Solar Wind Research Articles

Considering Embodied Greenhouse Emissions of Nuclear and Renewable Power Plants for Electrolytic Hydrogen and Its Use for Synthetic Ammonia, Methanol, Fischer-Tropsch Fuel Production.

Recent concerns surrounding climate change and the contribution of fossil fuels to greenhouse gas (GHG) emissions have sparked interest and advancements in renewable energy sources including wind, solar, and hydroelectricity. These energy sources, often referred to as "clean energy", generate no operational onsite GHG emissions. They also offer the potential for clean hydrogen production through water electrolysis, presenting a viable solution to create an environmentally friendly alternative energy carrier with the potential to decarbonize industrial processes reliant on hydrogen. To conduct a full life cycle analysis, it is crucial to account for the embodied emissions associated with renewable and nuclear power generation plants as they can significantly impact the GHG emissions linked to hydrogen production and its derived products. In this work, we conducted a comprehensive analysis of the embodied emissions associated with solar photovoltaic (PV), wind, hydro, and nuclear electricity. We investigated the implications of including plant-embodied emissions in the overall emission estimates of electrolysis hydrogen production and subsequently on the production of synthetic ammonia, methanol, and Fischer-Tropsch (FT) fuels. Results show that average embodied GHG emissions of solar PV, wind, hydro, and nuclear electricity generation in the United States (U.S.) were estimated to be 37, 9.8, 7.2, and 0.3 g CO2 e/kWh, respectively. Life cycle GHG emissions of electrolytic hydrogen produced from solar PV, wind, and hydroelectricity were estimated as 2.1, 0.6, and 0.4 kg of CO2 e/kg of H2, respectively, in contrast to the zero-emissions often used when the embodied emissions in their construction were excluded. Average life cycle emission estimates (CO2 e/kg) of synthetic ammonia, methanol, and FT-fuel from solar PV electricity are increased by 5.5, 16, and 49 times, respectively, compared to the case when embodied emissions are excluded. This change also depends on the local irradiance for solar power, which can result in a further increase of GHG emissions by 35-41% in areas of low irradiance or reduce GHG emissions by 21-25% in areas with higher irradiance.

Surge in Power and Gas Demand in the Southeast Atlantic—Reasons and Solutions

The Southeast Atlantic region (much of it in Virginia and the Carolinas) is anticipating an extraordinary growth in electric demand over the next few years, with a resulting surge in natural gas demand. Utilities in all three states, anticipating rapid growth in electricity demand through the 2030s, have ambitious plans for renewables. However, these states are acknowledging that solar and offshore wind will need to be backed up by a significant increase in natural gas‐fired generation to join energy storage in providing load‐following dispatchable power, to compensate for the variability of wind and solar generation. There is also a recognition that load is growing too fast to rely solely on new wind and solar for baseload generation. Further, a significant amount of remaining coal‐fired generation is expected to phase out in the three states, creating the need for more generation unrelated to load growth.

Novel hybrid data-driven models for enhanced renewable energy prediction

Global power grid management depends on accurate solar energy estimation, yet present prediction techniques frequently suffer from unreliability as a result of abnormalities in solar energy data. Solar radiation projections are affected by variables such as anticipated horizon length, meteorological classification, and power measuring techniques. Therefore, a Solar Wind Energy Prediction System (SWEPS) is proposed as a solution to these problems. It improves renewable energy projections by taking sun trajectories and atmospheric characteristics into account. In addition to using a variety of optimization methods and pre-processing techniques, such as Principal Component Analysis (PCA), Recursive Feature Elimination (RFE), Least Absolute Shrinkage Selection Operator (LASSO), and recursive feature addition processes (RFA), complemented by a genetic algorithm for feature selection (GAFS). The SWEPS also makes use of sophisticated machine learning algorithms and Statistical Correlation Analysis (SCA) to find important connections. Neural Network Algorithms (NNA) and other metaheuristic techniques like Cuckoo Search Optimization (CSO), Social Spider Optimization (SSO), and Particle Swarm Optimization (PSO) are adopted in this work to increase the predictability and accuracy of models. Utilizing the strengths of machine learning and deep learning techniques (Artificial Neural Networks (ANN), Decision Trees, Support Vector Machine (SVM), Recurrent Neural Networks (RNN), and Long Short Term Memory (LSTM)) for robust forecasting, as well as meta-heuristic optimization techniques to fine-tune hyper-parameters and achieve near-optimal values and significantly improve model performance, are some of this work contributions to the development of a comprehensive prediction system.

