Showing papers on "Space weather published in 2022"

Extreme solar events

Abstract: We trace the evolution of research on extreme solar and solar-terrestrial events from the 1859 Carrington event to the rapid development of the last twenty years. Our focus is on the largest observed/inferred/theoretical cases of sunspot groups, flares on the Sun and Sun-like stars, coronal mass ejections, solar proton events, and geomagnetic storms. The reviewed studies are based on modern observations, historical or long-term data including the auroral and cosmogenic radionuclide record, and Kepler observations of Sun-like stars. We compile a table of 100- and 1000-year events based on occurrence frequency distributions for the space weather phenomena listed above. Questions considered include the Sun-like nature of superflare stars and the existence of impactful but unpredictable solar "black swans" and extreme "dragon king" solar phenomena that can involve different physics from that operating in events which are merely large.

Extreme solar events

Abstract: We trace the evolution of research on extreme solar and solar-terrestrial events from the 1859 Carrington event to the rapid development of the last twenty years. Our focus is on the largest observed/inferred/theoretical cases of sunspot groups, flares on the Sun and Sun-like stars, coronal mass ejections, solar proton events, and geomagnetic storms. The reviewed studies are based on modern observations, historical or long-term data including the auroral and cosmogenic radionuclide record, and Kepler observations of Sun-like stars. We compile a table of 100- and 1000-year events based on occurrence frequency distributions for the space weather phenomena listed above. Questions considered include the Sun-like nature of superflare stars and the existence of impactful but unpredictable solar "black swans" and extreme "dragon king" solar phenomena that can involve different physics from that operating in events which are merely large.

Signatures of Equatorial Plasma Bubbles and Ionospheric Scintillations from Magnetometer and GNSS Observations in the Indian Longitudes during the Space Weather Events of Early September 2017

Abstract: Scintillation due to ionospheric plasma irregularities remains a challenging task for the space science community as it can severely threaten the dynamic systems relying on space-based navigation services. In the present paper, we probe the ionospheric current and plasma irregularity characteristics from a latitudinal arrangement of magnetometers and Global Navigation Satellite System (GNSS) stations from the equator to the far low latitude location over the Indian longitudes, during the severe space weather events of 6–10 September 2017 that are associated with the strongest and consecutive solar flares in the 24th solar cycle. The night-time influence of partial ring current signatures in ASYH and the daytime influence of the disturbances in the ionospheric E region electric currents (Diono) are highlighted during the event. The total electron content (TEC) from the latitudinal GNSS observables indicate a perturbed equatorial ionization anomaly (EIA) condition on 7 September, due to a sequence of M-class solar flares and associated prompt penetration electric fields (PPEFs), whereas the suppressed EIA on 8 September with an inverted equatorial electrojet (EEJ) suggests the driving disturbance dynamo electric current (Ddyn) corresponding to disturbance dynamo electric fields (DDEFs) penetration in the E region and additional contributions from the plausible storm-time compositional changes (O/N2) in the F-region. The concurrent analysis of the Diono and EEJ strengths help in identifying the pre-reversal effect (PRE) condition to seed the development of equatorial plasma bubbles (EPBs) during the local evening sector on the storm day. The severity of ionospheric irregularities at different latitudes is revealed from the occurrence rate of the rate of change of TEC index (ROTI) variations. Further, the investigations of the hourly maximum absolute error (MAE) and root mean square error (RMSE) of ROTI from the reference quiet days’ levels and the timestamps of ROTI peak magnitudes substantiate the severity, latitudinal time lag in the peak of irregularity, and poleward expansion of EPBs and associated scintillations. The key findings from this study strengthen the understanding of evolution and the drifting characteristics of plasma irregularities over the Indian low latitudes.

SpaceX—Sailing Close to the Space Weather?

Abstract: The 3 February 2022 launch of 49 of SpaceX's Starlink satellites has provided a fascinating example of how even modest space weather can have significant practical and financial consequences. Enhanced atmospheric drag associated with a minor geomagnetic storm led to the loss of the majority of the 49 launched satellites. Although the 36th launch by SpaceX in the past 3 years, it was the first that experienced stormy space weather. We expect more stormy space weather as Solar Cycle 25 ramps up toward its peak expected in 2025. A subsequent Starlink launch on 21 February used a higher initial orbit at 300 km, reducing the payload from 49 to 46 satellites, and can be considered an agile response to the space weather losses experienced 2 weeks earlier. Lessons to be learned by the space industry and the space weather community are discussed, including a better dialog, nuanced understanding of space weather risks associated with modest events, but also an opportunity to investigate the space environment in relatively unexplored regions such as very low and high low Earth orbits.

