Solar Wind Measurements Research Articles

A Broad Set of Solar and Cosmochemical Data Indicates High C-N-O Abundances for the Solar System

We examine the role of refractory organics as a major C carrier in the outer protosolar nebula and its implications for the compositions of large Kuiper Belt objects (KBOs) and CI chondrites. By utilizing Rosetta measurements of refractory organics in comet 67P/Churyumov–Gerasimenko, we show that they would make up a large fraction of the protosolar C inventory in the KBO-forming region based on the current widely adopted solar abundances. However, this would free up too much O to form water ice, producing solid material that is not sufficiently rock-rich to explain the uncompressed density of the Pluto–Charon system and other large KBOs; the former has been argued as the most representative value we have for the bulk composition of large KBOs. This inconsistency further highlights the solar abundances problem—an ongoing challenge in reconciling spectroscopically determined heavy-element abundances with helioseismology constraints. By employing a new data set from solar CNO neutrinos and solar wind measurements of C, N, and O, we show that the uncompressed density of the Pluto–Charon system can be reproduced over a wide range of scenarios. We show that a lack of sulfates in Ryugu and Bennu samples implies a lower amount of water ice initially accreted into CI chondrite parent bodies than previously thought. These data are found to be consistent with the solar C/O ratio implied by the new data set. Our predictions can be tested by future neutrino, helioseismology, and cosmochemical measurements.

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.

Assessment of the Weimer Geomagnetic Perturbation Model for High‐Latitude Positioning and Navigation

AbstractThe Geomagnetism Group at the National Centers for Environmental Information (NCEI), in collaboration with the Cooperative Institute for Research in Environmental Sciences (CIRES), along with various government and industry partners, specializes in developing and providing access to Earth's internal magnetic field models. These models are crucial for applications such as compass navigation and wellbore positioning, offering reference values for the geomagnetic field (total field, dip or inclination, and declination) at any location across the Earth. This study assesses the Weimer geomagnetic perturbation model, proposed by Weimer in 2013, which is an empirical model of magnetic field variations at high latitudes driven by solar wind measurements, as a candidate for capturing geomagnetic field variations in high‐latitude areas. We compare the geomagnetic field variations predicted by the Weimer model against data from the INTERMAGNET and SuperMAG observatory networks. Our findings indicate that the Weimer model achieves a reduction in the standard deviations of high‐latitude magnetic field variations by approximately 20%–30% once quiet‐time baselines are removed from the data set. Furthermore, we compare the performance of the Weimer model against the Space Weather Modeling Framework's Geospace model during specific geomagnetic storms in 2017 and 2018, and we find that the Weimer model is more effective in predicting magnetic variations at high latitudes than the Geospace model during these storms. Additionally, comparisons to magnetometer data collected from high‐latitude directional‐drilling operations align with the trends observed in comparisons with INTERMAGNET and SuperMAG observatory data, confirming the Weimer model's reliability and effectiveness for such applications.

Operational SYM‐H Forecasting With Confidence Intervals Using Deep Neural Networks

AbstractIn this study, we develop a robust real‐time forecast system for the SYM‐H index using Deep Neural Networks and real‐time Solar Wind measurements along with Interplanetary Magnetic Field parameters. This system provides not only one‐off forecasts but also quantile‐based confidence intervals, offering a range within which the observed SYM‐H values are expected to fall, enhancing forecast reliability and usability. The model is tested both on historical level 2 science‐ready data and on preliminary observations, which are closer to the operational environment that the model is expected to work on, demonstrating the model's robustness and practical utility in real‐time scenarios. The integration of quantile forecasts into SYM‐H prediction models represents a significant advancement, providing decision‐makers with more accurate and trustworthy information to manage the hazard of geomagnetic storms.

МГД-волны в области предфронта межпланетных ударных волн 6 и 7 сентября 2017 г.