Scenario-based stochastic optimization on the variability of solar and wind for component sizing of integrated energy systems

The inherent intermittency and variability of renewable energy sources present significant challenges to the optimal design and implementation of integrated energy systems (IES). This paper introduces a novel stochastic optimization model that integrates advanced scenario generation and clustering algorithm for renewable energy sources within a multi-objective, bi-level optimization framework. Specifically, the clearness index is employed to represent the stochastic distribution of solar radiation intensity by beta distribution, while wind speed uncertainty is modeled seasonally using the Weibull distribution. Monte Carlo sampling with synchronous back substitution is applied for scenario generation and reduction of solar radiation and wind speed. To address the multi-objective evaluation, the analytic hierarchy process is utilized, and the joint optimization is achieved by combining a region contraction algorithm with stochastic programming. The proposed methodology is validated on an IES featuring various heating devices, incorporating uncertainties in both wind and solar energy. The results indicate that the absorption heat pump-based scheme achieves superior energy-saving performance, achieving an energy rate of 0.4724. Additionally, the compression heat pump-based scheme exhibits excellent economic efficiency and environmental sustainability, with a cost of energy of 0.3639 and a renewable fraction of 0.5536.

Charging effects on Rosetta dust measurements

ABSTRACT Dust particles released from comet 67P/Churyumov-Gerasimenko collect electrostatic charges. Their motion is influenced by the electric fields induced by the flow of the solar wind and by the charging of the Rosetta spacecraft itself. Dust grains with sufficiently low tensile strength might even be destroyed en route from the nucleus to Rosetta. A simple model of the plasma environment is discussed here to enable simultaneously following the charging and the dynamics of dust particles as a function of the heliocentric distance of the comet, the distance between Rosetta and the nucleus, the asymmetry in gas production between the northern and southern hemispheres of the nucleus, the amplitude and timing of ultraviolet flares, and the possible outbursts intermittently increasing the production rate of the comet. The electrostatic disruption, and the combination of attractive and repulsive forces between the dust grains and Rosetta might significantly alter the conclusions about the size and spatial distributions of dust grains released from 67P/Churyumov-Gerasimenko. These calculations are presented to help assess the effects of dust and spacecraft charging in the analysis and interpretation of dust measurements by Rosetta.

Mercury’s plasma environment after BepiColombo’s third flyby

Understanding Mercury’s magnetosphere is crucial for advancing our comprehension of how the solar wind interacts with the planetary magnetospheres. Despite previous missions, several gaps remain in our knowledge of Mercury’s plasma environment. Here, we present findings from BepiColombo’s third flyby, offering a synoptic view of the large scale structure and composition of Mercury’s magnetosphere. The Mass Spectrum Analyzer (MSA), Mass Ion Analyzer (MIA), and Mass Electron Analyzer (MEA) on the magnetospheric orbiter reveal insights, including the identification of trapped energetic hydrogen (H+) with energies around 20 keV e−1 evidencing a ring current, and a cold ion plasma with energies below 50 eV e−1. Additionally, we observe a Low-Latitude Boundary Layer (LLBL), which is a region of turbulent plasma at the edge of the magnetosphere, characterized by bursty ion enhancements, indicating an ongoing injection process in the duskside magnetosphere flank. These observations during cruise phase provide a tantalizing glimpse of future discoveries expected from the Mercury Plasma Particle Experiment (MPPE) instruments after orbit insertion, promising broader impacts on our understanding of planetary magnetospheres.