Multipoint Interplanetary Coronal Mass Ejections Observed with Solar Orbiter, BepiColombo, Parker Solar Probe, Wind, and STEREO-A

Abstract: We report the result of the first search for multipoint in situ and imaging observations of interplanetary coronal mass ejections (ICMEs) starting with the first Solar Orbiter (SolO) data in 2020 April - 2021 April. A data exploration analysis is performed including visualizations of the magnetic field and plasma observations made by the five spacecraft SolO, BepiColombo, Parker Solar Probe (PSP), Wind and STEREO-A, in connection with coronagraph and heliospheric imaging observations from STEREO-A/SECCHI and SOHO/LASCO. We identify ICME events that could be unambiguously followed with the STEREO-A heliospheric imagers during their interplanetary propagation to their impact at the aforementioned spacecraft, and look for events where the same ICME is seen in situ by widely separated spacecraft. We highlight two events: (1) a small streamer blowout CME on 2020 June 23 observed with a triple lineup by PSP, BepiColombo and Wind, guided by imaging with STEREO-A, and (2) the first fast CME of solar cycle 25 ($ \approx 1600$ km s$^{-1}$) on 2020 November 29 observed in situ by PSP and STEREO-A. These results are useful for modeling the magnetic structure of ICMEs and the interplanetary evolution and global shape of their flux ropes and shocks, and for studying the propagation of solar energetic particles. The combined data from these missions are already turning out to be a treasure trove for space weather research and are expected to become even more valuable with an increasing number of ICME events expected during the rise and maximum of solar cycle 25.

Global Monitoring of Ionospheric Weather by GIRO and GNSS Data Fusion

Abstract: Prompt and accurate imaging of the ionosphere is essential to space weather services, given a broad spectrum of applications that rely on ionospherically propagating radio signals. As the 3D spatial extent of the ionosphere is vast and covered only fragmentarily, data fusion is a strong candidate for solving imaging tasks. Data fusion has been used to blend models and observations for the integrated and consistent views of geosystems. In space weather scenarios, low latency of the sensor data availability is one of the strongest requirements that limits the selection of potential datasets for fusion. Since remote plasma sensing instrumentation for ionospheric weather is complex, scarce, and prone to unavoidable data noise, conventional 3D-var assimilative schemas are not optimal. We describe a novel substantially 4D data fusion service based on near-real-time data feeds from Global Ionosphere Radio Observatory (GIRO) and Global Navigation Satellite System (GNSS) called GAMBIT (Global Assimilative Model of the Bottomside Ionosphere with Topside estimate). GAMBIT operates with a few-minute latency, and it releases, among other data products, the anomaly maps of the effective slab thickness (EST) obtained by fusing GIRO and GNSS data. The anomaly EST mapping aids understanding of the vertical plasma restructuring during disturbed conditions.

Prediction of Global Ionospheric TEC Based on Deep Learning

Abstract: The accurate prediction of ionospheric Total Electron Content (TEC) is important for global navigation satellite systems (GNSS), satellite communications and other space communications applications. In this study, a prediction model of global IGS-TEC maps are established based on testing several different long short-term memory (LSTM) network (LSTM)-based algorithms to explore a direction that can effectively alleviate the increasing error with prediction time. We find that a Multi-step auxiliary algorithm based prediction model performs best. It can effectively predict the global ionospheric IGS-TEC in the next 6 days (the mean absolute deviation (MAD) and root mean square error (RMSE) are 2.485 and 3.511 TECU, respectively) compared to the IRI (the MAD and RMSE are 4.248 and 5.593 TECU). The analyses of four geomagnetic storm events are completely separate from the time range of the training set, so as to further validate the performance of the model. The International Reference Ionosphere model is used as a reference for the performance of our predictive model, and a rotated persistence is estimated by time-shift algorithm of IGS-TEC. The result suggests that the Multi-step auxiliary prediction model has a good generalization performance and can have a relatively good stability and low error during a geomagnetic storm and quiet time.

