Solar Wind Data Research Articles

Global magnetic field properties in the solar wind interaction of Mercury from MESSENGER measurements

The space environment of Mercury is shaped by its proximity to the Sun and by the relatively weak planetary magnetic field, presenting a unique regime of plasmas and shock conditions. We present the global magnetic properties in Mercury's space environment based on more than 4 years of MESSENGER Magnetometer data. We used 20 Hz magnetic field data to examine the magnetic strength, the field configurations, and the fluctuations. We considered both compressional and transverse modes, with frequencies from 5 mHz to 10 Hz, which cover typical ultra-low frequency waves at Mercury. We identified regions of the solar wind, the magnetosheath, and the magnetosphere during over 4\,000 MESSENGER orbits. The solar wind and magnetosheath data were analysed in the solar wind interplanetary magnetic field (IMF) coordinate system, and the magnetosphere data were analysed in the aberrated Mercury solar magnetospheric coordinate system. Each data point was relocated into normalised space using averaged magnetopause and bow-shock models. The magnetic environments for a quasi-parallel and quasi-perpendicular IMF were compared. Under the typical Parker-spiral IMF, the magnetic environment of Mercury features strong fluctuations that are dominated by the transverse mode and stem from interactions at the bow shock and the magnetopause. When they are subjected to a quasi-perpendicular IMF, the magnetic fluctuations diminish, and the magnetic field strength becomes highly compressed throughout the bow shock, magnetosheath, and magnetosphere. Unlike Earth, Mercury exhibits weaker dawn-dusk asymmetries in magnetic field strength and lacks substantial magnetosheath-generated sources of magnetic fluctuations. The magnetic field draping pattern associated with the IMF cone angle at Mercury also differs from that at Earth. Our comparative analysis highlights the critical role of the solar wind Mach number, the radial IMF component, and the system scale size in shaping planetary space environments.

Predicting the Energy Spectra of Solar Energetic Particles with a Machine Learning Regression Algorithm

Solar energetic particles (SEPs) are a major source of space radiation, especially within the inner heliosphere. These particles, originating from solar flares and coronal mass ejections (CMEs), propagate primarily along interplanetary magnetic fields. The energy spectra of SEP events are crucial for assessing radiation effects and understanding the acceleration and propagation mechanisms in their source regions. In this study, we employed a decision tree regression algorithm with cost complexity pruning to predict SEP energy spectra, including peak flux and integral fluence spectra. This approach uses only solar flares, CMEs, and solar wind data as input parameters and demonstrates strong performance to accurately predict SEP spectra. This method holds significant real-time application value for monitoring and forecasting radiation risks in both deep space and near-Earth environments.

Solar Wind Turbulence and Complexity Probed with Rank-Ordered Multifractal Analysis (ROMA)

The Rank-Ordered Multifractal Analysis (ROMA) is a tool designed to characterize scale (in)variance and multifractality based on rank ordering the fluctuations in “groups” characterized by the same mono-fractal behavior (Hurst exponent). A range-limited structure-function analysis provides the mono-fractal index for each rank-ordered range of fluctuations. We discuss here two examples of multi-scale solar wind turbulence and complexity where ROMA is applied on the following: (a) data collected by Ulysses spacecraft in the fast solar wind, outside the ecliptic, between 25 and 31 January 2007, at roughly 2.5 Astronomical Units (AU) from the Sun, in the Southern heliosphere, at latitudes between −76.5 and −77.3 degrees, and (b) slow solar wind data collected in the ecliptic plane by Venus Express spacecraft, at 0.72 AU, on 28 January 2007. The ROMA spectrum of fast solar wind derived from ULYSSES data shows a scale-dependent structure of fluctuations: (1) at the smallest/kinetic range of scales (800 to 3200 km), persistent fluctuations are dominant, and (2) at the inertial range of scales (104 to 2 × 105 km), anti-persistent fluctuations are dominant, but less clearly developed and possibly indicative for the development of instabilities with cross-over behavior. The ROMA spectrum of the slow solar wind derived from Venus Express data, suggests a different structure of turbulence: (1) fully developed multifractal turbulence across scales between 5 × 104 and 4 × 105 km, with the Hurst index changing from anti-persistent to persistent values for the larger amplitude magnetic fluctuations; (2) at the smallest scales (400 to 6400 km), fluctuations are mainly anti-persistent, and the ROMA spectrum indicates a tendency towards mono-fractal behavior.