We analyze strong space weather disturbances during first ten days of September 2017, using the geomagnetic Dst index, parameters of normals to interplanetary shock fronts, direct measurements of interplanetary magnetic field, solar wind, and cosmic ray parameters. By applying spectral analysis methods to interplanetary medium data, we analyze MHD waves at the pre-front of two interplanetary shocks responsible for geomagnetic disturbances on September 6 and 7, 2017. The main results are as follows: the contribution of three branches of MHD waves (Alfvén, fast and slow magnetosonic) to the observed spectrum of the interplanetary magnetic field modulus has been established. We have confirmed the conclusion that the generation of Alfvén waves and fast magnetosonic waves is due to the presence of low-energy proton fluxes (Ep~1 MeV) at the pre-front of interplanetary shocks. We have also discovered a predominant contribution of slow magnetosonic waves to the observed spectrum of the interplanetary magnetic field modulus, but its reason is yet unknown. It is noted that different orientations of the normals to the interplanetary shock fronts and to the direction of the interplanetary magnetic field average vector on spacecraft located fairly close to each other may indicate waviness of the shock front structure.

MHD waves at the pre-front of interplanetary shocks on September 6 and 7, 2017

We analyze strong space weather disturbances during first ten days of September 2017, using the geomagnetic Dst index, parameters of normals to interplanetary shock fronts, direct measurements of interplanetary magnetic field, solar wind, and cosmic ray parameters. By applying spectral analysis methods to interplanetary medium data, we analyze MHD waves at the pre-front of two interplanetary shocks responsible for geomagnetic disturbances on September 6 and 7, 2017. The main results are as follows: the contribution of three branches of MHD waves (Alfvén, fast and slow magnetosonic) to the observed spectrum of the interplanetary magnetic field modulus has been established. We have confirmed the conclusion that the generation of Alfvén waves and fast magnetosonic waves is due to the presence of low-energy proton fluxes (Ep~1 MeV) at the pre-front of interplanetary shocks. We have also discovered a predominant contribution of slow magnetosonic waves to the observed spectrum of the interplanetary magnetic field modulus, but its reason is yet unknown. It is noted that different orientations of the normals to the interplanetary shock fronts and to the direction of the interplanetary magnetic field average vector on spacecraft located fairly close to each other may indicate waviness of the shock front structure.

Establishment and Application of an Interplanetary Disturbance Index Based on the Solar Wind–Magnetosphere Energy Coupling Function and the Spectral Whitening Method

We develop a preliminary interplanetary disturbance index (J sW ) by applying the spectral whitening method to an energy coupling function with solar wind measurements during the years 1998–2014 as the input, which can be used as an indicator of perturbations in the near-Earth solar wind. The correlation and temporal variation between J sW and the geomagnetic disturbance index (J pG ) constructed from the same method have been analyzed in detail for 167 geomagnetic storms with the minimum D st index less than or equal to −50 nT. The time delay between J sW and J pG is clearly observable and varies for different events, according to which J sW is shifted backward with respect to J pG . We obtain a fairly good negative correlation between the shifted J sW and the J pG indices for the majority of events, and the significance level for 88% of the events (i.e., 147 events) does not exceed 0.05. A statistical analysis of the shifted J sW and the J pG indices for 147 selected events reveals that larger values of J sW and smaller magnitudes of J pG are commonly accompanied by enhanced southward magnetic fields, which implies that more solar wind energy is entering the magnetosphere and thus causing strong geomagnetic storms. Furthermore, a linear fit of the two indices suggests that the evolution of J pG can be predicted about 2 hr in advance based on J sW , indicating that J sW can provide early warnings of possible disturbances in the geomagnetic fields, which is crucial for space weather monitoring and operational forecasting.