The solar wind heavy ion composition in the ascending phases of the solar cycles 23 and 25

The approximately 11-year solar cycle has been shown to impact the heavy ion composition of the solar wind, even when accounting for streams of differing speeds; however, the heavy ion composition observed between the same specific phases of a past solar cycle and the current cycle has rarely, if ever, been compared. Here, we compare the heavy ion composition of the solar wind, as measured in situ during the solar cycle 23 and 25 ascending phases. We examine the mean iron and oxygen charge state composition and the O7+/O6+ ratio in multiple ranges of associated bulk wind speeds. Then, we compare the iron and oxygen charge state composition and relative abundance of iron to oxygen in the traditionally defined fast and slow solar wind. Finally, to determine the impact of individual ion contributions on the solar wind iron abundance, we examine individual ratios of iron and oxygen ions. Although the charge state composition remained broadly similar between these two ascending phases, both the O7+/O6+ ratio and iron fractionation in fast-speed streams were higher in the solar cycle 25 ascending phase than they were during the solar cycle 23 ascending phase, suggesting that equatorial coronal hole fields more frequently reconnected with helmet streamers or active regions in the latter of the two ascending phases; however, more work will need to be done to connect these observations back to their coronal origins. The individual ion ratios used in this work provided a spectrum to analyze the aggregate elemental abundances, and this work, as a whole, is an important step in determining how conditions in the corona may vary between solar cycles between the same phases.

LiveWire: Horizontal Geoelectric Field Prediction With 1‐hr Lead‐Time Using Multi‐Fidelity Boosted Neural Networks

AbstractGeomagnetically Induced Currents (GICs) are electrical currents generated by rapid changes in the geomagnetic field during space weather events, posing risks to power grids and pipelines. Traditional approaches predict GICs indirectly by forecasting , the temporal variation of the geomagnetic field, which is proportional to the induced electric field via Faraday's law. However, current physics‐based models driven by in situ solar wind measurements offer only 10–30 min lead times, insufficient for power grid operators to take mitigating actions. Additionally, grid operators prefer direct forecasts of the geoelectric field, which directly influences GICs, rather than relying on intermediate predictions of that require complex and time‐consuming calculations. We present a novel approach that directly forecasts the horizontal geoelectric field with a one‐hour lead time, bypassing predictions. Our method combines magnetometer data, magnetotelluric survey data, and solar wind inputs into a new probabilistic multi‐fidelity machine learning technique, ProBoost, resulting in the LiveWire model. Using data from the Boulder Geomagnetic Observatory (BOU) since 2002, we trained and validated LiveWire on the top 50 geoelectric field events during geomagnetic storms. Our results show that LiveWire outperforms both a persistence forecast and the operational Space Weather Modeling Framework (SWMF) by at least 31% and 23%, respectively. This advancement in geoelectric field forecasting promises more accurate GIC predictions, helping enhance the resilience of critical infrastructure to space weather.

Techno-economic analysis of solar, wind and biomass hybrid renewable energy systems in Bhorha village, India

The present study investigates the potential use of Hybrid Renewable Energy Systems (Solar photovoltaic, wind, biomass, and diesel), both with and without the inclusion of battery/supercapacitor storage in the Bhorha village, Bihar, India. A comprehensive assessment of different possible system configurations is conducted using hybrid optimization model for electric renewable (HOMER) software to determine the most economically viable and efficient system for the designated place. The current analysis is focused on six distinct cases in the village community, in view of sufficing the daily energy requirement of 615.625 kWh and a peak demand of 86.47 kW, pertaining to major factors viz. system efficiency, financial viability, and ecological consequences. The primary aim of the research is to elucidate the comparative analysis of energy generation for different proposed designs of hybrid renewable energy systems. Detailed techno-commercial assessments are also carried out to examine the energy production, consumption, and financial impacts of each HRES configuration. The research outcome of this study obtained from HOMER software reveals that the optimized hybrid system comprises 86.7 kW solar photovoltaic, 30 kW wind turbine, 5 kW biogas generator, a 50 kW diesel generator, 280 kWh battery bank with nominal capacity, and 38.8 kW converter to sustain the for energy needs of the nominated place. This system has a minimum cost of energy of 0.309 $/kWh with a net present cost of $854894 along with operating cost 51847 $/year and net carbon dioxide emission of 56728 kg/yr. The research offers useful insights for designers, scholars, and policymakers on the existing design constraints and policies of biomass-based hybrid systems for a safe, sustainable and independent green future for the generations to come.