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.

Automatic Detection of Large-scale Flux Ropes and Their Geoeffectiveness with a Machine-learning Approach

Abstract Detecting large-scale flux ropes (FRs) embedded in interplanetary coronal mass ejections (ICMEs) and assessing their geoeffectiveness are essential, since they can drive severe space weather. At 1 au, these FRs have an average duration of 1 day. Their most common magnetic features are large, smoothly rotating magnetic fields. Their manual detection has become a relatively common practice over decades, although visual detection can be time-consuming and subject to observer bias. Our study proposes a pipeline that utilizes two supervised binary classification machine-learning models trained with solar wind magnetic properties to automatically detect large-scale FRs and additionally determine their geoeffectiveness. The first model is used to generate a list of autodetected FRs. Using the properties of the southward magnetic field, the second model determines the geoeffectiveness of FRs. Our method identifies 88.6% and 80% of large-scale ICMEs (duration ≥ 1 day) observed at 1 au by the Wind and the Solar TErrestrial RElations Observatory missions, respectively. While testing with continuous solar wind data obtained from Wind, our pipeline detected 56 of the 64 large-scale ICMEs during the 2008–2014 period (recall = 0.875), but also many false positives (precision = 0.56), as we do not take into account any additional solar wind properties other than the magnetic properties. We find an accuracy of 0.88 when estimating the geoeffectiveness of the autodetected FRs using our method. Thus, in space-weather nowcasting and forecasting at L1 or any planetary missions, our pipeline can be utilized to offer a first-order detection of large-scale FRs and their geoeffectiveness.

Forecasting Geoffective Events from Solar Wind Data and Evaluating the Most Predictive Features through Machine Learning Approaches

This study addresses the prediction of geomagnetic disturbances by exploiting machine learning techniques. Specifically, the Long Short-term Memory recurrent neural network, which is particularly suited for application over long time series, is employed in the analysis of in situ measurements of solar wind plasma and magnetic field acquired over more than one solar cycle, from 2005 to 2019, at the Lagrangian point L1. The problem is approached as a binary classification aiming to predict 1 hr in advance a decrease in the SYM-H geomagnetic activity index below the threshold of −50 nT, which is generally regarded as indicative of magnetospheric perturbations. The strong class imbalance issue is tackled by using an appropriate loss function tailored to optimize appropriate skill scores in the training phase of the neural network. Beside classical skill scores, value-weighted skill scores are then employed to evaluate predictions, suitable in the study of problems, such as the one faced here, characterized by strong temporal variability. For the first time, the content of magnetic helicity and energy carried by solar transients, associated with their detection and likelihood of geoeffectiveness, were considered as input features of the network architecture. Their predictive capabilities are demonstrated through a correlation-driven feature selection method to rank the most relevant characteristics involved in the neural network prediction model. The optimal performance of the adopted neural network in properly forecasting the onset of geomagnetic storms, which is a crucial point for giving real warnings in an operational setting, is finally showed.

