Abstract This study presents a comprehensive statistical comparison of solar wind measurements between the OMNI database which contains data collected at L1 that is shifted to the bow shock nose, and near‐Earth observations from MMS, Cluster, and THEMIS missions near the bow shock nose. Using a threshold‐based classification methodology, the analysis encompasses approximately 353 days (MMS), 283 days (Cluster), and 125 days (THEMIS) of solar wind intervals. Bisector regression analysis reveals that the anti‐sunward flow component demonstrates exceptional agreement across all missions with near‐unity slopes and correlation coefficients of 0.92 for THEMIS and 0.97 for MMS and Cluster. However, perpendicular velocity components show progressively degraded performance: exhibits correlation coefficients of 0.63–0.77 with intercepts ranging from 21.57 km/s (MMS) to 47.49 km/s (THEMIS), while shows weaker correlations (0.42–0.72) with intercepts of 4.73–11.94 km/s. Ion density measurements reveal systematic mission‐specific biases: MMS and THEMIS show ion density regression slopes below unity (0.59 and 0.54, respectively), while Cluster shows a slope above unity (1.14) compared to OMNI measurements. Magnetic field measurements show higher consistency, with near‐unity slopes and correlation coefficients exceeding 0.84 for most components. The northward magnetic field component exhibits elevated variance ratios and reduced correlations across all missions, reaching 0.74 for THEMIS. These results quantify inherent uncertainties in cross‐platform solar wind comparisons and assess the accuracy of time‐shifted solar wind measurements in the OMNI database as proxies for actual near‐Earth conditions, with implications for space weather applications, multispacecraft studies, and magnetohydrodynamic simulation validation requiring accurate upstream boundary conditions.

Abstract Recent missions, including the Parker Solar Probe, Solar Orbiter, and the Mars Atmosphere and Volatile EvolutioN (MAVEN), have probed a wide range of heliocentric distances, enabling new investigations of solar wind evolution. By comparing solar wind measurements from spacecraft located at different positions in the heliosphere (0.1–1.5 au), we study the persistence of solar wind velocity in longitude, latitude, and over radial distance. To this end, we introduce a new persistence model based on in situ observations being propagated across longitudes and radial distance, conserving latitude. The resulting time-dependent two-dimensional maps of projected solar wind parameters are then compared with in situ measurements from the OMNI database at 1 au and from MAVEN orbiting Mars. We find that the mean absolute error (MAE) of the solar wind velocity increases by 2.4 km s −1 for each degree of latitudinal spacecraft separation over a range of up to 14 . ° 3. In addition, we identify a logarithmic relationship between persistence time and MAE, revealing spatial and temporal solar wind variability in the heliosphere. Over radial separation, we find no clear relationship based on current data coverage.

Solar wind measurements by the Mars Atmosphere and Volatile EvolutioN (MAVEN) mission provide samples of the heliosphere at 1.38-1.67 AU, and of the upstream conditions that drive numerous processes in the near-Mars plasma environment. We reduce ion measurements from MAVEN's Solar Wind Ion Analyzer (SWIA), using contextual magnetic field measurements, to 13 independent macroscopic plasma parameters by fitting a convolution of SWIA's 3-dimensional response function and a superposition of phase-space bi-kappa distribution functions to each measured distribution using an iterative Poisson optimization scheme. This ensemble of parameters represents the solar wind H+ core, H+ beam, and He2+ (alpha) populations, effectively separating each population's contribution to any measured distribution. Sporadic plasma frequency measurements from MAVEN's Langmuir Probe and Waves (LPW) instrument are used to calibrate the SWIA measurements such that ion charge densities match LPW-derived electron charge densities. The resulting dataset is effectively ground-truthed, largely corrected for instrumental particularities, and provides a rich timeline of solar wind properties at Mars, including composition, velocities, temperature anisotropies, differential drifts, and degree of thermalization.

