Space Weather Prediction Research Articles

Predicting the Arrival Time of an Interplanetary Shock Based on DSRT Spectrum Observations for the Corresponding Type II Radio Burst and a Blast Wave Theory

Since fast head-on coronal mass ejections and their associated shocks represent potential hazards to the space environment of the Earth and even other planets, forecasting the arrival time of the corresponding interplanetary shock is a priority in space weather research and prediction. Based on the radio spectrum observations of the 16-element array of the Daocheng Solar Radio Telescope (DSRT), the flagship instrument of the Meridian Project of China, during its construction, this study determines the initial shock speed of a type II solar radio burst on 2022 April 17 from its drifting speed in the spectrum. Assuming that the shock travels at a steady speed during the piston-driven phase (determined from the X-ray flux of the associated flare) and then propagates through interplanetary space as a blast wave, we estimate the propagation and arrival time of the corresponding shock at the orbit of the Solar Terrestrial Relations Observatory-A (STEREO-A). The prediction shows that the shock will reach STEREO-A at 14:31:57 UT on 2022 April 19. The STEREO-A satellite detected an interplanetary shock at 13:52:12 UT on the same day. The discrepancy between the predicted and observed arrival time of the shock is only 0.66 hr. The purpose of this paper is to establish a general method for predicting the shock’s propagation and arrival time from this example, which will be utilized to predict more events in the future based on the observations of ground-based solar radio spectrometers or telescopes like DSRT.

Using Solar Orbiter as an Upstream Solar Wind Monitor for Real Time Space Weather Predictions

AbstractCoronal mass ejections (CMEs) can create significant disruption to human activities and systems on Earth, much of which can be mitigated with prior warning of the upstream solar wind conditions. However, it is currently extremely challenging to accurately predict the arrival time and internal structure of a CME from coronagraph images alone. In this study, we take advantage of a rare opportunity to use Solar Orbiter, at 0.5 au upstream of Earth, as an upstream solar wind monitor. In combination with low‐latency images from STEREO‐A, we successfully predicted the arrival time of two CME events before they reached Earth. Measurements at Solar Orbiter were used to constrain an ensemble of simulation runs from the ELEvoHI model, reducing the uncertainty in arrival time from 10.4 to 2.5 hr in the first case study. There was also an excellent agreement in the Bz profile between Solar Orbiter and Wind spacecraft for the second case study, despite being separated by 0.5 au and 10° longitude. The opportunity to use Solar Orbiter as an upstream solar wind monitor will repeat once a year, which should further help assess the efficacy upstream in‐situ measurements in real time space weather forecasting.

Three-dimensional Velocity Fields of the Solar Filament Eruptions Detected by CHASE

The eruption of solar filaments, also known as prominences appearing off limb, is a common phenomenon in the solar atmosphere. It ejects massive plasma and high-energy particles into interplanetary space, disturbing the solar-terrestrial environment. It is vital to obtain the three-dimensional velocity fields of erupting filaments for space-weather predictions. We derive the three-dimensional kinematics of an off-limb prominence and an on-disk filament, respectively, using the full-disk spectral and imaging data detected by the Chinese Hα Solar Explorer (CHASE). It is found that both the prominence and the filament experience a fast semicircle-shaped expansion at first. The prominence keeps propagating outward with an increasing velocity until escaping successfully, with the south leg of the prominence finally moving back to the Sun in a swirling manner. For the filament, the internal plasma falls back to the Sun in a counterclockwise rotation in the late ejection, matching the failed eruption without a coronal mass ejection. During the eruptions, both the prominence and the filament show material splitting along the line-of-sight direction, revealed by the bimodal Hα spectral profiles. For the prominence, the splitting begins at the top and gradually spreads to almost the whole prominence with a fast blueshift component and a slow redshift component. The material splitting in the filament is more fragmental. As shown by the present results, the CHASE full-disk spectroscopic observations make it possible to systematically study the three-dimensional kinematics of solar filament eruptions.