Revisiting Jupiter's deuterium fraction in the rotational ground-state line of HD at high spectral resolution

The cosmic deuterium fraction, set by primordial nucleosynthesis and diminished by subsequent astration, is a valuable diagnostic tool to link the protosolar nebula to the history of star formation. However, in the present-day Solar System, the deuterium fraction in various carriers varies by more than an order of magnitude and reflects environmental conditions rather than the protosolar value. The latter is believed to be preserved in the atmospheres of the gas giant planets, yet determinations inferred from the CH$_3$D/CH$_4$ pair require a larger fractionation correction than those from HD/H$_2$, which are close to unity. The question of whether a stratospheric emission feature contaminates the absorption profile forming in subjacent layers was never addressed, owing to the lack of spectral resolving power. Here we report on the determination of the Jovian deuterium fraction using the rotational ground-state line of HD ($J=1-0$) at $ Employing the GREAT heterodyne spectrometer on board SOFIA, we detected the HD absorption and, thanks to the high resolving power, a weak stratospheric emission feature underneath; the former is blue-shifted with respect to the latter. The displacement is attributed to a pressure-induced line shift and reproduced by dedicated radiative-transfer modeling based on recent line-profile parameters. Using atmospheric standard models, we obtained $ D/H $, which agrees with a recent measurement in Saturn's atmosphere and with the value inferred from solar-wind measurements and meteoritic data. The result suggests that all three measurements represent bona fide protosolar D/H fractions. As a supplement and test for the consistency of the layering assumed in our model, we provide an analysis of the purely rotational $J=6-5$ line of CH$_4$ (in the vibrational ground state, at $ mu$m).

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.

SWAN: A multihead autoregressive attention model for solar wind speed forecasting

High speed streams of solar wind can impact Earth’s magnetic environment. Current state of the art geomagnetic effect forecasting methods can predict perturbations up to two hours in advance by using solar wind measurements made in the L1 Lagrange point. By forecasting these solar wind parameters, it should be possible to extend the time range for which said methods can be used.We present Solar Wind Attention Network (SWAN), an attention-based autoregressive model for the forecasting of daily average bulk speed values. We apply a novel automatic design method to create its architecture by developing a pipeline of hyperparameter optimization techniques as a proof of concept. Usage of this pipeline leads to a robust, reliable architecture which we empirically verify the convergence of in a variety of problem configurations regarding solar wind speed forecasting.An improvement in terms of root mean squared error (RMSE) is found when compared to WindNet and convolutional neural networks (CNNs) expanding it, even when SWAN does not incorporate images of the solar disk into the input. SWAN’s RMSE of 68.79 ± 1.05 km s−1 with a lead time of three days compares to RMSE values in the order of 80.28 ± 3.05, 76.30 ± 1.87 and 71.93 ± 0.40 km s−1. From these results, we discuss the possibility of incorporating both autoregressive and image information as input to enhance predictive capability of newly developed models. Furthermore, we analyze the resulting model’s behavior to characterize its main error sources, and find its performance to be at its best in scenarios not involving newly formed coronal holes or coronal mass ejections. It is found that this behavior matches well with a priori expectations derived from the underlying physics. Finally, we briefly discuss the need for an in-depth study of different evaluation metrics to provide a more thorough understanding of modeling results on the field.

Evolution of coronal mass ejections with and without sheaths from the inner to the outer heliosphere: Statistical investigation for 1975 to 2022

Aims. This study covers a thorough statistical investigation of the evolution of interplanetary coronal mass ejections (ICMEs) with and without sheaths through a broad heliocentric distance and temporal range. The analysis treats the sheath and magnetic obstacle (MO) separately in order to gain more insight on their physical properties. In detail, we aim to unravel different characteristics of these structures occurring over the inner and outer heliosphere. Methods. The method is based on a large statistical sample of ICMEs probed over different distances in the heliosphere. For this, information about detection times for the sheath and MO from 13 individual ICME catalogs was collected and crosschecked. The time information was then combined into a main catalog that was used as the basis for the statistical investigation. The data analysis based on this catalog covers a large number of spacecraft missions, enabling in situ solar wind measurements from 1975 to 2022. This allowed us to study the differences between solar cycles. Results. All the structures under study (sheath, MO with and without sheath) show the biggest increase in size together with the largest decrease in density at a distance of ∼0.75 AU. At 1 AU, we found different sizes for MOs with and without a sheath, with the former being larger. Up to 1 AU, the upstream solar wind shows the strongest pileup close to the interface with the sheath. For larger distances, the pileup region seems to shift, and it recedes from that interface further into the upstream solar wind. This might refer to a change in the sheath formation mechanism (driven versus non-driven) with heliocentric distance, suggesting the relevance of the CME propagation and the expansion behavior in the outer heliosphere. A comparison to previous studies showed inconsistencies over the solar cycle, which makes more detailed studies necessary in order to fully understand the evolution of ICME structures.