The Effects of Turbulence on Heliosheath Ions and Implications for Energetic Neutral Atoms

The distribution of ions in the heliosheath—the region between the heliospheric termination shock and the heliopause—is important for understanding remote observations of energetic neutral atoms (ENAs). The ion distributions were estimated previously based on hybrid simulations of the heating and evolution of solar wind and interstellar pickup ions across the solar wind termination shock, but these estimates only provide the distributions near the shock. In this work, we use self-consistent hybrid kinetic simulations to investigate the effects of turbulence on ion distributions in the heliosheath. The simulations are compared against Voyager observations, constraining the feasible amplitude and compressibility of turbulence. We find that the heating due to turbulent dissipation can lead to a significant increase in the temperature of thermal solar wind ions. Both turbulent velocity fluctuations and the heating of solar wind ions increase the charge-exchange source for ENAs at low energies (around 100 eV), where current ENA models underpredict observations by more than an order of magnitude. However, the effects of turbulence are likely not strong enough to fully explain these discrepancies.

The operationally ready full 3D magnetohydrodynamic model from the Sun to Earth: COCONUT+Icarus

Context. Solar wind modelling has become a crucial area of study due to the increased dependence of modern society on technology, navigation, and power systems. Accurate space weather forecasts can predict upcoming threats to Earth’s geospace and allow for harmful socioeconomic impacts to be mitigated. Coronal and heliospheric models must be as realistic as possible to achieve successful predictions. In this study, we examine a novel full magnetohydrodynamic (MHD) chain from the Sun to Earth. Aims. The goal of this study is to demonstrate the capabilities of the full MHD modelling chain from the Sun to Earth by finalising the implementation of the full MHD coronal model into the COolfluid COroNa UnsTructured (COCONUT) model and coupling it to the MHD heliospheric model Icarus. The resulting coronal model has significant advantages compared to the pre-existing polytropic alternative, as it includes more physics and allows for a more realistic modelling of bi-modal wind, which is crucial for heliospheric studies. In particular, we examine different empirical formulations for the heating terms in the MHD equations to determine an optimal one that would be able to mimic a realistic solar wind configuration most accurately. Methods. New heating source terms were implemented into the MHD equations of the pre-existing polytropic COCONUT model. A realistic specific heat ratio was applied. In this study, only thermal conduction, radiative losses, and approximated coronal heating function were considered in the energy equation. Multiple approximated heating profiles were examined to see the effect on the solar wind. The output of the coronal model was used to onset the 3D MHD heliospheric model Icarus. A minimum solar activity case was chosen as the first test case for the full MHD model. The numerically simulated data in the corona and the heliosphere were compared to observational products. First, we compared the density data to the available tomography data near the Sun and then the modelled solar wind time series in Icarus was compared to OMNI 1-min data at 1 AU. Results. A range of approximated heating profiles were used in the full MHD coronal model to obtain a realistic solar wind configuration. The bi-modal solar wind was obtained for the corona when introducing heating that is dependent upon the magnetic field. The modelled density profiles are in agreement with the tomography data. The modelled wind in the heliosphere is in reasonable agreement with observations. Overall, the density is overestimated, whereas the speed at 1 AU is more similar to OMNI 1-min data. The general profile of the magnetic field components is modelled well, but its magnitude is underestimated. Conclusions. We present a first attempt to obtain the full MHD chain from the Sun to Earth with COCONUT and Icarus. The coronal model has been upgraded to a full MHD model for a realistic bi-modal solar wind configuration. The approximated heating functions have modelled the wind reasonably well, but simple approximations are not enough to obtain a realistic density-speed balance or realistic features in the low corona and farther, near the outer boundary. The full MHD model was computed in 1.06 h on 180 cores of the Genius cluster of the Vlaams Supercomputing Center, which is only 1.8 times longer than the polytropic simulation. The extended model gives the opportunity to experiment with different heating formulations and improves the approximated function to model the real solar wind more accurately.

