Space Weather Processes Research Articles

Space Weather: From solar origins to risks and hazards evolving in time

Space Weather is the portion of space physics that has a direct effect on humankind. Space Weather is an old branch of space physics that originates back to 1808 with the publication of a paper by the great naturalist Alexander von Humboldt (Von Humboldt, Ann. Phys. 1808, 29, 425–429), first defining a “Magnetische Ungewitter” or magnetic storm from auroral observations from his home in Berlin, Germany. Space Weather is currently experiencing explosive growth, because its effects on human technologies have become more and more diverse. Space Weather is due to the variability of solar processes that cause interplanetary, magnetospheric, ionospheric, atmospheric and ground level effects. Space Weather can at times have strong impacts on technological systems and human health. The threats and risks are not hypothetical, and in the event of extreme Space Weather events the consequences could be quite severe for humankind. The purpose of the review is to give a brief overall view of the full chain of physical processes responsible for Space Weather risks and hazards, tracing them from solar origins to effects and impacts in interplanetary space, in the Earth’s magnetosphere and ionosphere and at the ground. In addition, the paper shows that the risks associated with Space Weather have not been constant over time; they have evolved as our society becomes more and more technologically advanced. The paper begins with a brief introduction to the Carrington event, arguably the greatest geomagnetic storm in recorded history. Next, the descriptions of the strongest known Space Weather processes are reviewed, tracing them from their solar origins. The concepts of geomagnetic storms and substorms are briefly introduced. The main effects/impacts of Space Weather are also considered, including geomagnetically induced currents (GICs) which are thought to cause power outages. The effects of radiation on avionics and human health, ionospheric effects and impacts, and thermosphere effects and satellite drag will also be discussed. Finally, we will discuss the current challenges of Space Weather forecasting and examine some of the worst-case scenarios.

Ensemble Machine Learning of Random Forest, AdaBoost and XGBoost for Vertical Total Electron Content Forecasting

Space weather describes varying conditions between the Sun and Earth that can degrade Global Navigation Satellite Systems (GNSS) operations. Thus, these effects should be precisely and timely corrected for accurate and reliable GNSS applications. That can be modeled with the Vertical Total Electron Content (VTEC) in the Earth’s ionosphere. This study investigates different learning algorithms to approximate nonlinear space weather processes and forecast VTEC for 1 h and 24 h in the future for low-, mid- and high-latitude ionospheric grid points along the same longitude. VTEC models are developed using learning algorithms of Decision Tree and ensemble learning of Random Forest, Adaptive Boosting (AdaBoost), and eXtreme Gradient Boosting (XGBoost). Furthermore, ensemble models are combined into a single meta-model Voting Regressor. Models were trained, optimized, and validated with the time series cross-validation technique. Moreover, the relative importance of input variables to the VTEC forecast is estimated. The results show that the developed models perform well in both quiet and storm conditions, where multi-tree ensemble learning outperforms the single Decision Tree. In particular, the meta-estimator Voting Regressor provides mostly the lowest RMSE and the highest correlation coefficients as it averages predictions from different well-performing models. Furthermore, expanding the input dataset with time derivatives, moving averages, and daily differences, as well as modifying data, such as differencing, enhances the learning of space weather features, especially over a longer forecast horizon.

SCSS-Net: solar corona structures segmentation by deep learning

ABSTRACT Structures in the solar corona are the main drivers of space weather processes that might directly or indirectly affect the Earth. Thanks to the most recent space-based solar observatories, with capabilities to acquire high-resolution images continuously, the structures in the solar corona can be monitored over the years with a time resolution of minutes. For this purpose, we have developed a method for automatic segmentation of solar corona structures observed in the EUV spectrum that is based on a deep-learning approach utilizing convolutional neural networks. The available input data sets have been examined together with our own data set based on the manual annotation of the target structures. Indeed, the input data set is the main limitation of the developed model’s performance. Our SCSS-Net model provides results for coronal holes and active regions that could be compared with other generally used methods for automatic segmentation. Even more, it provides a universal procedure to identify structures in the solar corona with the help of the transfer learning technique. The outputs of the model can be then used for further statistical studies of connections between solar activity and the influence of space weather on Earth.