A Survey of Uncertainty Quantification in Machine Learning for Space Weather Prediction

Abstract: With the availability of data and computational technologies in the modern world, machine learning (ML) has emerged as a preferred methodology for data analysis and prediction. While ML holds great promise, the results from such models are not fully unreliable due to the challenges introduced by uncertainty. An ML model generates an optimal solution based on its training data. However, if the uncertainty in the data and the model parameters are not considered, such optimal solutions have a high risk of failure in actual world deployment. This paper surveys the different approaches used in ML to quantify uncertainty. The paper also exhibits the implications of quantifying uncertainty when using ML by performing two case studies with space physics in focus. The first case study consists of the classification of auroral images in predefined labels. In the second case study, the horizontal component of the perturbed magnetic field measured at the Earth’s surface was predicted for the study of Geomagnetically Induced Currents (GICs) by training the model using time series data. In both cases, a Bayesian Neural Network (BNN) was trained to generate predictions, along with epistemic and aleatoric uncertainties. Finally, the pros and cons of both Gaussian Process Regression (GPR) models and Bayesian Deep Learning (DL) are weighed. The paper also provides recommendations for the models that need exploration, focusing on space weather prediction.

An Approach for Predicting Global Ionospheric TEC Using Machine Learning

Abstract: Accurate corrections for ionospheric total electron content (TEC) and early warning information are crucial for global navigation satellite system (GNSS) applications under the influence of space weather. In this study, we propose to use a new machine learning model—the Prophet model, to predict the global ionospheric TEC by establishing a short-term ionospheric prediction model. We use 15th-order spherical harmonic coefficients provided by the Center for Orbit Determination in Europe (CODE) as the training data set. Historical spherical harmonic coefficient data from 7 days, 15 days, and 30 days are used as the training set to model and predict 256 spherical harmonic coefficients. We use the predicted coefficients to generate a global ionospheric TEC forecast map based on the spherical harmonic function model and select a year with low solar activity (63.4 < F10.7 < 81.8) and a year with the high solar activity (79.5 < F10.7 < 255.0) to carry out a sliding 2-day forecast experiment. Meanwhile, we verify the model performance by comparing the forecasting results with the CODE forecast product (COPG) and final product (CODG). The results show that we obtain the best predictions by using 15 days of historical data as the training set. Compared with the results of CODE’S 1-Day (C1PG) and CODE’S 2-Day (C2PG). The number of days with RMSE better than COPG on the first and second day of the low-solar-activity year is 151 and 158 days, respectively. This statistic for high-solar-activity year is 183 days and 135 days.

Space Weather Environment During the SpaceX Starlink Satellite Loss in February 2022

Abstract: On 3 February 2022, SpaceX Starlink launched and subsequently lost 38 of 49 satellites due to enhanced neutral density associated with a geomagnetic storm. This study examines the space weather conditions related to the satellite loss, based on observations, forecasts, and numerical simulations from the National Oceanic and Atmospheric Administration Space Weather Prediction Center (SWPC). Working closely with the Starlink team, the thermospheric densities along the satellite orbits were estimated and the neutral density increase leading to the satellite loss was investigated. Simulation results suggest that during the geomagnetic storm, pre-launch Monte Carlo analyses performed by the Starlink team using empirical neutral density inputs from NRLMSISE-00 tended to underestimate the impact relative to predictions from the operational coupled Whole Atmosphere Model and Ionosphere Plasmasphere Electrodynamics physics-based model. The numerical simulation indicated this minor to moderate geomagnetic storm was sufficient to create 50%–125% density enhancement at altitudes ranging between 200 and 400 km. With the increasing solar activity of Solar Cycle 25, satellites in low-Earth orbit are expected to experience an increasing number of thermospheric expansion events. Currently, no alerts and warnings issued by SWPC are focused on satellite users concerned with atmospheric drag and related applications. Thus, during geomagnetic storms, it is crucial to establish suitable alerts and warnings based on neutral density predictions to provide users guidance for preventing satellite losses due to drag and to aid in collision avoidance calculations.