Identifying Coronal Sources of L1 Solar Wind Disturbances Using the Fisk Heliospheric Magnetic Field and Potential Field Extrapolations during Three Solar Minima

The solar minima between solar cycles 22–23, 23–24, and 24–25 are the best observed minima on record. In situ solar wind and interplanetary magnetic field measurements by the Wind and ACE spacecraft at L1 with 1 hr cadence are explored using wavelet analyses for the most quiescent year during each minimum. Times of local peaks in periodicities are identified in the solar wind velocity, magnetic field components, and proton number densities. The measured radial velocities at these times are used to trace magnetic field lines to the photosphere using two models. The first is the Fisk heliospheric magnetic field that traces field lines from L1 to the photosphere. They connect exclusively to solar poles and in 88% of instances to locations of polar coronal holes (PCHs). The second model uses the Parker spiral to trace from L1 to the solar source surface and potential-field extrapolations from the source surface to the photosphere. These field lines terminate at equatorial and midlatitude coordinates, of which some are located close to coronal holes (CHs). This study connects for the first time CH signatures in the ecliptic plane at L1 with PCHs using the Fisk field. It shows how sources from both the solar equator and poles influence the solar wind at L1 and how the two models complement each other to identify these sources.

Sliding-window cross-correlation and mutual information methods in the analysis of solar wind measurements

Context. When describing the relationships between two data sets, four crucial aspects must be considered, namely: timescales, intrinsic lags, linear relationships, and non-linear relationships. We present a tool that combines these four aspects and visualizes the underlying structure where two data sets are highly related. The basic mathematical methods used here are cross-correlation and mutual information (MI) analyses. As an example, we applied these methods to a set of two-month’s worth of solar wind density and total magnetic field strength data. Aims. Two neighboring solar wind parcels may have undergone different heating and acceleration processes and may even originate from different source regions. However, they may share very similar properties, which would effectively “hide” their different origins. When this hidden information is mixed with noise, describing the relationships between two solar wind parameters becomes challenging. Time lag effects and non-linear relationships between solar wind parameters are often overlooked, while simple time-lag-free linear relationships are sometimes insufficient to describe the complex processes in space physics. Thus, we propose this tool to analyze the monotonic (or linear) and non-monotonic (or non-linear) relationships between a pair of solar wind parameters within a certain time period, taking into consideration the effects of different timescales and possible time lags. Methods. Our tool consists of two parts: the sliding-window cross-correlation (SWCC) method and sliding-window mutual information (SWMI) method. As their names suggest, both parts involve a set of sliding windows. By independently sliding these windows along the time axis of the two time series, this technique can assess the correlation coefficient (and mutual information) between any two windowed data sets with any time lags. Visualizing the obtained results enables us to identify structures where two time series are highly correlated, while providing information on the relevant timescales and time lags. Results. We applied our proposed tool to solar wind density and total magnetic field strength data. Structures with distinct timescales were identified. Our tool also detected the presence of short-term anti-correlations coexisting with long-term positive correlations between solar wind density and magnetic field strength. Some non-monotonic relationships were also found. Conclusions. The visual products of our tool (the SWCC+SWMI maps) represent an innovative extension of traditional numerical methods, offering users a more intuitive perspective on the data. The SWCC and SWMI methods can be used to identify time periods where one parameter has a strong influence on the other. Of course, they can also be applied to other data, such as multi-wavelength photometric and spectroscopic time series, thus providing a new tool for solar physics analyses.