GNSS-based hardware-in-the-loop simulations of spacecraft formation flying with the global ionospheric model TIEGCM

Low-earth-orbit (LEO) spacecraft formation flying (SFF) simulated on the Virginia Tech Formation Flying Testbed (VTFFTB) demonstrated the feasibility of detecting simplified ionospheric electron density (Ne) structures (i.e., one-dimensional equatorial plasma bubbles/EPBs) utilizing a differential total electron content (TEC) method. The Thermosphere-Ionosphere-Electrodynamics General Circulation Model (TIEGCM) is used to greatly enhance the ionospheric Ne simulation capability and fidelity (e.g., dimension, resolution, more realistic space weather processes) of GNSS-based hardware-in-the-loop (HIL) simulations in the current work. An algorithm is developed to incorporate TIEGCM 4D ionospheric Ne profiles into the VTFFTB HIL infrastructure by simulating the TEC impacts on multi-band GNSS signals. First, a series of first-principle analyses on ionospheric Ne retrieval accuracy are presented using software-based LEO SFF simulations in the TIEGCM ionosphere. Subsequently, two HIL ionospheric space weather observation simulation test cases, EPB and Tongues-of-Ionization (TOI), are simulated on the VTFFTB to demonstrate the enhanced 4D ionospheric simulation capability and evaluate the Ne retrieval characteristics. The Ne retrieval techniques are validated and characterized in the TIEGCM ionosphere via both software and HIL simulations. In principle, the technique developed is expected to be applicable to other similar simulation platforms, as well as other global or regional ionospheric models. This is advantageous for future GNSS testing, HIL simulations, and technology developments.

Pro‐L* ‐ A Probabilistic L* Mapping Tool for Ground Observations

AbstractBoth ground and space observations are used extensively in the modeling of space weather processes within the Earth’s magnetosphere. In radiation belt physics modeling, one of the key phase‐space coordinates is L*, which indicates the location of the drift paths of energetic electrons. Global magnetic field models allow a subset of locations on the ground (mainly subauroral) to be mapped along field lines to a location in space and transformed into L*, provided that the initial ground location maps to a closed drift path. This allows observations from ground, or low‐altitude space‐based platforms to be mapped into space in order to inform radiation belt modeling. Many data‐based magnetic field models exist; however, these models can significantly disagree on mapped L* values for a single point on the ground, during both quiet times and storms. We present a state of the art probabilistic L* mapping tool, Pro‐L*, which produces probability distributions for L* corresponding to a given ground location. Pro‐L* has been calculated for a high resolution magnetic latitude by magnetic local time grid in the Earth’s Northern Hemisphere. We have developed the probabilistic model using 11 years of L* calculations for seven widely used magnetic field models. Usage of the tool is highlighted for both event studies and statistical models, and we demonstrate a number of potential applications.

Hesse Receives 2019 Space Weather and Nonlinear Waves and Processes Prize

Michael Hesse received the 2019 Space Weather and Nonlinear Waves and Processes Prize at AGU’s Fall Meeting 2019, held 9–13 December in San Francisco, Calif. The award “recognizes cutting-edge work in the fields of space weather and nonlinear waves and processes from midcareer and senior scientists.”

Geomagnetic Field Processes and Their Implications for Space Weather

Understanding the magnetic environment of our planet and the geomagnetic field changes in time and space is a very important issue for assessing the Sun–Earth interactions. All changes in solar activity impact the delicate balance between influences of interplanetary magnetic field and of geomagnetic field. The most dynamic events eventually result in disturbances in the magnitude and direction of the Earth’s magnetic field and therefore impact our planet and its magnetosphere as a whole. The dynamics of the ionosphere and thermosphere during magnetic storms and substorms involves the heating, expansion, and composition changes at high latitudes, but also the surface-level response in terms of geomagnetically induced currents and other geomagnetic and geoelectric disturbances. Here, we provide a short overview of the current knowledge of the Earth’s magnetic field, its present shape and the way it responds to external forces. The main aim of the paper is not to present the complexity of the space weather processes, but rather to bring the attention of the geohazard community to the possible dramatic effects of space weather events. For this, the paper highlights some societal implications of space weather on our increasingly technology-dependent society, including some possible effects of geomagnetically induced currents, the disruption of satellite communications and navigation, and risks of radiation damage both in space and in aviation.