Simulating the Space Weather in the AU Mic System: Stellar Winds and Extreme Coronal Mass Ejections

Abstract: Two close-in planets have been recently found around the M-dwarf flare star AU Microscopii (AU Mic). These Neptune-sized planets (AU Mic b and c) seem to be located very close to the so-called “evaporation valley” in the exoplanet population, making this system an important target for studying atmospheric loss on exoplanets. This process, while mainly driven by high-energy stellar radiation, will be strongly mediated by the space environment surrounding the planets. Here we present an investigation of this last area, performing 3D numerical modeling of the quiescent stellar wind from AU Mic, as well as time-dependent simulations describing the evolution of a highly energetic coronal mass ejection (CME) event in this system. Observational constraints on the stellar magnetic field and properties of the eruption are incorporated in our models. We carry out qualitative and quantitative characterizations of the stellar wind, the emerging CMEs, as well as the expected steady and transient conditions along the orbit of both exoplanets. Our results predict extreme space weather for AU Mic and its planets. This includes sub-Alfvénic regions for the large majority of the exoplanet orbits, very high dynamic and magnetic pressure values in quiescence (varying within 102–105 times the dynamic pressure experienced by Earth), and an even harsher environment during the passage of any escaping CME associated with the frequent flaring observed in AU Mic. These space weather conditions alone pose an immense challenge for the survival of exoplanetary atmospheres (if any) in this system.

Unveiling the Space Weather During the Starlink Satellites Destruction Event on 4 February 2022

Abstract: On 4 February 2022, 38 Starlink satellites were destroyed by the geomagnetic storm, which brought significant financial, aerospace and public influences. In this letter, we reveal the space weather process during 3-4 February 2022 geomagnetic disturbances, from the Sun all the way to the satellite orbiting atmosphere. Initiated by an M1.0 class flare and the following coronal mass ejection (CME), a moderate geomagnetic storm was stimulated on February 3rd by the CME arrival at Earth. Subsequently, another moderate storm was triggered on February 4th by the passage of another CME. Model simulations driven by solar wind show that the first geomagnetic storm induced around 20% atmospheric density perturbations at 210 km altitude on February 3rd. The unexpected subsequent storm on February 4th led to a density enhancement of around 20%-30% at around 210 km. The resulting atmospheric drag can be even larger, since the regional density enhancement was over 60% and the satellite orbits were continuously decaying. This event brings forth the urgent requirements of better understanding and accurate prediction of the space weather as well as collaborations between industry and space weather community.

Uncertainty quantification techniques for data-driven space weather modeling: thermospheric density application

Abstract: Machine learning (ML) has been applied to space weather problems with increasing frequency in recent years, driven by an influx of in-situ measurements and a desire to improve modeling and forecasting capabilities throughout the field. Space weather originates from solar perturbations and is comprised of the resulting complex variations they cause within the numerous systems between the Sun and Earth. These systems are often tightly coupled and not well understood. This creates a need for skillful models with knowledge about the confidence of their predictions. One example of such a dynamical system highly impacted by space weather is the thermosphere, the neutral region of Earth's upper atmosphere. Our inability to forecast it has severe repercussions in the context of satellite drag and computation of probability of collision between two space objects in low Earth orbit (LEO) for decision making in space operations. Even with (assumed) perfect forecast of model drivers, our incomplete knowledge of the system results in often inaccurate thermospheric neutral mass density predictions. Continuing efforts are being made to improve model accuracy, but density models rarely provide estimates of confidence in predictions. In this work, we propose two techniques to develop nonlinear ML regression models to predict thermospheric density while providing robust and reliable uncertainty estimates: Monte Carlo (MC) dropout and direct prediction of the probability distribution, both using the negative logarithm of predictive density (NLPD) loss function. We show the performance capabilities for models trained on both local and global datasets. We show that the NLPD loss provides similar results for both techniques but the direct probability distribution prediction method has a much lower computational cost. For the global model regressed on the Space Environment Technologies High Accuracy Satellite Drag Model (HASDM) density database, we achieve errors of approximately 11% on independent test data with well-calibrated uncertainty estimates. Using an in-situ CHAllenging Minisatellite Payload (CHAMP) density dataset, models developed using both techniques provide test error on the order of 13%. The CHAMP models-on validation and test data-are within 2% of perfect calibration for the twenty prediction intervals tested. We show that this model can also be used to obtain global density predictions with uncertainties at a given epoch.