Solar wind data analysis aided by synthetic modeling: A better understanding of plasma frame variations from temporal data

Context. In situ measurements of the solar wind, a turbulent and anisotropic plasma flow originating at the Sun, are mostly carried out by single spacecraft, resulting in one-dimensional time series. Aims. The conversion of these measurements to the spatial frame of the plasma is a great challenge, but it is required for direct comparison of the measurements with magnetohydrodynamic turbulence theories. Methods. We present a tool kit based on the synthetic modeling of solar wind fluctuations as two-dimensional noise maps with adjustable spectral and power anisotropy that can help with the temporal-spatial conversion of real data. Specifically, by following the spacecraft trajectory through a noise map (relative velocity and angle relative to some mean magnetic field) with properties tuned to mimic those of the solar wind, the likelihood that the temporal data fluctuations represent parallel or perpendicular fluctuations in the plasma frame can be quantified by correlating structure functions of the noise map. Synthetic temporal data can also be generated, which can provide a testing ground for analysis applied to the solar wind data. Results. We demonstrate this tool by investigating Parker Solar Probe’s seventh encounter trajectory and data, and we showcase several possible ways in which it can be used. We find that whether temporal variations in the spacecraft frame come from parallel or perpendicular variations in the plasma frame strongly depends on the spectral and power anisotropy of the measured wind. Conclusions. Data analysis assisted by such underlying synthetic models as presented here could open up new ways to interpret measurements in the future, specifically in the more reliable determination of plasma frame quantities from temporal measurements.

Solar Wind Interactions with Comet C/2021 A1 Using STEREO HI and a Data-assimilative Solar Wind Model

Cometary tails display dynamic behavior attributed to interactions with solar wind structures. Consequently, comet-tail observations can serve as in situ solar wind monitors. During 2021 December, Comet Leonard (C/2021 A1) was observed by the STEREO-A heliospheric imager. The comet tail exhibited various signatures of interactions with the solar wind including bending, kink formation, and finally complete disconnection. In this study, we compare the timing of these events with solar wind structures predicted by the Heliospheric Upwind eXtrapolation model with a time-dependency (or HUXt) solar wind model using new solar wind data assimilation (DA) techniques. This serves both to provide the most accurate solar wind context to interpret the cometary processes, but also as a test of the DA and an example of how comet observations can be used in model validation studies. Coronal mass ejections, stream interaction regions (SIRs), and heliospheric current sheet (HCS) crossings were all considered as potential causes of the tail disconnection. The results suggest the tail disconnection at Comet Leonard was the result of it crossing the HCS and into an SIR. The timing of these features agree better with the DA model results than the non-DA model, showing the value of this approach. Case studies such as this expand our understanding of comet–solar wind interactions, and in demonstrating the utility of DA for solar wind modeling. We note that this could lead to comets acting as additional in situ measures for solar wind conditions for regions where no in situ spacecraft are available, potentially improving solar wind DA in the future.

IMF Bx, By, and Bz dependence of energetic particle precipitation in the magnetosphere

The orientation of Interplanetary Magnetic Field (IMF B) components plays a major role in magnetospheric Energetic Particles Precipitation (EPP). This is because the IMF B components in the Geocentric Solar Magnetospheric (GSM) coordinate system control magnetic reconnection at the subsolar magnetopause. This study intends to unravel the dependence of the preceding conditions of the orientations of IMF B components that drove geomagnetic storms on the EPPs produced in the Van Allen radiation belt. We classified the geomagnetic storms based on their drivers and IMF Bz orientations prior to the storms’ onset. Energetic particle fluxes, IMF B, and solar wind data were used. The study showed that electrons are more reactive to solar wind speed than protons. Additionally, storms with southward IMF Bz orientation caused more EPP enhancement than with northward orientation. Our results also showed that protons are more influenced by magnetospheric compression than electrons at the northward orientation of IMF Bz. In fact, at this point, the proton’s motion violates adiabatic invariants. As particles interact with electric fields, a significant population of EPPs is produced by pitch angle diffusion. Generally, IMF Bx and IMF By are often anticorrelated. The preceding conditions of orientations of the IMF Bx indicated weaker dependence on auroral indices, and invariably on energetic particles precipitation than the IMF By.