Dynamics Of Solar Wind And Cosmic Ray Interactions At The Edge Of Interstellar Space: Insights From Voyager 1 And IBEX Data Analysis

This research paper investigates the relationship between solar wind intensity and cosmic ray flux in the outer heliosphere, utilizing data from Voyager 1 and the Interstellar Boundary Explorer (IBEX). Voyager 1’s measurements beyond the heliopause, where the solar wind significantly weakens, show a marked increase in cosmic ray intensities, suggesting that solar wind acts as a modulating force. Meanwhile, IBEX's all-sky maps of energetic neutral atoms (ENAs) offer complementary data, demonstrating how the solar wind interacts with the interstellar medium at the boundary of the solar system. By analyzing the inverse correlation between solar wind strength and cosmic ray penetration into the heliosphere, this study provides a comprehensive understanding of the dynamic processes occurring at the solar system’s edge. The combined data from these missions not only enhance our understanding of space weather but also provide valuable insights into the broader interaction between the heliosphere and the interstellar medium. This research underscores the significance of continuous monitoring and multi-mission data integration for advancing our understanding of cosmic ray modulation and solar wind behavior in the outer reaches of the solar system.

Investigating the Role of Inward Alfvén Waves and Structures in the Slow Solar Wind

The solar wind dynamics are thought to be governed by counter-propagating Alfvén waves. Alfvén waves generate a turbulent cascade through nonlinear couplings between shearing wave packets. However, imbalances, structures, and intermittency complicate the solar wind’s magnetohydrodynamic (MHD) turbulence. Simulations and theories have suggested that dynamic alignment, or the tendency for velocity and magnetic fluctuations to become more correlated as a function of scale, may dictate the turbulent cascade. Observations have hinted at dynamic alignment at large scales but remain inconclusive at small scales. To investigate the nature of dynamically aligning fluctuations in the solar wind, we examine slow wind intervals from the Helios 2 spacecraft. We develop a two-component model of solar wind turbulence consisting of Alfvénic and non-Alfvénic contributions. We assume that only counter-propagating Alfvén waves experience dynamic alignment, and the non-Alfvénic structures do not participate in the alignment process. Our model allows us to constrain the relative contribution of inward Alfvén waves and non-Alfvénic structures with respect to the amplitude of outward Alfvén waves in the slow solar wind as a function of scale under the assumption of dynamic alignment. We show that the power ratio of inward to outward Alfvén fluctuations decreases as a function of scale. At the same time, the non-Alfvénic structures to outward Alfvén fluctuations increase with decreasing scale. Increasing structures provide a possible explanation for no dynamic alignment at small scales. Our study implies the need for new theoretical models to fully account for the solar wind’s compressibility, intermittency, and imbalanced nature.

Applicability of Bernoulli's theorem to upstream and downstream of the bow shock

In this research, the validity of Bernoulli's theorem for the solar wind crossing the Earth's bow shock has been investigated based on space explorations. Using solar wind plasma and interplanetary magnetic field data from the OMNI database, along with magnetosheath plasma and magnetic field data measured by the magnetospheric multiscale satellite in 2016 and 2018, 95 events were selected to study the Bernoulli invariant from the upstream to the downstream of Earth's bow shock (from the unshocked solar wind region to the shocked magnetosheath region). Statistical investigations show that the differences in the characteristic energy of plasmas between the solar wind region and the magnetosheath region are less than 5%, regardless of the upstream solar wind speed and the satellite's location in the magnetosheath. These results quantitatively support the generalization of Bernoulli's equation to the solar wind flowing through the bow shock, including the contribution of magnetic field energy.