Analysis of the Motion of Solar Wind (SW) and Other Solar Activities Impacts on the Earth

Solar wind (SW), the powerful outflow of charged particles from the Sun, has a significant impact on Earth's magnetosphere, ionosphere, and atmospheric processes. This paper examines the motion of SW and its effects on Earth, focusing on changes such as slow and fast SWs and their impacts on geomagnetic storms, ionospheric disturbances, and precipitation patterns. Observations from spacecraft, including the Solar and Heliospheric Observatory (SOHO), the Advanced Composition Explorer (ACE), and the Wind spacecraft, have provided valuable data on solar wind properties. However, there are limitations in spatial coverage and the measurement of internal SW properties. Accurately predicting SW behavior remains a challenge. Future research should focus on developing advanced observational techniques, utilizing multiple spacecraft, and improving modeling and data analysis approaches. The forthcoming expeditions, specifically the Solar Orbiter and Parker Solar Probe, are predicted to provide novel understandings into solar activity. Enhancing our understanding of solar activity and improving space weather predictions are crucial for mitigating the potential adverse effects of SW on electricity, communication, and other human activities. This research contributes to advancements in space weather forecasting and the reliable and safe operation of our technological infrastructure.

Correlation between Supergranular Lane Widths and Sunspot Number; A Simple Way to Predict the Amplitude of Sunspot Cycle

The network structure seen in the solar images is the outline of supergranulation, which is the large-scale convection in the Sun with a size of about 30 Mm and a lifetime of 24 hr. We have obtained the supergranulation lane widths from the autocorrelation function of image windows from the Ca ii K spectroheliograms. The images are obtained from the 100 yr Kodaikanal data, which contains information on more than nine solar cycles. The lane widths are known to show a positive correlation with the sunspot number. It is now found that the lane widths, obtained near the mid-latitudes during the sunspot cycle minima, are strongly correlated to the following sunspot number maxima. A straight-line fit adequately describes the variation. It is also found that the correlation is weak or insignificant at other times. The strong correlation of the two parameters thus provides a simple way to predict the maximum sunspot number about 4–5 yr in advance. The results are important in space weather predictions and solar irradiance variations.

Electricity consumption in Finland influenced by climate effects of energetic particle precipitation

It is known that electricity consumption in many cold Northern countries depends greatly on prevailing outdoor temperatures especially during the winter season. On the other hand, recent research has demonstrated that solar wind driven energetic particle precipitation from space into the polar atmosphere can influence the stratospheric polar vortex and tropospheric weather patterns during winter. These changes are significant, e.g., in Northern Europe, especially in Finland. In this study we demonstrate that geomagnetic activity, as a proxy of energetic particle precipitation, significantly influences Finland’s average temperature and total wintertime electricity consumption in Finland. This influence is only seen when the prevailing equatorial stratospheric winds, so called QBO winds, are easterly. The results demonstrate a previously unrecognized societal influence of space weather, and imply that long-term energy consumption forecasts could potentially be improved by considering long-term space weather predictions.

Quantifying the Consistency and Characterizing the Confidence of Coronal Holes Detected by Active Contours Without Edges (ACWE)

Coronal Holes (CHs) are regions of open magnetic-field lines, resulting in high-speed solar wind. Accurate detection of CHs is vital for space-weather prediction. This paper presents an intramethod ensemble for coronal-hole detection based on the Active Contours Without Edges (ACWE) segmentation algorithm. The purpose of this ensemble is to develop a confidence map that defines, for all ondisk regions of a solar extreme ultraviolet (EUV) image, the likelihood that each region belongs to a CH based on that region’s proximity to, and homogeneity with, the core of identified CH regions. By relying on region homogeneity, and not intensity (which can vary due to various factors, including line-of-sight changes and stray light from nearby bright regions), to define the final confidence of any given region, this ensemble is able to provide robust, consistent delineations of the CH regions. Using the metrics of global consistency error (GCE), local consistency error (LCE), intersection over union (IOU), and the structural similarity index measure (SSIM), the method is shown to be robust to different spatial resolutions maintaining a median IOU >0.75\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$>0.75$\\end{document} and minimum SSIM >0.93\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$>0.93$\\end{document} even when the segmentation process was performed on an EUV image decimated from 4096×4096\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$4096\ imes 4096$\\end{document} pixels down to 512×512\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$512\ imes 512$\\end{document} pixels. Furthermore, using the same metrics, the method is shown to be robust across short timescales, producing segmentation with a mean IOU of 0.826 from EUV images taken at a 1-h cadence, and showing a smooth decay in similarity across all metrics as a function of time, indicating self-consistent segmentations even when corrections for exposure time have not been applied to the data. Finally, the accuracy of the segmentations and confidence maps are validated by considering the skewness (i.e., unipolarity) of the underlying magnetic field.