On the Impact of Geospace Weather on the Occurrence of M7.8/M7.5 Earthquakes on 6 February 2023 (Turkey), Possibly Associated with the Geomagnetic Storm of 7 November 2022

A joint analysis of solar wind, geomagnetic field, and earthquake catalog data showed that before the catastrophic M = 7.8 and M = 7.5 Kahramanmaras earthquake sequence on 6 February 2023, a closed strong magnetic storm occurred on 7 November 2022, SYM/H = −117 nT. The storm started at 08:04 UT. At this time, the high-latitudinal part of Turkey’s longitudinal region of future epicenters was located under the polar cusp, where the solar wind plasma would directly access the Earth’s environment. The time delay between storm onset and earthquake occurrence was ~91 days. We analyzed all seven strong (M7+) earthquakes from 1967 to 2020 to verify the initial findings. A similar pattern has been revealed for all events. The time delay between magnetic storm onset and earthquake occurrence varies from days to months. To continue these investigations, a retrospective analysis of seismic and other geophysical parameters just after preceded geomagnetic storms in the epicenter areas is desirable.

A Multi‐Model Ensemble System for the Outer Heliosphere (MMESH): Solar Wind Conditions Near Jupiter

AbstractHow the solar wind influences the magnetospheres of the outer planets is a fundamentally important question, but is difficult to answer in the absence of consistent, simultaneous monitoring of the upstream solar wind and the large‐scale dynamics internal to the magnetosphere. To compensate for the relative lack of in‐situ solar wind data, propagation models are often used to estimate the ambient solar wind conditions at the outer planets for comparison to remote observations or in‐situ measurements. This introduces another complication: the propagation of near‐Earth solar wind measurements introduces difficult‐to‐assess uncertainties. Here, we present the Multi‐Model Ensemble System for the outer Heliosphere (MMESH) to begin to address these issues, along with the resultant multi‐model ensemble (MME) of the solar wind conditions near Jupiter. MMESH accepts as input any number of solar wind models together with contemporaneous in‐situ spacecraft data. From these, the system characterizes typical uncertainties in model timing, quantifies how these uncertainties vary under different conditions, attempts to correct for systematic biases in the input model timing, and composes a MME with uncertainties from the results. For the Juno‐era (04/07/2016–04/07/2023) MME hindcast for Jupiter presented here, three solar wind propagation models were compared to in‐situ measurements from the near‐Jupiter spacecraft Ulysses and Juno spanning diverse geometries and phases of the solar cycle across >14,000 hr of data covering 2.5 decades. The MME gives the most‐probable near‐Jupiter solar wind conditions for times within the tested epoch, outperforming the input models and returning quantified estimates of uncertainty.

Prediction of high latitude nightside Geomagnetic Field Disturbances (GGFDs) during the 9-March-2012 geomagnetic storm using Recurrent Neural Network (RNN)

Geomagnetically Induced currents (GICs) pose a potentially catastrophic threat to the safety and security of large-scale power distribution systems. Using a recurrent neural network, we predict high (≥60°) magnetic latitude (MLAT) and night side (06–18) magnetic local time (MLT) geomagnetic field disturbances (surface dBs/dt and vertical dBz/dt), the trigger of GICs, during a geomagnetic storms occurred on March 09, 2012. We use the high latitude geomagnetic field data from SuperMAG and the solar wind particle and magnetic field data from NASA-OMNIWeb. We divide the high latitude night side of Earth’s globe into multiple sectors and obtain a time series of maximum geomagnetic disturbances observed in each sector. We use the Long Short Term Memory (LSTM) network technique sector by sector on both, the solar wind data and the sectored geomagnetic field disturbance (10° geomagnetic latitude, 1-hour MLT) time series obtained from SuperMAG. Each sector prediction is plotted on a polar map to reveal high latitude night side geomagnetic disturbance patterns. This high latitude nigh side model has great potential. With solar wind input at a Lagrangian-1 point, and geomagnetic field data from SuperMAG, it can predict the peak magnetic disturbance at any given sector for couple of hours in advance. Our machine learning techniques provide the best set of solar wind input that gives the best disturbance prediction. This paper presents a prototype of the global geomagnetic disturbance model with low-resolution spatial grids and a subset of potential solar wind input parameters. Once matured, the global magnetic disturbance model will be provided to the public.