Context. Solar wind propagation behavior has significant implications for solar wind forecasting and measurements. Variability in coherence and plasma turbulence under different plasma conditions is important for cross-satellite comparisons. Forecasting also depends on whether upstream measurements remain valid at the magnetosphere. Aims. We used computational methods to analyze magnetic coherence and connections to plasma parameters, utilizing multi-decade ACE and Wind measurements to capture turbulence behavior across a wide range of spatial separations and solar cycle phases. Methods. The measurements were separated into three frequency ranges within the inertial range of solar wind turbulence: in periods of 1–2.5 min, 2.5–10 min, and 10–30 min. We assessed the coherence in each frequency band using time-lagged cross-correlations and applied a clustering algorithm to identify connections between coherence and plasma parameters (velocity, proton density, flow pressure). We performed this analysis in the radial, nonradial, and total directions. Results. We used a k -means clustering algorithm to find that higher coherence in all cases is associated with smaller variance in plasma parameters. Taking this into consideration, we find a trivial association with the satellite separation or solar cycle phase. Small variations in dynamic pressure and velocity appear to be the best indicators of high coherence at these high-frequency inertial scales. Identifying connections between turbulence and plasma parameters could improve our understanding of the underlying physical processes. This information will also be vital for instrument calibration on future missions such as the Space Weather Follow On L1 (SWFO-L1).

Abstract Using solar wind measurements from Solar Orbiter, we investigated the distribution and evolution of the temperature anisotropy of alpha-particles (the ratio of perpendicular to parallel temperatures) in the inner heliosphere, between 0.29 and approximately 1 au. We also compared these observations with the corresponding proton temperature anisotropy to better understand the evolution of ion thermal properties in the expanding solar wind. We found that the slow wind (≤400 km s −1 ) exhibits higher alpha-particle anisotropy compared to the fast wind (≥600 km s −1 ), where the anisotropy values are mostly below one. The alpha-particle anisotropy increases as solar wind speed decreases, in contrast to protons, where anisotropy increases with solar wind speed. Additionally, we find that, regardless of the distance from the Sun, alpha-particle anisotropy is greater than proton anisotropy when the normalized differential speed between alpha-particles and protons (Δ V / V A ) is less than 0.3. However, when Δ V / V A exceeds 0.4, the trend reverses, and proton anisotropy becomes larger. The radial evolution also shows distinct differences between fast and slow wind. In the fast wind, proton anisotropy decreases with distance from 0.29 to 1 au, while alpha-particle anisotropy shows no clear radial trend and remains nearly constant. In the slow wind, both proton and alpha anisotropies decrease with distance from the Sun. These results reveal distinct behaviors of alpha-particles and protons in the expanding solar wind, highlighting the dependence of ion heating on solar wind speed and differential flow.

In this study, we investigate the cold ions (<70 eV) originated in the high-latitude ionosphere of the Earth entering the magnetosphere towards the magnetotail. We analyze measurements from Cluster spacecraft along with solar irradiance, solar wind (SW), and geomagnetic observations. Two machine learning models driven by solar irradiance and solar wind measurements are derived to predict the cold ion flux. With the linear baseline model, we provide an empirical formula. The nonlinear model (Extra-Trees Regressor) yields 17% better performance. The total cold ion escape rate from the polar cap ranges between ∼1.1⋅1024 and ∼2.7⋅1026s−1. The upper limit is comparable to the neutral escape rate. The results show that spatial location is the most important predictor. Solar EUV irradiance is also among the top predictors, followed by the solar wind electric field, the interplanetary magnetic field (IMF), and solar wind dynamic pressure. These results can help to evaluate the influence of the stellar wind-magnetospheric interaction on the ion outflow at Earth-like exoplanets. They indicate the importance of such an interaction for the atmospheric escape during active geomagnetic conditions. Stronger outflow from the Northern Hemisphere than from the Southern Hemisphere hints that the magnetic field strength can impact the amount of ionospheric outflow.