Multipoint Observations of the Dynamics at an ICME Sheath–Ejecta Boundary

The radial evolution of interplanetary coronal mass ejections (ICMEs) is dependent on their interaction with the ambient medium, which causes ICME erosion and affects their geoefficiency. Here, an ICME front boundary, which separates the confined ejecta from the mixed, interacted sheath–ejecta plasma upstream, is analyzed in a multipoint study examining the ICME at 1 au on 2020 April 20. A bifurcated current sheet, highly filamented currents, and a two-sided jet were observed at the boundary. The two-sided jet, which was recorded for the first time for a magnetic shear angle <40°, implies multiple (patchy) reconnection sites associated with the ICME erosion. The reconnection exhaust exhibited fine structure, including multistep magnetic field rotation and localized structures that were measured only by separate Cluster spacecraft with the mission inter-spacecraft separation of 0.4–1.6 R E. The mixed plasma upstream of the boundary with a precursor at 0.8 au lacked coherency at 1 au and exhibited substantial variations of southward magnetic fields over radial (transverse) distances of 41–237 R E (114 R E). This incoherence demonstrates the need for continuous (sub)second-resolution plasma and field measurements at multiple locations in the solar wind to adequately address the spatiotemporal structure of ICMEs and to produce accurate space weather predictions.

Scope and limitations of ad hoc neural network reconstructions of solar wind parameters

Context.Solar wind properties are determined by the conditions of their solar source region and transport history. Solar wind parameters, such as proton speed, proton density, proton temperature, magnetic field strength, and the charge state composition of oxygen, are used as proxies to investigate the solar source region of the solar wind. The solar source region of the solar wind is relevant to both the interaction of this latter with the Earth’s magnetosphere and to our understanding of the underlying plasma processes, but the effect of the transport history of the wind is also important. The transport and conditions in the solar source region affect several solar wind parameters simultaneously. Therefore, the typically considered solar wind properties (e.g., proton density and oxygen charge-state composition) carry redundant information. Here, we are interested in exploring this redundancy.Aims.The observed redundancy could be caused by a set of hidden variables that determine the solar wind properties. We test this assumption by determining how well a (arbitrary, non-linear) function of four of the selected solar wind parameters can model the fifth solar wind parameter. If such a function provided a perfect model, then this solar wind parameter would be uniquely determined from hidden variables of the other four parameters and would therefore be redundant. If no reconstruction were possible, this parameter would be likely to contain information unique to the parameters evaluated here. In addition, isolating redundant or unique information contained in these properties guides requirements for in situ measurements and development of computer models. Sufficiently accurate measurements are necessary to understand the solar wind and its origin, to meaningfully classify solar wind types, and to predict space weather effects.Methods.We employed a neural network as a function approximator to model unknown, arbitrary, non-linear relations between the considered solar wind parameters. This approach is not designed to reconstruct the temporal structure of the observations. Instead a time-stable model is assumed and each point of measurement is treated separately. This approach is applied to solar wind data from the Advanced Composition Explorer (ACE). The neural network reconstructions are evaluated in comparison to observations, and the resulting reconstruction accuracies for each reconstructed solar wind parameter are compared while differentiating between different solar wind conditions (i.e., different solar wind types) and between different phases in the solar activity cycle. Therein, solar wind types are identified according to two solar-wind classification schemes based on proton plasma properties.Results.Within the limits defined by the measurement uncertainties, the proton density and proton temperature can be reconstructed well. Each parameter was evaluated with multiple criteria. Overall proton speed was the parameter with the most accurate reconstruction, while the oxygen charge-state ratio and magnetic field strength were most difficult to recover. We also analysed the results for different solar wind types separately and found that the reconstruction is most difficult for solar wind streams preceding and following stream interfaces.Conclusions.For all considered solar wind parameters, but in particular the proton density, proton temperature, and the oxygen charge-state ratio, parameter reconstruction is hindered by measurement uncertainties. The proton speed, while being one of the easiest to measure, also seems to carry the highest degree of redundancy with the combination of the four other solar wind parameters. Nevertheless, the reconstruction accuracy for the proton speed is limited by the large measurement uncertainties on the respective input parameters. The reconstruction accuracy of sector reversal plasma is noticeably lower than that of streamer belt or coronal hole plasma. We suspect that this is a result of the effect of stream interaction regions, which strongly influence the proton plasma properties and are typically assigned to sector reversal plasma. The fact that the oxygen charge-state ratio –a non-transport-affected property– is difficult to reconstruct may imply that recovering source-specific information from the transport-affected proton plasma properties is challenging. This underlines the importance of measuring the heavy ion charge-state composition.