An Adaptive X‐Ray Dynamic Image Estimation Method Based on OMNI Solar Wind Parameters and SXI Simulated Observations

AbstractObservations of the overall interactions between solar wind and the Earth's magnetosphere are crucial for space weather monitoring. Upcoming missions like the Solar Wind Magnetosphere Ionosphere Link Explorer (SMILE) and the Lunar Environment heliosphere X‐ray Imager (LEXI) aim to make comprehensive global imaging of Earth's magnetosphere using soft X‐ray imager (SXI) in order to understand its dynamic response to solar wind impact. Short‐duration X‐ray images have a low signal‐to‐noise ratio (SNR), limited by cosmic background and Poisson noise. Longer integration times provide better SNR of magnetospheric structures but fail to capture the short‐term dynamics during the integration. Our study introduces a neural network method which is able to estimate the short‐term dynamics during a long integration, driven by OMNI solar wind data and simulated soft X‐ray images. Specifically, an adaptive X‐ray image estimator and a spatio‐temporal discriminator are used. It leverages X‐ray models like Magnetohydrodynamic (MHD) and Jorgensen & Sun model, driven by OMNI data to provide high‐temporal‐resolution prior information on magnetosphere motion, with SXI observation images acting as a posterior constraint on the magnetosphere's state. Experimental validation demonstrates apparent improvements in Peak signal‐to‐noise ratio (PSNR) and Structural Similarity (SSIM) compared to traditional linear and optical flow interpolation methods. The method's flexibility, considering input‐output consistency, enables easy extension to any interval (>3 min), meeting diverse application needs. In conclusion, our study presents a new approach to soft X‐ray image estimation based on neural networks, providing insights into magnetospheric dynamics as observed in soft X‐rays.

A Statistical Examination of Interactions Between 1‐Hz Whistler Waves and Ions in the Earth's Foreshock

AbstractThe 1 Hz whistler wave precursors attached to shock‐like structures are often observed in the foreshock. Using observations from the Magnetospheric Multiscale mission, we investigate the interactions between 1 Hz waves and ions. Incoming solar wind ions do not cyclotron‐resonate with the wave, since typically the wave is right‐handed in their frame. We demonstrate that solar wind ions commonly exhibit 180° gyro‐phase bunching from the wave magnetic field, understanding it with a reconciled linear theory and non‐linear trapping theory for non‐cyclotron‐resonant modulations. Along the longitudinal direction, solar wind ions experience Landau resonance, exhibiting either modulations at small wave potentials or trapping in phase‐space holes at large potentials. The results also improve our understanding of foreshock structure evolution and 1 Hz wave excitation. Shock‐like structures start with having incoming solar wind and remotely reflected ions from further downstream. The ion‐scale 1 Hz waves can already appear during this stage. The excitation may be due to shock‐like dispersive radiation or kinetic instabilities resonant with these remotely reflected ions. Ions reflected by local shock‐like structures occur later, so they are not always necessary for generating 1 Hz waves. The wave leads to ion reflection further upstream, which may cause reformation of shock‐like structures. In one event, locally reflected ions exhibit non‐cyclotron‐resonant modulation in the early stage, and later approach the anomalous cyclotron resonant condition with gyro‐phases ∼270°. The latter is possibly due to nonlinear trapping in regions with an upstream‐pointing magnetic field gradient, linked to reformation. Some additional special features, such as frequency dispersions, are observed, encouraging further investigations.