Revisiting the Ground Magnetic Field Perturbations Challenge: A Machine Learning Perspective

Abstract: Forecasting ground magnetic field perturbations has been a long-standing goal of the space weather community. The availability of ground magnetic field data and its potential to be used in geomagnetically induced current studies, such as risk assessment, have resulted in several forecasting efforts over the past few decades. One particular community effort was the Geospace Environment Modeling (GEM) challenge of ground magnetic field perturbations that evaluated the predictive capacity of several empirical and first principles models at both mid- and high-latitudes in order to choose an operative model. In this work, we use three different deep learning models-a feed-forward neural network, a long short-term memory recurrent network and a convolutional neural network-to forecast the horizontal component of the ground magnetic field rate of change (dB H /dt) over 6 different ground magnetometer stations and to compare as directly as possible with the original GEM challenge. We find that, in general, the models are able to perform at similar levels to those obtained in the original challenge, although the performance depends heavily on the particular storm being evaluated. We then discuss the limitations of such a comparison on the basis that the original challenge was not designed with machine learning algorithms in mind.

An Investigation of Ionospheric TEC Prediction Maps Over China Using Bidirectional Long Short‐Term Memory Method

Abstract: The ionospheric total electron content (TEC) is an important ionospheric parameter, and it is widely utilized in research such as space weather prediction and precise positioning. However, it is still challenging to develop an ionospheric TEC prediction model with high accuracy. In this study, a new ionospheric TEC model over China was developed using the bidirectional long short-term memory (bi-LSTM) method and observations from 257 ground-based global navigation satellite system (GNSS) stations in the Crustal Movement Observation Network of China from January 2018 to December 2021. The root mean square errors of the bi-LSTM-based model’s 1 and 2 hr ahead predictions on the test data set (from June 2021 to December 2021) are 1.12 and 1.68 TECU, respectively, which are 75/50/32% and 72/48/22% smaller than those of the IRI-2016, artificial neural network and LSTM-based models, correspondingly. The bi-LSTM-based model shows the best performance, which is most likely due to the fact that the sequence information in both forward and backward directions is taken into consideration in the new model. In addition, the diurnal variation, seasonal variation of the ionospheric TEC, and variations under geomagnetic storm conditions are successfully captured by the bi-LSTM-based model. Moreover, the TEC maps resulting from the bi-LSTM model agree well with those obtained from the final ionospheric product from the Chinese Academy of Sciences. Hence, the new model can be a good choice for the investigation of the spatiotemporal variation trend in the ionosphere and GNSS navigation.

Causes and Consequences of Magnetic Complexity Changes within Interplanetary Coronal Mass Ejections: A Statistical Study

Abstract: We present the first statistical analysis of complexity changes affecting the magnetic structure of interplanetary coronal mass ejections (ICMEs), with the aim of answering the questions: How frequently do ICMEs undergo magnetic complexity changes during propagation? What are the causes of such changes? Do the in situ properties of ICMEs differ depending on whether they exhibit complexity changes? We consider multi-spacecraft observations of 31 ICMEs by MESSENGER, Venus Express, ACE, and STEREO between 2008 and 2014 while radially aligned. By analyzing their magnetic properties at the inner and outer spacecraft, we identify complexity changes which manifest as fundamental alterations or significant re-orientations of the ICME. Plasma and suprathermal electron data at 1 au, and simulations of the solar wind enable us to reconstruct the propagation scenario for each event, and to identify critical factors controlling their evolution. Results show that ~65% of ICMEs change their complexity between Mercury and 1 au and that interaction with multiple large-scale solar wind structures is the driver of these changes. Furthermore, 71% of ICMEs observed at large radial (>0.4 au) but small longitudinal (<15 degrees) separations exhibit complexity changes, indicating that propagation over large distances strongly affects ICMEs. Results also suggest ICMEs may be magnetically coherent over angular scales of at least 15 degrees, supporting earlier theoretical and observational estimates. This work presents statistical evidence that magnetic complexity changes are consequences of ICME interactions with large-scale solar wind structures, rather than intrinsic to ICME evolution, and that such changes are only partly identifiable from in situ measurements at 1 au.

Low-Latitude Ionospheric Responses and Coupling to the February 2014 Multiphase Geomagnetic Storm from GNSS, Magnetometers, and Space Weather Data