The correlation between equatorial electrojet and equatorial ionisation anomaly over the east african region during the solar minimum period 2008-2009

This study analyzed the correlation between the equatorial electrojet (EEJ) and the occurrence of equatorial ionisation anomaly (EIA) over the East African region. The study was carried out during both geomagnetically quiet and disturbed conditions when Kp index values were &lt; 2+ and &gt; 5+, respectively. The EEJ data were obtained using a pair of magnetometers located at Adis Ababa (geographic 9.04°N, 38.77°E, geomagnetic 0.17°N, 110.47°E) and Adigrat (geographic 14.281°N, 39.46°E, geomagnetic 6.0°N, 111.06°E), both in Ethiopia while the EIA data were derived from the total electron content (TEC) data that were obtained using a set of Global Navigation Satellite System (GNSS) receivers within the East African region. The data used were for the years 2008 and 2009. The TEC data over the crest of EIA were divided by those over the trough to quantify EIA strength over the region. The EEJ intensity was estimated from the difference in the horizontal component of the geomagnetic field observed by the pair of magnetometers. The results during quiet geomagnetic conditions showed that peak values of EEJ which range from 48nT - 110nT occurred between 10:00 and 14:00 LT. The EIA’s peak which varies from 1.20 to 1.45 occurs between 20:00 to 22:00 LT. The correlation coefficients were found to vary from moderate (0.58) to strongest (0.74). During geomagnetically disturbed conditions, the correlation coefficient ranges from 0.28 to 0.45. The increased eastward electric field and photo-ionization on TEC are responsible for the strong link between EEJ and EIA. This study reveals the trend in the variation of the strength of EEJ and EIA over the East African region which can be used as a basis for developing regional models to forecast or nowcast scintillations and the ionospheric space weather prediction over this region.

CASHEW, A RESPONSE TO THE COCOA CRISIS IN DIMBOKRO IN THE N'ZI REGION? (IVORY COAST)

As the Sun progresses through its solar cycle and its activity increases, a greater number of sunspots occur, and solar fluxes become more intense. The change in solar activity is related to the change in electron and ion density of the ionosphere. Studying this relation is very important in terms of space plasma studies and space weather predictions, which play a significant role in radio and satellite communication as well as GPS navigation. In this paper, we are presenting the sunspot and solar flux data for solar cycle 24 (year 2008-2020). We are using C/NOFS satellite data to understand the variationof ion densities of the upper atmosphere with solar cycle 24. Our analysis shows decrease of H+ density by a factor of 10 and increase of O+ density by a factor of 10 during solar maxima. This data set can be used as a framework for future advancement in empirical modelling of regional and global ion density of the ionosphere.