The Space Weather Atmosphere Models and Indices (SWAMI) project: Overview and first results

Space weather driven atmospheric density variations affect low Earth orbit (LEO) satellites during all phases of their operational lifetime. Rocket launches, re-entry events and space debris are also similarly affected. A better understanding of space weather processes and their impact on atmospheric density is thus critical for satellite operations as well as for safety issues. The Horizon 2020 project Space Weather Atmosphere Model and Indices (SWAMI) project, which started in January 2018, aims to enhance this understanding by:Developing improved neutral atmosphere and thermosphere models, and combining these models to produce a new whole atmosphere model.Developing new geomagnetic activity indices with higher time cadence to enable better representation of thermospheric variability in the models, and improving the forecast of these indices.The project stands out by providing an integrated approach to the satellite neutral environment, in which the main space weather drivers are addressed together with model improvement. The outcomes of SWAMI will provide a pathway to improved space weather services as the project will not only address the science issues, but also the transition of models into operational services.The project aims to develop a unique new whole atmosphere model, by extending and blending the Unified Model (UM), which is the Met Office weather and climate model, and the Drag Temperature Model (DTM), which is a semi-empirical model which covers the 120–1500 km altitude range. A user-focused operational tool for satellite applications shall be developed based on this. In addition, improved geomagnetic indices shall be developed and shall be used in the UM and DTM for enhanced nowcast and forecast capability.In this paper, we report on progress with SWAMI to date. The UM has been extended from its original upper boundary of 85 km to run stably and accurately with a 135 km lid. Developments to the UM radiation scheme to enable accurate performance in the mesosphere and lower thermosphere are described. These include addition of non-local thermodynamic equilibrium effects and extension to include the far ultraviolet and extreme ultraviolet. DTM has been re-developed using a more accurate neutral density observation database than has been used in the past. In addition, we describe an algorithm to develop a new version of DTM driven by geomagnetic indices with a 60 minute cadence (denoted Hp60) rather than 3-hourlyKpindices (and corresponding ap indices). The development of the Hp60 index, and the Hp30 and Hp90 indices, which are similar to Hp60 but with 30 minute and 90 minute cadences, respectively, is described, as is the development and testing of neural network and other machine learning methods applied to the forecast of geomagnetic indices.

Using Eccentricity to Locate Ionospheric Exit Points of Magnetospheric Whistler Mode Waves

Whistler mode waves are a type of electromagnetic plasma wave and play a dominant role in the energy dynamics of the near-earth space environment and associated space weather processes. Ground-based observations of these waves are important for numerical model validation, space weather monitoring, and discovery science. Unfortunately, ground-based observations are often challenging to interpret since information about traversal through the ionosphere and distance propagated in the earth–ionosphere waveguide is rarely available. A new approach to identifying the ionospheric exit points of the magnetospheric whistler mode emissions is presented, which takes into account the observed wave polarization. The method relies on the initial circular polarization of the ducted magnetospheric emissions which is shown to convert to linear polarization after propagation in the earth–ionosphere waveguide at a predictable rate. Finite-difference time domain modeling of observed eccentricity provides a metric for determining distance from observer to ionospheric exit point. The method is shown to produce good agreement with observations of magnetospheric chorus emissions, artificially triggered emissions, and lightning-induced whistlers.

Bruno Receives 2018 Space Weather and Nonlinear Waves and Processes Prize

Roberto Bruno will receive the Space Weather and Nonlinear Waves and Processes Prize at AGU’s Fall Meeting 2018, to be held 10–14 December in Washington, D. C. The award recognizes “cutting-edge work in the fields of space weather and nonlinear waves and processes.”

International Geophysical Year: Space Weather Impacts in February 1958

AbstractSixty years ago the large February 1958 geomagnetic storm in the International Geophysical Year produced significant impacts on the electrical technologies of the day. The impacts of this major storm had considerable public exposure and interest. In the six decades since the large International Geophysical Year storm, the technology elements impacted during that storm—communications and power systems—continue to be employed, sometimes in new ways. Importantly, over the decades new human technologies that can be impacted by space weather processes have been invented and implemented. Continued progress is required in the research and understanding of space processes so as to design for, mitigate against, and operate through severe geomagnetic disturbances.