Detection of carbon dioxide and hydrogen peroxide on the stratified surface of Charon with JWST

Charon, Pluto’s largest moon, has been extensively studied, with research focusing on its primitive composition and changes due to radiation and photolysis. However, spectral data have so far been limited to wavelengths below 2.5 μm, leaving key aspects unresolved. Here we present the detection of carbon dioxide (CO2) and hydrogen peroxide (H2O2) on the surface of Charon’s northern hemisphere, using JWST data. These detections add to the known chemical inventory that includes crystalline water ice, ammonia-bearing species, and tholin-like darkening constituents previously revealed by ground- and space-based observations. The H2O2 presence indicates active radiolytic/photolytic processing of the water ice-rich surface by solar ultraviolet and interplanetary medium Lyman-α photons, solar wind, and galactic cosmic rays. Through spectral modeling of the surface, we show that the CO2 is present in pure crystalline form and, possibly, in intimately mixed states on the surface. Endogenically sourced subsurface CO2 exposed on the surface is likely the primary source of this component, with possible contributions from irradiation of hydrocarbons mixed with water ice, interfacial radiolysis between carbon deposits and water ice, and the implantation of energetic carbon ions from the solar wind and solar energetic particles.

Proton Temperature Anisotropy Constraint Associated With Alpha Beam Instability in the Solar Wind

AbstractSolar wind observations in the space of the proton temperature anisotropy versus parallel proton beta always show a distorted rhomboid‐like pattern, where the boundaries of this plot are associated with several instabilities. However, the specific mechanism on the constraint of the proton temperature anisotropy is unclear in the low‐beta plasma. In this work, we study the kinetic instabilities driven by proton temperature anisotropy and alpha beam with the Vlasov theory and investigate the nonlinear evolution of these instabilities with hybrid simulations. We also compare the theoretical and simulation results with Wind observations. The alpha beam with drift velocity leads to a new kind of Alfvén/ion‐cyclotron instability (A/IC instability) in the region of . In the region of and , the nonlinear wave‐particle interaction through the A/IC instability can decelerate alpha beams to and result in the anti‐correlation between and . In the region of and , the nonlinear wave‐particle interaction through the A/IC instability further leads to slight increase of . The present results provide a potential mechanism for regulating the proton temperature anisotropy in the low‐beta plasma with in the solar wind.

Properties of Solar Wind Current Sheets in the Martian Space Environment

Transient current sheets (CSs) are common magnetic structures in the solar wind that can significantly disturb the planetary space environment and cause space weather phenomena. This study focuses on their properties in the Martian space environment and investigates their transformations caused by the Martian bow shock. Based on 14 months of data from the Mars Atmosphere and Volatile Evolution mission during similar solar activity, 786 CSs with directional changes greater than 90° and magnetic field depression were automatically identified, including 256 events in the upstream solar wind and 530 events in the Martian magnetosheath. After crossing the bow shock, the duration and thickness of the CSs decrease, while their current density and occurrence rate significantly increase. In the Martian magnetosheath, CSs located on the dayside exhibit stronger current density, a relatively lower occurrence rate, and a smaller Bx/BT than CSs located on the nightside. From the dayside to the nightside, the predominant magnetic field component of CSs changes to Bx due to the draping process. Moreover, a clear dawn–dusk asymmetry in CS properties emerges from the foreshock to the downstream region of the Martian bow shock. Our results reveal the properties and evolution of solar wind CSs in the Martian space environment.

Latitudinal Distribution of Dayside Magnetospheric Currents Based on Cluster Observations

AbstractBased on the curlometer method, we calculate the azimuthal component of the current density from nearly 19 years of Fluxgate Magnetometer (FGM) data of Cluster and investigate its latitudinal distribution in the dayside noon sector (09:00–15:00 magnetic local time, MLT). A crossing event in the noon meridian plane shows an unexpected eastward current at a geocentric distance of 8 RE, away from the equator with latitudes of 30–40°. Further statistical results of the current distribution show that, the topology of the current can be radially divided into the inner and outer branches over the whole rxy − z plane (rxy = and z are in solar magnetic (SM) coordinates), with the separation point of these two branches at a geocentric distance of about 8 RE. The current variations of the inner and outer branches are different under different Kp, solar wind flow speed Vsw, and solar wind dynamic pressure Pdyn. It is shown that the current densities in both the inner and outer branches increase significantly with the Kp. High solar wind dynamic pressure enhances the current density of the outer branch, while high solar wind speed, on the contrary, enhances that of the inner branch. The formation of the outer branch may be related to the anisotropy of plasma pressure.