Accuracy of Global Geospace Simulations: Influence of Solar Wind Monitor Location and Solar Wind Driving

AbstractSome space weather models, such as the Space Weather Modeling Framework (SWMF) used in this study, use solar wind propagated from the first Lagrange point (L1) to the bow shock nose (BSN) to forecast geomagnetic storms. The SWMF is a highly coupled framework of space weather models that include multiple facets of the Geospace environment, such as the magnetosphere and ionosphere. The propagated solar wind measurements are used as a boundary condition for SWMF. The solar wind propagation method is a timeshift based on the calculated phase front normal (PFN) which leads to some uncertainties. For example, the propagated solar wind could have evolved during this timeshift. We use a data set of 123 geomagnetic storms between 2010 and 2019 run by the SWMF Geospace configuration to analyze the impact solar wind propagation and solar wind driving has on the geomagnetic indices. We look at the probability distributions of errors in SYM‐H, cross polar cap potential (CPCP), and auroral electrojet indices AL and AU. Through studying the median errors (MdE), standard deviations and standardized regression coefficients, we find that the errors depend on the propagation parameters. Among the results, we show that the accuracy of the simulated SYM‐H depends on the spacecraft distance from the Sun‐Earth line. We also quantify the dependence of the standard deviation in SYM‐H errors on the PFN and solar wind pressure. These statistics provide an insight into how the propagation method affects the final product of the simulation, which are the geomagnetic indices.

Large TEC Variations Between Mars and Earth: Simulation and Observation Comparisons

AbstractLarge total electron content (TEC) variations along the line from Mars to Earth have been demonstrated by analyzing the ground received Tianwen‐1 differential one‐way range (DOR) signals when corona mass ejections (CMEs) and co‐rotating interaction regions (CIRs) pass through the signal path. Here, the TEC variations along the line from Mars to Earth are calculated from the Wang‐Sheeley‐Arge (WSA)‐Enlil space weather forecast model. The results are compared with the Tianwen‐1 line of sight (LOS) observations as well as the STEREO‐A in situ solar wind density measurements. It is found that the TEC variations calculated from the WSA‐Enlil model are usually much smaller in amplitude, especially in the cases of CIRs passing through the signal path. In addition, the timings of CMEs and CIRs passing through the DOR signal path in the WSA‐Enlil model can be half a day earlier or later than the observed ones. In comparison with in situ solar wind measurements, the ground received Tianwen‐1 DOR signals carry solar wind density information over a large spatial region along the signal path. Such remote sensing measurement can be valuable for constraining and improving global solar wind forecast models.

The Impact of Non‐Equilibrium Plasma Distributions on Solar Wind Measurements by Vigil's Plasma Analyser

AbstractIn order to protect society from space weather impacts, we must monitor space weather and obtain early warnings for extreme events if possible. For this purpose, the European Space Agency is currently preparing to launch the Vigil mission toward the end of this decade as a space‐weather monitor at the fifth Lagrange point of the Sun–Earth system. Vigil will carry, amongst other instruments, the Plasma Analyser (PLA) to provide quasi‐continuous measurements of solar wind ions. We model the performance of the PLA instrument, considering typical solar wind plasma conditions, to compare the expected observations of PLA with the assumed input conditions of the solar wind. We evaluate the instrument performance under realistic, non‐equilibrium plasma conditions, accounting for temperature anisotropies, proton beams, and the contributions from α‐particles. We examine the accuracy of the instrument's performance over a range of input solar wind moments. We identify sources of potential errors due to non‐equilibrium plasma conditions and link these to instrument characteristics such as its angular and energy resolution and its field of view. We demonstrate the limitations of the instrument and potential improvements such as applying ground‐based fitting techniques to obtain more accurate measurements of the solar wind even under non‐equilibrium plasma conditions. The use of ground processing of plasma moments instead of on‐board processing is crucial for the extraction of reliable measurements.

