earth magnetic field cause geomagnetic disturbances (GMDs), which in turn induce quasi-dc voltage along long conductors in power systems. The induced electric field can be calculated by magnetic field data measured by magnetometers at several stations. In this paper, a set of online processing methods are proposed to improve signal to noise ratio (SNR) of the calculated electric field signals. In the first step, magnetic field signals are denoised inside a sliding window by a wavelet transform. The sliding window width is optimized and the introduced transients are compensated. Time derivative of magnetic field signals are estimated by a continuous wavelet transform in the next step, and the size of the sliding window is minimized for optimal computational speed. Ultimately, a method is proposed to further enhance the SNR by updating the previously processed data points. The magnetic and electric field signals obtained by the online and offline processing methods are compared with each other. It is demonstrated that the online processing methods can decrease the noises to a level comparable to that obtained by the offline methods. The results of this study can be implemented to protect key elements of power systems against severe solar storms.

LSTM-Based Deep Learning Methods for Prediction of Earthquakes Using Ionospheric Data

The ionosphere may play an essential role in the atmosphere and earth. Solar flares due to coronal mass ejection, seismic movements, and geomagnetic activity cause deviations in the ionosphere. The main parameter for investigating the structure of the ionosphere is Total Electron Content (TEC). TEC values obtained from GPS stations are a powerful technique for analyzing the ionospheric response to earthquakes and solar storms. This article analyzes the relations between earthquakes and TEC data to detect earthquakes. Our goal is to propose a prediction model to detect earthquakes in previous days. The ionospheric variability during moderate and severe earthquake events of varying strengths for 2012-2019 is discussed in this paper. The proposed models use LSTM-based (Long Short-Term Memory) deep learning models to classify earthquake days by analyzing TEC values of the last days. The LSTM-Based prediction models are compared against the SVM (Support Vector Machine), LDA (Linear Discriminant Analysis) classifier and Random Forest classifier to evaluate the proposed models based on earthquake prediction. The results reveal that the proposed models improve in detecting the earthquakes at an accuracy rate of about 0.82 and can be used as a successful tool for detecting earthquakes based on the previous days.