Abstract: The ionospheric response and the associated mechanisms to geomagnetic storms are very complex, particularly during the February 2014 multiphase geomagnetic storm. In this paper, the low-latitude ionosphere responses and their coupling mechanisms, during the February 2014 multiphase geomagnetic storm, are investigated from ground-based magnetometers and global navigation satellite system (GNSS), and space weather data. The residual disturbances between the total electron content (TEC) of the International GNSS Service (IGS) global ionospheric maps (GIMs) and empirical models are used to investigate the storm-time ionospheric responses. Three clear sudden storm commencements (SSCs) on 15, 20, and 23 February are detected, and one high speed solar wind (HSSW) event on 19 February is found with the absence of classical SSC features due to a prevalent magnetospheric convection. The IRI-2012 shows insufficient performance, with no distinction between the events and overestimating approximately 20 TEC units (TECU) with respect to the actual quiet-time TEC. Furthermore, the median average of the IGS GIMs TEC during February 2014 shows enhanced values in the southern hemisphere, whereas the IRI-2012 lacks this asymmetry. Three low-latitude profiles extracted from the IGS GIM data revealed up to 20 TECU enhancements in the differential TEC. From these profiles, longer-lasting TEC enhancements are observed at the dip equator profiles than in the profiles of the equatorial ionospheric anomaly (EIA) crests. Moreover, a gradual increase in the global electron content (GEC) shows approximately 1 GEC unit of differential intensification starting from the HSSW event, while the IGS GIM profiles lack this increasing gradient, probably located at higher latitudes. The prompt penetration electric field (PPEF) and equatorial electrojet (EEJ) indices estimated from magnetometer data show strong variability after all four events, except the EEJ’s Asian sector. The low-latitude ionosphere coupling is mainly driven by the variable PPEF, DDEF (disturbance dynamo electric fields), and Joule heating. The auroral electrojet causing eastward PPEF may control the EIA expansion in the Asian sector through the dynamo mechanism, which is also reflected in the solar-quiet current intensity variability.

An inner boundary condition for solar wind models based on coronal density

Abstract: Accurate forecasting of the solar wind has grown in importance as society becomes increasingly dependent on technology that is susceptible to space weather events. This work describes an inner boundary condition for ambient solar wind models based on tomography maps of the coronal plasma density gained from coronagraph observations, providing a novel alternative to magnetic extrapolations. The tomographical density maps provide a direct constraint of the coronal structure at heliocentric distances of 4 to 8 Rs, thus avoiding the need to model the complex non-radial lower corona. An empirical inverse relationship converts densities to solar wind velocities which are used as an inner boundary condition by the Heliospheric Upwind Extrapolation (HUXt) model to give ambient solar wind velocity at Earth. The dynamic time warping (DTW) algorithm is used to quantify the agreement between tomography/HUXt output and insitu data. An exhaustive search method is then used to adjust the lower boundary velocity range in order to optimize the model. Early results show a 40% decrease in mean absolute error between measured and modelled velocities compared to that of the coupled MAS/HUXt model. The use of density maps gained from tomography as an inner boundary constraint is thus a valid alternative to coronal magnetic models, and offers a significant advancement in the field given the availability of routine space-based coronagraph observations.

Assessing the Influence of Input Magnetic Maps on Global Modeling of the Solar Wind and CME‐Driven Shock in the 2013 April 11 Event

Abstract: In the past decade, significant efforts have been made in developing physics-based solar wind and coronal mass ejection (CME) models, which have been or are being transferred to national centers (e.g., SWPC, Community Coordinated Modeling Center) to enable space weather predictive capability. However, the input data coverage for space weather forecasting is extremely limited. One major limitation is the solar magnetic field measurements, which are used to specify the inner boundary conditions of the global magnetohydrodynamic (MHD) models. In this study, using the Alfvén wave solar model, we quantitatively assess the influence of the magnetic field map input (synoptic/diachronic vs. synchronic magnetic maps) on the global modeling of the solar wind and the CME-driven shock in the 11 April 2013 solar energetic particle event. Our study shows that due to the inhomogeneous background solar wind and dynamical evolution of the CME, the CME-driven shock parameters change significantly both spatially and temporally as the CME propagates through the heliosphere. The input magnetic map has a great impact on the shock connectivity and shock properties in the global MHD simulation. Therefore this study illustrates the importance of taking into account the model uncertainty due to the imperfect magnetic field measurements when using the model to provide space weather predictions.