Gonzalez Receives 2017 Space Weather and Nonlinear Waves and Processes Prize

Walter Gonzalez will receive the Space Weather and Nonlinear Waves and Processes Prize at the 2017 American Geophysical Union Fall Meeting, to be held 11–15 December in New Orleans, La. The award recognizes “cutting-edge work in the fields of space weather and nonlinear waves and processes.”

Space Weather Research: Indian perspective

AbstractSpace weather, just like its meteorological counterpart, is of extreme importance when it comes to its impact on terrestrial near‐ and far‐space environments. In recent years, space weather research has acquired an important place as a thrust area of research having implications both in space science and technology. The presence of satellites and other technological systems from different nations in near‐Earth space necessitates that one must have a comprehensive understanding not only of the origin and evolution of space weather processes but also of their impact on technology and terrestrial upper atmosphere. To address this aspect, nations across the globe including India have been investing in research concerning Sun, solar processes and their evolution from solar interior into the interplanetary space, and their impact on Earth's magnetosphere‐ionosphere‐thermosphere system. In India, over the years, a substantial amount of work has been done in each of these areas by various agencies/institutions. In fact, India has been, and continues to be, at the forefront of space research and has ambitious future programs concerning these areas encompassing space weather. This review aims at providing a glimpse of this Indian perspective on space weather research to the reader and presenting an up‐to‐date status of the same.

Progress towards physics-based space weather forecasting with exascale computing

Space weather is a rapidly growing field of science which studies processes occurring in the area of space between the Sun and the Earth. The development of space weather forecasting capabilities is a task of great societal relevance: space weather effects may damage a number of technological assets, among which power and communication lines, transformers, pipelines and the telecommunication infrastructure. Exascale computing is a fundamental ingredient for space weather forecasting tools based on physical, rather than statistical, models. We describe here our recent progresses towards a physics-based space weather forecasting tool with exascale computing. We select the semi-implicit, Particle In Cell, Implicit Moment Method implemented in the parallel, object-oriented, C++ iPic3D code as a promising starting point. We analyze the structure and the performances of the current version of the iPic3D code. We describe three algorithmic developments, the fully implicit method, the Multi-Level Multi-Domain method, and the fluid-kinetic method, which can help addressing the multiple spatial and temporal scales present in space weather simulations. We then examine, in a co-design approach, which requirements – vectorization, extreme parallelism and reduced communication – an application has to satisfy to fully exploit architectures such as GPUs and Xeon Phi’s. We address how to modify the iPic3D code to better satisfy these requirements. We then describe how to port the iPic3D code to the DEEP architecture currently under construction. The FP7 project DEEP (www.deep-project.eu) aims at building an exascale-ready machine composed of a cluster of Xeon nodes and of a collection of Xeon Phi coprocessors, used as boosters. The aim of the DEEP project is to enable exascale performance for codes, such as iPic3D, composed of parts which exhibit different potential for extreme scalability. Finally, we provide examples of simulations of space weather processes done with the current version of the iPic3D code.

Sources and characteristics of medium‐scale traveling ionospheric disturbances observed by high‐frequency radars in the North American sector

AbstractMedium‐scale traveling ionospheric disturbances (MSTIDs) are wave‐like ionospheric perturbations routinely observed by high‐frequency radars. We focus on a class of MSTIDs observed during the winter daytime at high latitudes and midlatitudes. The source of these MSTIDs remains uncertain, with the two primary candidates being space weather and lower atmospheric processes. We surveyed observations from four high‐latitude and six midlatitude Super Dual Auroral Radar Network radars in the North American sector from November to May 2012 to 2015. The MSTIDs observed have horizontal wavelengths between ∼150 and 650 km and horizontal velocities between ∼75 and 325 m s−1. In local fall and winter seasons the majority of MSTIDs propagated equatorward, with bearings ranging from ∼125° to 225° geographic azimuth. No clear correlation with space weather activity as parameterized by AE and SYM‐H could be identified. Rather, MSTID observations were found to have a strong correlation with polar vortex dynamics on two timescales. First, a seasonal timescale follows the annual development and decay of the polar vortex. Second, a shorter 2–4 week timescale again corresponds to synoptic polar vortex variability, including stratospheric warmings. Additionally, statistical analysis shows that MSTIDs are more likely during periods of strong polar vortex. Direct comparison of the MSTID observations with stratospheric zonal winds suggests that a wind filtering mechanism may be responsible for the strong correlation. Collectively, these observations suggest that polar atmospheric processes, rather than space weather activity, are primarily responsible for controlling the occurrence of high‐latitude and midlatitude winter daytime MSTIDs.