Possible Influence of Solar Activity on Lightning Occurrence: A Preliminary Study Over Nepal

One of the fundamental mysteries of lightning is its initiation mechanism. Many aspects of lightning phenomenon are well known; however, the exact processes that initiate lightning discharge are not fully understood. This study delves into a relatively underexplored domain: the relationship between solar activity specifically solar sunspots and F10.7 indices, as well as solar wind speed, on the occurrence of lightning events. The study utilizes a rich dataset spanning from 1998 to 2008, capturing an extensive period of solar activity during Solar Cycle 23. Multiple correlation analyses were conducted, comparing solar activity parameters with lightning occurrence. Lightning data was sourced from the Lightning Imaging Sensor (LIS) whereas Sunspot data was obtained from the Solar Influences Data Analysis Center (SIDC) and F10.7 as well as Solar wind data was obtained from the OMNI dataset. From this preliminary study, we observed that the correlation between solar parameters and the lightning occurrence over Nepal are found to vary at different time period from insignificant correlation to moderate correlation. Hence, further exploration is needed to shed more light to interpret the relationship between solar activity and the lightning occurrence in Nepal.

Employing the Coupled EUHFORIA‐OpenGGCM Model to Predict CME Geoeffectiveness

AbstractEUropean Heliospheric FORecasting Information Asset (EUHFORIA) is a physics‐based data‐driven solar wind and coronal mass ejections (CMEs) propagation model designed for space weather forecasting and event analysis investigations. Although EUHFORIA can predict the solar wind plasma and magnetic field properties at Earth, it is not equipped to quantify the geo‐effectiveness of the solar transients in terms of geomagnetic indices like the disturbance storm time (Dst) index and the auroral indices, that quantify the impact of the magnetized plasma encounters on Earth's magnetosphere. Therefore, we couple EUHFORIA with the Open Geospace General Circulation Model (OpenGGCM), a magnetohydrodynamic model of the response of Earth's magnetosphere, ionosphere, and thermosphere to transient solar wind characteristics. In this coupling, OpenGGCM is driven by the solar wind and interplanetary magnetic field obtained from EUHFORIA simulations to produce the magnetospheric and ionospheric response to the CMEs. This coupling is validated with two observed geo‐effective CME events driven with the spheromak flux‐rope CME model. We compare these simulation results with the indices obtained from OpenGGCM simulations driven by the measured solar wind data from spacecraft. We further employ the dynamic time warping (DTW) technique to assess the model performance in predicting Dst. The main highlight of this study is to use EUHFORIA simulated time series to predict the Dst and auroral indices 1–2 days in advance, as compared to using the observed solar wind data at L1, which only provides predictions 1–2 hr before the actual impact.

Data-driven optimal placement of minichannel-based solar water heater using satellite-derived and ground-telemetry weather information

The aluminum minichannel solar collector is a novel technology for solar water heating. Minichannel-based solar collectors have higher thermal efficiency than conventional flat plate collectors and do not suffer from potential loss of vacuum as evacuated-tube collectors. This technology can play a significant role in reducing natural gas consumption that translates into lower greenhouse gas emissions to the atmosphere. However, the performance of solar collectors depends on the geographical location of the installation due to solar resource availability and weather pattern. The potential reduction in natural gas consumption using aluminum minichannel solar collectors is assessed using solar irradiance, ambient temperature, and wind data obtained from ground weather station and satellite-derived data. A data-driven numerical analysis is performed using a validated solar water heater (SWH) model, population, and natural gas consumption data for the entire state of California to assess the best locations to install these systems. The SWH model is validated based on data collected from an actual SWH system installed at a single-family house in Northridge, California. A K-means clustering method is then applied to select the best regions for installation of this technology. Based on performance, population density, and natural gas consumption, the regions of Southern California and the Central Valley are chosen as having the highest potential for reduction of natural gas consumption. The analysis was performed from weather data obtained based on two full years (2020 and 2022), where the effect of COVID-19 (year 2020) is observed as having higher water tank temperatures and higher solar fractions, which could be associated with lower hot water consumption.