The Solar Activity Monitor Network – SAMNet

Abstract: The Solar Activity Magnetic Monitor (SAMM) Network (SAMNet) is a future UK-led international network of ground-based solar telescope stations. SAMNet, at its full capacity, will continuously monitor the Sun’s intensity, magnetic, and Doppler velocity fields at multiple heights in the solar atmosphere (from photosphere to upper chromosphere). Each SAMM sentinel will be equipped with a cluster of identical telescopes each with a different magneto-optical filter (MOFs) to take observations in K I, Na D, and Ca I spectral bands. A subset of SAMM stations will have white-light coronagraphs and emission line coronal spectropolarimeters. The objectives of SAMNet are to provide observational data for space weather research and forecast. The goal is to achieve an operationally sufficient lead time of e.g., flare warning of 2–8 h and provide many sought-after continuous synoptic maps (e.g., LoS magnetic and velocity fields, intensity) of the lower solar atmosphere with a spatial resolution limited only by seeing or diffraction limit, and with a cadence of 10 min. The individual SAMM sentinels will be connected to their master HQ hub where data received from all the slave stations will be automatically processed and flare warning issued up to 26 h in advance.

Concurrent Effects between Geomagnetic Storms and Magnetospheric Substorms

Abstract: An accurate understanding of dissimilarities in geomagnetic variability between quiet and disturbed periods has the potential to vastly improve space weather diagnosis. In this work, we exploit some recently developed methods of dynamical system theory to provide new insights and conceptual ideas in space weather science. In particular, we study the co-variation and recurrence statistics of two geomagnetic indices, SYM-H and AL, that measure the intensity of the globally symmetric component of the equatorial electrojet and that of the westward auroral electrojet, respectively. We find that the number of active degrees of freedom, required to describe the phase space dynamics of both indices, depends on the geomagnetic activity level. When the magnetospheric substorm activity, as monitored by the AL index, increases, the active number of degrees of freedom increases at high latitudes above the dimension obtained through classical time delay embedding methods. Conversely, a reduced number of degrees of freedom is observed during geomagnetic storms at low latitude by analysing the SYM-H index. By investigating time-dependent relations between both indices we find that a significant amount of information is shared between high and low latitude current systems originating from coupling mechanisms within the magnetosphere–ionosphere system as the result of a complex interplay between processes and phenomena of internal origin activated by the triggering of external source processes. Our observations support the idea that the near-Earth electromagnetic environment is a complex system far from an equilibrium.

Occurrence rate and duration of space weather impacts to high frequency radio communication used by aviation

Abstract: High frequency (HF) radio wave propagation is sensitive to space weather induced ionospheric disturbances that result from enhanced photoionization and energetic particle precipitation. Recognizing the potential risk to HF radio communication systems used by the aviation industry, as well as potential impacts to GNSS navigation and the risk of elevated radiation levels, the International Civil Aviation Organization (ICAO) initiated development of a space weather advisory service. For HF systems, this service specifically identifies shortwave fadeout, auroral absorption, polar cap absorption, and post storm maximum useable frequency depression (PSD) as phenomena impacting HF radio communication, and specifies moderate and severe event thresholds to describe event severity. This paper examines the occurrence rate and duration of events crossing the moderate and severe thresholds. Shortwave fadeout was evaluated based on thresholds in the solar X-ray flux. Analysis of 40-years of solar X-ray flux data showed that moderate and severe level solar X-ray flares were observed, on average, 123 and 5 times per 11-year solar cycle, respectively. The mean event duration was 68 minutes for moderate level events and 132 minutes for severe level events. Auroral absorption events crossed the moderate threshold for 40 events per solar cycle, with a mean event duration of 5.1 hours. The severe threshold was crossed for 3 events per solar cycle with a mean event duration of 12 hours. Polar cap absorption had the longest mean duration at ~8 hours for moderate events and 1.6 days for severe events; on average, 24 moderate and 13 severe events observed per solar cycle. Moderate and severe thresholds for shortwave fadeout, auroral absorption, and polar cap absorption were used to determine the expected impacts on HF radio communication. Results for polar cap absorption and shortwave fadeout were consistent with each other, but the expected impact for auroral absorption was shown to be 2-3 times higher. Analysis of 22 years of ionosonde data showed moderate and severe PSD events occurred, on average, 200 and 56 times per 11-year solar cycle, respectively. The mean event duration was 5.5 hours for moderate level events and 8.5 hours for severe level events. During solar cycles 22 and 23, HF radio communication was expected to experience moderate or severe impacts due to the ionospheric disturbances caused by space weather a maximum of 163 and 78 days per year, respectively, due to the combined effect of absorption and PSD. The distribution of events is highly non-uniform with respect to solar cycle: 70% of moderate or severe events were observed during solar maximum compared to solar minimum.