Homogenizing SOHO/EIT and SDO/AIA 171 Å Images: A Deep-learning Approach

Extreme-ultraviolet (EUV) images of the Sun are becoming an integral part of space weather prediction tasks. However, having different surveys requires the development of instrument-specific prediction algorithms. As an alternative, it is possible to combine multiple surveys to create a homogeneous data set. In this study, we utilize the temporal overlap of Solar and Heliospheric Observatory Extreme ultraviolet Imaging Telescope and Solar Dynamics Observatory Atmospheric Imaging Assembly 171 Å surveys to train an ensemble of deep-learning models for creating a single homogeneous survey of EUV images for two solar cycles. Prior applications of deep learning have focused on validating the homogeneity of the output while overlooking the systematic estimation of uncertainty. We use an approach called “approximate Bayesian ensembling” to generate an ensemble of models whose uncertainty mimics that of a fully Bayesian neural network at a fraction of the cost. We find that ensemble uncertainty goes down as the training set size increases. Additionally, we show that the model ensemble adds immense value to the prediction by showing higher uncertainty in test data that are not well represented in the training data.

Erratum: Seven sisters: a mission to study fundamental plasma physical processes in the solar wind and a pathfinder to advance space weather prediction

Erratum: Seven sisters: a mission to study fundamental plasma physical processes in the solar wind and a pathfinder to advance space weather prediction

Heliospheric 3-D MHD ENLIL simulations of multi-CME and multi-spacecraft events

Interpreting multi-spacecraft heliospheric observations of the evolving solar wind (SW) streams with propagating and interacting coronal mass ejections (CMEs) is challenging. Numerical simulations can provide global context and suggest what may and may not be observed. The heliospheric three-dimensional (3D) magnetohydrodynamic (MHD) ENLIL model can provide a near-real-time prediction of heliospheric space weather, and it is used at NASA Community Coordinated Modeling Center (CCMC), NOAA Space Weather Prediction Center (SWPC), and UK Meteorological Office (MetOffice). However, this version does not show its full potential, especially in the case of multi-CME events observed by various spacecraft. We describe tools developed to interpret remote observations and in-situ measurements better and apply them to multi-CME events observed by ACE, STEREO-A, Parker Solar Probe (PSP), BepiColombo, and Solar Orbiter. We present some results on 1) global structures of the SW speed and density at the ecliptic, 2) the evolution of SW parameters at the spacecraft, 3) magnetic field connectivity at the spacecraft, 4) automatic detection of shock parameters and alert plots, and 5) synthetic white-light (WL) imaging. This paper is not on model initialization or analyzing specific CME events, but it describes features not used at space weather prediction centers and provided by NASA/CCMC Run-On-Request service. This paper advertises new tools and shows their benefits when applied to selected heliospheric space weather events observed at near-Earth, PSP, Solar Orbiter, and STEREO-A spacecraft.

SOLAR ACTIVITY VARIATION AND ITS EFFECTS ON IONOSPHERIC ION DENSITIES

As the Sun progresses through its solar cycle and its activity increases, a greater number of sunspots occur, and solar fluxes become more intense. The change in solar activity is related to the change in electron and ion density of the ionosphere. Studying this relation is very important in terms of space plasma studies and space weather predictions, which play a significant role in radio and satellite communication as well as GPS navigation. In this paper, we are presenting the sunspot and solar flux data for solar cycle 24 (year 2008-2020). We are using C/NOFS satellite data to understand the variationof ion densities of the upper atmosphere with solar cycle 24. Our analysis shows decrease of H+ density by a factor of 10 and increase of O+ density by a factor of 10 during solar maxima. This data set can be used as a framework for future advancement in empirical modelling of regional and global ion density of the ionosphere.