Classification of Enhanced Geoeffectiveness Resulting from High-speed Solar Wind Streams Compressing Slower Interplanetary Coronal Mass Ejections

High-speed solar wind streams (HSSs) interact with the preceding ambient solar wind to form stream interaction regions (SIRs), which are a primary source of recurrent geomagnetic storms. However, HSSs may also encounter and subsequently interact with interplanetary coronal mass ejections (ICMEs). In particular, the impact of the interaction between slower ICMEs and faster HSSs represents an unexplored area that requires further in-depth investigation. This specific interaction can give rise to unexpected geomagnetic storm signatures, diverging from the conventional expectations of individual SIR events sharing similar HSS properties. Our study presents a comprehensive analysis of solar wind data spanning from 1996 to 2020, capturing 23 instances where such encounters led to geomagnetic storms (SymH < −30 nT). We determined that interaction events between preceding slower ICMEs and faster HSSs possess the potential to induce substantial storm activity, statistically nearly doubling the geoeffective impact in comparison to SIR storm events. The increase in the amplitude of the SymH index appears to result from heightened dynamic pressure, often coupled with the concurrent amplification of the CMEs rearward ∣B∣ and/or B z components.

Magnetohydrodynamic Perspective on the Disappearance of Mercury’s Bow Shock by Helios Data Exploration

In the magnetohydrodynamic (MHD) perspective, the planet’s bow shock would disappear when the fast-mode Mach number (M F) of the solar wind is less than one. Compared to Earth, Mercury is subject to a lower M F solar wind due to its proximity to the Sun, resulting in a higher possibility of the disappearance of its bow shock. To examine the variability of Mercury’s bow shock in response to the solar wind properties, analyses of the observations by the Helios spacecraft at 0.30–0.50 au during 1975–1983, covering solar cycle 21, together with the theoretical solutions and MHD simulations are conducted in this study. Our observational analyses show that more solar wind data with extremely low fast-mode Mach numbers (say, M F ≤ 1.5) are observed during the rising and maximum phases and are characterized by a significantly low proton number density. It is also found that approximately 35% of the extremely low fast-mode Mach number solar wind events (M F ≤ 1.5) occur within the main body of interplanetary coronal mass ejections (ICMEs), while about 58% of them are unrelated to ICMEs. Three of these events are selected to demonstrate that the occurrences of the solar wind with M F ≤ 1.5 may not be necessarily affected by ICMEs. Our theoretical and numerical results indicate that when Mercury encounters the solar wind with M F ≤ 1.5, its bow shock would move farther away, become flattened, and even disappear. Furthermore, our calculations suggest that Mercury’s bow shock would become a slow-mode shock with a concave-upward structure under such extreme solar wind conditions.

Hydrogen Production using a Hybrid System Built with Renewable Energy Resources in Yenice