A new climatological electron density model for supporting space weather services

Abstract: The ionosphere is the ionized part of the Earth’s atmosphere, ranging from about 60 km up to several Earth radii, whereas the upper part above about 1000 km height up to the plasmapause is usually called the plasmasphere. We present a new three-dimensional electron density model to support space weather services and mitigate propagation errors for trans-ionospheric signals. The model is developed by superposing the Neustrelitz Plasmasphere Model (NPSM) to an ionosphere model composed of separate F and E-layer distributions. It uses the Neustrelitz TEC model (NTCM), Neustrelitz Peak Density Model (NPDM), and the Neustrelitz Peak Height Model (NPHM) for the total electron content (TEC), peak ionization, and peak height information. These models describe the spatial and temporal variability of the key parameters as a function of local time, geographic/geomagnetic location, solar irradiation, and activity. The model is developed to calculate the electron concentration at any given location and time in the ionosphere for trans-ionospheric applications and named the Neustrelitz Electron Density Model (NEDM2020). A comprehensive validation study is conducted against electron density in-situ data from DMSP and Swarm, Van Allen Probes and ICON missions, and topside TEC data from COSMIC/FORMOSAT-3 mission, bottom side TEC data from TOPEX/Poseidon mission, and ground-based TEC data from International GNSS Service (IGS) covering both high and low solar activity conditions. Additionally, the model performance is compared with the 3D electron density model NeQuick2. Our investigation shows that the NEDM2020 performs better than the NeQuick2 compared with the in-situ data from Van Allen Probes and ICON satellites and TEC data from COSMIC and TOPEX/Poseidon missions. When compared with DMSP and IGS TEC data, both NEDM2020 and NeQuick2 perform very similarly.

Solar X-ray and Extreme Ultraviolet Imager (X-EUVI) loaded onto China’s Fengyun-3E Satellite

Abstract: The solar X-ray and Extreme Ultraviolet Imager (X-EUVI), which was developed by CIOMP, is China's first space-based solar X-ray and Extreme Ultraviolet (EUV) imager; it has been loaded onto the Fengyun-3E Satellite, which is supported by the China Meteorological Administration (CMA), for solar observation. It commenced working on July 11, 2021, and was used to obtain the first X-ray and EUV images in China. X-EUVI employs an innovation dual band design to monitor a much larger temperature range across the Sun, covering the 0.6-8.0 nm wavelength band of the X-ray region and the 19.5 nm band of the EUV region.

A Comparative Analysis of Machine-learning Models for Solar Flare Forecasting: Identifying High-performing Active Region Flare Indicators

Abstract: Solar flares create adverse space weather impacting space- and Earth-based technologies. However, the difficulty of forecasting flares, and by extension severe space weather, is accentuated by the lack of any unique flare trigger or a single physical pathway. Studies indicate that multiple physical properties contribute to active region flare potential, compounding the challenge. Recent developments in machine learning (ML) have enabled analysis of higher-dimensional data leading to increasingly better flare forecasting techniques. However, consensus on high-performing flare predictors remains elusive. In the most comprehensive study to date, we conduct a comparative analysis of four popular ML techniques (k nearest neighbors, logistic regression, random forest classifier, and support vector machine) by training these on magnetic parameters obtained from the Helioseismic and Magnetic Imager on board the Solar Dynamics Observatory for the entirety of solar cycle 24. We demonstrate that the logistic regression and support vector machine algorithms perform extremely well in forecasting active region flaring potential. The logistic regression algorithm returns the highest true skill score of 0.967 ± 0.018, possibly the highest classification performance achieved with any strictly parametric study. From a comparative assessment, we establish that magnetic properties like total current helicity, total vertical current density, total unsigned flux, R_VALUE, and total absolute twist are the top-performing flare indicators. We also introduce and analyze two new performance metrics, namely, severe and clear space weather indicators. Our analysis constrains the most successful ML algorithms and identifies physical parameters that contribute most to active region flare productivity.

CUSP: a two CubeSats constellation for space weather and solar flares x-ray polarimetry

Abstract: The CubeSat solar polarimeter (CUSP) project aims to develop a constellation of two CubeSats orbiting the Earth to measure the linear polarisation of solar flares in the hard x-ray band by means of a Compton scattering polarimeter on board of each satellite. CUSP will allow to study the magnetic reconnection and particle acceleration in the flaring magnetic structures. CUSP is a project approved for a Phase A study by the Italian Space Agency in the framework of the Alcor program aimed to develop CubeSat technologies and missions.