Analysis of the dependence of surfatron acceleration of electrons by an electromagnetic wave in space plasma on the particle momentum along the wave front

Based on the numerical solution of the nonlinear nonstationary second-order equation for the wave phase on the particle trajectory, the dynamics of surfatron acceleration of electrons by an electromagnetic wave propagating across the external magnetic field in space plasma is analyzed as a function of the electron momentum along the wave front. Numerical calculations show that, for strongly relativistic initial values of the electron momentum component along the wave front gy(0) (the other parameters of the problem being the same), electrons are trapped into the regime of ultrarelativistic surfatron acceleration within a certain interval of the initial wave phase Ψ(0) on the particle trajectory. It is assumed in the calculations that |Ψ(0)| ≤ π. For strongly relativistic values of gy(0), electrons are immediately trapped by the wave for 19% of the initial values of the phase Ψ(0) (favorable phases). For the rest of the values of Ψ(0), trapping does not occur even at long times. This circumstance substantially simplifies estimations of the wave damping due to particle acceleration in subsequent calculations. The dynamics of the relativistic factor and the components of the electron velocity and momentum under surfatron acceleration is also analyzed. The obtained results are of interest for the development of modern concepts of possible mechanisms of generation of ultrarelativistic particle fluxes in relatively calm space plasma, as well as for correct interpretation of observational data on the fluxes of such particles and explanation of possible reasons for the deviation of ultrarelativistic particle spectra detected in the heliosphere from the standard power-law scalings and the relation of these variations to space weather and large-scale atmospheric processes similar to tropical cyclones.

Lakhina Receives 2015 Space Weather and Nonlinear Waves and Processes Prize

Gurbax Lakhina will receive the Space Weather and Nonlinear Waves and Processes Prize at the 2015 American Geophysical Union Fall Meeting, to be held 14–18 December in San Francisco, Calif. The award recognizes significant contributions in the field of space weather or nonlinear waves and processes.

Gombosi Receives 2013 Space Weather and Nonlinear Waves and Processes Prize: Response

It is a great honor to be the recipient of AGU's inaugural Space Weather and Nonlinear Waves and Processes Prize. I am truly humbled by this recognition because there are many other highly deserving colleagues in our research field.

Gombosi Receives 2013 Space Weather and Nonlinear Waves and Processes Prize: Citation

The Space Physics and Aeronomy section of AGU awards the Space Weather and Nonlinear Waves and Processes Prize to Tamas Gombosi of the University of Michigan. Gombosi, the founding director of the Center for Space Environment Modeling, has been a leader in space weather research, a visionary in space weather numerical modeling for several decades, and a pioneer of international space physics collaboration. Gombosi's major contributions to space weather include the development of the first time‐dependent numerical model of the terrestrial polar wind and the creation of the BATS‐R‐US magnetohydrodynamic model, a powerful and versatile numerical tool widely used today for modeling the global geospace, the heliosphere, and the solar interior.

Remote sensing the plasmasphere, plasmapause, plumes and other features using ground-based magnetometers

The plasmapause is a highly dynamic boundary between different magnetospheric particle populations and convection regimes. Some of the most important space weather processes involve wave-particle interactions in this region, but wave properties may also be used to remote sense the plasmasphere and plasmapause, contributing to plasmasphere models. This paper discusses the use of existing ground magnetometer arrays for such remote sensing. Using case studies we illustrate measurement of plasmapause location, shape and movement during storms; refilling of flux tubes within and outside the plasmasphere; storm-time increase in heavy ion concentration near the plasmapause; and detection and mapping of density irregularities near the plasmapause, including drainage plumes, biteouts and bulges. We also use a 2D MHD model of wave propagation through the magnetosphere, incorporating a realistic ionosphere boundary and Alfven speed profile, to simulate ground array observations of power and cross-phase spectra, hence confirming the signatures of plumes and other density structures.