Benefits of hybrid renewable energy; has a continuous power supply, has a better return on investment, is independent of the grid, has fewer operating hours of the diesel generator, requires less maintenance than a diesel generator system, and does not need frequent refueling, one-time initial capital required, protects from ever-increasing grid electricity tariffs. On the other hand, the demand for renewable energy sources such as wind, solar, hydrogen, biomass, and geothermal, which are the least harmful to nature and natural life, has increased due to the depletion of fossil fuels used in energy production and the greenhouse gases they have released to the environment. Thus, with the inclusion of renewable energy sources in the system, a rich structure in terms of energy diversity has emerged. An area that simulates a system consisting of renewable energy sources using HOMER Pro software and has an annual average electrical energy need of 3997104 kWh/day was selected. Solar radiation and wind data belonging to the region were taken from NASA Surface and used. The energy system of the area where the feasibility study was conducted consists of a generator, wind turbine, solar panel, hydrogen tank, electrifier inverter, and battery system. The energy produced by the resulting system will be used by 11.26 kg/day hydrogen load and 3997104 kWh/day electrical load. The economic evaluation criteria of the hybrid renewable energy system are the energy unit cost of energy production, the net present value (NPC), and the payback period; The environmental evaluation criteria were determined as the renewable energy rate and emission values evaluation criteria. When the renewable energy rate is at the lowest level, the most economical system is the Gen/PV/WT/Bat/300Htank/ H loaded hybrid system when emissions are not considered, while the Gen/PV/WT/Bat hybrid system has the lowest emissions without considering the cost. When the renewable energy rate is high, the most economical system has been the PV/WT/Bat hybrid system when emissions are not considered.

Probabilistic modelling of substorm occurrences with an echo state network

Abstract. The relationship between solar-wind conditions and substorm activity is modelled with an approach based on an echo state network. Substorms are a fundamental physical phenomenon in the magnetosphere–ionosphere system, but the deterministic prediction of substorm onset is very difficult because the physical processes that underlie substorm occurrences are complex. To model the relationship between substorm activity and solar-wind conditions, we treat substorm onset as a stochastic phenomenon and represent the stochastic occurrences of substorms with a non-stationary Poisson process. The occurrence rate of substorms is then described with an echo state network model. We apply this approach to two kinds of substorm onset proxies. One is a sequence of substorm onsets identified from auroral electrojet intensity, and the other is onset events identified from activity of Pi2 pulsations, which are irregular geomagnetic oscillations often associated with substorm onsets. We then analyse the response of substorm activity to solar-wind conditions by feeding synthetic solar-wind data into the echo state network. The results indicate that the effect of the solar-wind speed is important, especially for Pi2 substorms. A Pi2 pulsation can often occur even if the interplanetary magnetic field (IMF) is northward, while the activity of auroral electrojets is depressed during northward IMF conditions. We also observe spiky enhancements in the occurrence rate of substorms when the solar-wind density abruptly increases, which might suggest an external triggering due to a sudden impulse of solar-wind dynamic pressure. It seems that northward turning of the IMF also contributes to substorm occurrences, though the effect is likely to be minor.

Modeling the Formation and Evolution of Solar Wind Microstreams: From Coronal Plumes to Propagating Alfvénic Velocity Spikes

We investigate the origin of mesoscale structures in the solar wind called microstreams, defined as enhancements in the solar wind speed and temperature that last several hours. They were first clearly detected in Helios and Ulysses solar wind data and are now omnipresent in the “young” solar wind measured by the Parker Solar Probe and Solar Orbiter. These recent data reveal that microstreams transport a profusion of Alfvénic perturbations in the form of velocity spikes and magnetic switchbacks. In this study, we use a very-high-resolution 2.5D MHD model of the corona and the solar wind to simulate the emergence of magnetic bipoles interacting with the preexisting ambient corona and the creation of jets that become microstreams propagating in the solar wind. Our high-resolution simulations reach sufficiently high Lundquist numbers that capture the tearing mode instability that develops in the reconnection region and produces plasmoids released with the jet into the solar wind. Our domain runs from the lower corona to 20 R ⊙, which allows us to track the formation process of plasmoids and their evolution into Alfvénic velocity spikes. We obtain perturbed solar wind flows lasting several hours with velocity spikes occurring at characteristic periodicities of about 19 minutes. We retrieve several properties of the microstreams measured in the pristine solar wind by the Parker Solar Probe, namely an increase in wind velocity of about 100 km s−1 during a stream's passage together with superposed velocity spikes of also about 100 km s−1 released into the solar wind.