Solar Energetic Particle Events Detected in the Housekeeping Data of the European Space Agency's Spacecraft Flotilla in the Solar System

AbstractDespite the growing importance of planetary Space Weather forecasting and radiation protection for science and robotic exploration and the need for accurate Space Weather monitoring and predictions, only a limited number of spacecraft have dedicated instrumentation for this purpose. However, every spacecraft (planetary or astronomical) has hundreds of housekeeping sensors distributed across the spacecraft, some of which can be useful to detect radiation hazards produced by solar particle events. In particular, energetic particles that impact detectors and subsystems on a spacecraft can be identified by certain housekeeping sensors, such as the Error Detection and Correction (EDAC) memory counters, and their effects can be assessed. These counters typically have a sudden large increase in a short time in their error counts that generally match the arrival of energetic particles to the spacecraft. We investigate these engineering datasets for scientific purposes and perform a feasibility study of solar energetic particle event detections using EDAC counters from seven European Space Agency Solar System missions: Venus Express, Mars Express, ExoMars‐Trace Gas Orbiter, Rosetta, BepiColombo, Solar Orbiter, and Gaia. Six cases studies, in which the same event was observed by different missions at different locations in the inner Solar System are analyzed. The results of this study show how engineering sensors, for example, EDAC counters, can be used to infer information about the solar particle environment at each spacecraft location. Therefore, we demonstrate the potential of the various EDAC to provide a network of solar particle detections at locations where no scientific observations of this kind are available.

Importance of Stream Interaction Regions to Space Weather and Energetic Particle Predictions

Importance of Stream Interaction Regions to Space Weather and Energetic Particle Predictions

Space Weather Modeling and Prediction for Intermediate Time-scales

Space Weather Modeling and Prediction for Intermediate Time-scales

Statistical Study of the Correlation between Solar Energetic Particles and Properties of Active Regions

The flux of energetic particles originating from the Sun fluctuates during the solar cycles. It depends on the number and properties of active regions (ARs) present in a single day and associated solar activities, such as solar flares and coronal mass ejections. Observational records of the Space Weather Prediction Center NOAA enable the creation of time-indexed databases containing information about ARs and particle flux enhancements, most widely known as solar energetic particle (SEP) events. In this work, we utilize the data available for solar cycles 21–24 and the initial phase of cycle 25 to perform a statistical analysis of the correlation between SEPs and properties of ARs inferred from the McIntosh and Hale classifications. We find that the complexity of the magnetic field, longitudinal location, area, and penumbra type of the largest sunspot of ARs are most correlated with the production of SEPs. It is found that most SEPs (≈60%, or 108 out of 181 considered events) were generated from an AR classified with the “k” McIntosh subclass as the second component, and these ARs are more likely to produce SEPs if they fall in a Hale class containing a δ component. The resulting database containing information about SEP events and ARs is publicly available and can be used for the development of machine learning models to predict the occurrence of SEPs.

Interplanetary scintillation observation and space weather modelling

Interplanetary scintillation (IPS) refers to random fluctuations in radio intensity of distant small-diameter celestial object, over time periods of the order of 1 s. The scattering and scintillation of emergent radio waves are ascribed to turbulent density irregularities transported by the ubiquitous solar wind streams. The spatial correlation length of density irregularities and the Fresnel radius of radio diffraction are two key parameters in determining the scintillation pattern. Such a scintillation pattern can be measured and correlated between multi-station radio telescopes on the Earth. Using the “phase-changing screen” scenario based on the Born approximation, the bulk-flow speed and turbulent spectrum of the solar wind streams can be extracted from the single-station power spectra fitting and the multi-station cross-correlation analysis. Moreover, a numerical computer-assisted tomography (CAT) model, iteratively fit to a large number of IPS measurements over one Carrington rotation, can be used to reconstruct the global velocity and density structures in the inner heliosphere for the purpose of space weather modelling and prediction. In this review, we interpret the underlying physics governing the IPS phenomenon caused by the solar wind turbulence, describe the power spectrum and cross correlation of IPS signals, highlight the space weather application of IPS-CAT models, and emphasize the significant benefits from international cooperation within the Worldwide IPS Stations (WIPSS) network.