Revisiting the Solar Wind Deceleration Upstream of the Martian Bow Shock Based on MAVEN Observations

The solar wind deceleration upstream of the Martian bow shock is examined using particle and magnetic field measurements obtained by the Mars Atmosphere and Volatile EvolutioN (MAVEN). Mars lacks a strong intrinsic magnetic field so its upper atmosphere extends beyond the Martian bow shock and interacts directly with the solar wind. Neutral atoms in the Martian upper atmosphere can be ionized through several physical processes and then start to move with the solar wind flow to form pickup ions. In return, the solar wind is expected to slow down due to the momentum transfer to the pickup ions. The present study surveys the MAVEN solar wind measurements between 2015 and 2019 to evaluate the solar wind deceleration upstream of the Martian bow shock. Different from previous studies of solar wind deceleration, our analysis carefully excludes the solar wind deceleration in the shock foot region. The average solar wind deceleration obtained is about 0.7% of the initial solar wind speed, much smaller than the values given by previous studies. Further calculation using several reasonable Martian upper atmosphere density profiles demonstrates that the deceleration observed is consistent with the pickup ion mass-loading scenario.

What is the Reynolds Number of the Solar Wind?

The Reynolds number, Re, is an important quantity for describing a turbulent flow. It tells us about the bandwidth over which energy can cascade from large scales to smaller ones, prior to the onset of dissipation. However, calculating it for nearly collisionless plasmas like the solar wind is challenging. Previous studies have used formulations of an “effective” Reynolds number, expressing Re as a function of the correlation scale and either the Taylor scale or a proxy for the dissipation scale. We find that the Taylor scale definition of the Reynolds number has a sizable prefactor of approximately 27, which has not been employed in previous works. Drawing from 18 years of data from the Wind spacecraft at 1 au, we calculate the magnetic Taylor scale directly and use both the ion inertial length and the magnetic spectrum break scale as approximations for the dissipation scale, yielding three distinct Re estimates for each 12 hr interval. Average values of Re range between 116,000 and 3,406,000 within the general distribution of past work. We also find considerable disagreement between the methods, with linear associations of between 0.38 and 0.72. Although the Taylor scale method is arguably more physically motivated, due to its dependence on the energy cascade rate, more theoretical work is needed in order to identify the most appropriate way of calculating effective Reynolds numbers for kinetic plasmas. As a summary of our observational analysis, we make available a data product of 28 years of 1 au solar wind and magnetospheric plasma measurements from Wind.

Solar wind parameters in rising phase of solar cycle 25: Similarities and differences with solar cycles 23 and 24

Solar activity and solar wind parameters decreased significantly in solar cycles (SCs) 23–24. In this paper, we analyze solar wind measurements at the rising phase of SC 25 and compare them with similar data from the previous cycles. For this purpose, we simultaneously selected the OMNI database data for 1976–2022, both by phases of the 11-year solar cycle and by large-scale solar wind types (in accordance with catalog [http://www.iki.rssi.ru/pub/omni]), and calculated the mean values of the plasma and magnetic field parameters for the selected datasets. The obtained results support the hypothesis that the continuation of this cycle will be similar to that of cycle 24, i.e. SC 25 will be weaker than SCs 21 and 22

Параметры солнечного ветра на фазе роста 25-го солнечного цикла: сходства и различия с 23-м и 24-м солнечными циклами

Solar activity and solar wind parameters decreased significantly in solar cycles (SCs) 23–24. In this paper, we analyze solar wind measurements at the rising phase of SC 25 and compare them with similar data from the previous cycles. For this purpose, we simultaneously selected the OMNI database data for 1976–2022, both by phases of the 11-year solar cycle and by large-scale solar wind types (in accordance with catalog [http://www.iki.rssi.ru/pub/omni]), and calculated the mean values of the plasma and magnetic field parameters for the selected datasets. The obtained results support the hypothesis that the continuation of this cycle will be similar to that of cycle 24, i.e. SC 25 will be weaker than SCs 21 and 22