The Deep Space Climate Observatory (DSCOVR), launched in 2015, is the first Earth-observing mission to a Sun-Earth first Lagrange point (L1) orbit, about 1.5 million km from Earth on the Sun-Earth line. The goal of the mission is to provide continuous solar wind measurements for accurate space weather forecasting and observe the sunlit side of the Earth for enhancing climate science. The Earth Polychromatic Imaging Camera (EPIC) is one of the two Earth-observing instruments on DSCOVR. It takes images of nearly the entire sunlit side of the Earth in 10 spectral channels at a relatively high temporal resolution to monitor the changing planet. EPIC’s view contains polar regions that are barely visible from geostationary satellite (GEOs), providing observations of the global reflected spectral radiation. Among other capabilities of EPIC, such as observing atmospheric and surface properties, the well calibrated reflected global spectral radiation observed by EPIC and EPIC-based broadband shortwave (SW) radiance and flux can be used to monitor the changing planet of the Earth. However, to assess the long-term change of the Earth in terms of its spectral brightness and reflected SW radiation, the natural variability of global spectral reflectance and SW radiation must be quantitatively determined. This work provides quantitative estimates of the variability of global spectral reflectance and SW radiance and flux on different time scales. The main finds of this work are: (1) the hourly variability of global average reflectance in red and NIR bands is much larger than the variation in UV and blue bands, and the 24-h variability in boreal summer is significantly larger than in winter; (2) the presence of Antarctica and the Arctic is primarily responsible for seasonal variation in spectral reflectance and SW radiance and flux; (3) the global average SW radiance is highly anisotropic, particularly over land, and assumption of Lambertian reflection will overestimate the SW flux by 20%–30%. Furthermore, the responsible physical mechanisms are provided.

Abstract The 1/f noise is a ubiquitous phenomenon in natural systems. Since the advent of space exploration, the 1/f range has been consistently observed in in situ solar wind measurements throughout the heliosphere, sparking decades of debate regarding its origin. Recent Parker Solar Probe observations near the Alfvén surface have revealed a systematic absence of the 1/f range above 10−4 Hz in pristine solar wind, providing a unique opportunity to investigate its origin in solar wind turbulence. Despite numerous observations of the 1/f range at varying frequencies, no study has systematically examined its properties across different solar wind conditions. Here, we identify two distinct types of 1/f ranges in solar wind turbulence: the fast/Alfvénic wind type and the slow/mixed wind type. The fast/Alfvénic type appears to be an intrinsic feature of Alfvénic turbulence, while the slow/mixed type resembles classical flicker noise. For the fast/Alfvénic type, we find a near-perfect WKB evolution of the frequency-averaged fluctuation amplitude and an intriguing migration pattern in frequency space. For the slow/mixed type, we examine the solar cycle dependence of the 1/f noise using the OMNI (LRO) data set spanning solar cycles 22–25. We also analyze the autocorrelation function of the magnetic field vectors and identify a clear relationship between the 1/f range and the decline in correlation, as well as unexpected resonance peaks in the autocorrelation function.

Abstract We revisit an existing but unexplored finding on the calculation of the baseline (i.e., potential) magnetic energy in observed solar magnetic configurations and apply it to two series of high-cadence, cospatial, and cotemporal line-of-sight photospheric magnetograms with a factor of ∼4 difference in spatial resolution. The target is a small coronal hole, ∼80″ across. We find significant differences between the two data sets, with approximate factors of 2.4 in the unsigned magnetic flux, 2.1 in the potential magnetic energy, and 5.2 in the mean amplitudes of the energy variation, all in favor of the higher-resolution magnetograms. Additionally, we find a factor of 2.5 difference in the characteristic magnetic flux replenishment time, with configurations at higher resolution renewing their flux every 46 minutes on average. Energy decreases associated with apparent magnetic flux cancellation events in higher resolution yield power densities above 106 erg cm−2 s−1, seemingly sufficient to sustain coronal holes and drive the fast solar wind. For the first time, this represents apparent energy released at photospheric altitudes rather than energy deposited via the Poynting flux. Lower-resolution magnetograms give 5.4 times less power density output. These intriguing results could have wide-ranging implications for in situ solar wind measurements and their solar sources in the Parker Solar Probe mission, as well as for high-resolution observations featuring simultaneous photospheric and chromospheric magnetograms including, but not limited to, data from the Daniel K. Inouye Solar Telescope.

Abstract The electric field pulses caused by enhanced solar wind dynamic pressure are particularly effective in energization and inward transport of relativistic electrons in Earth's radiation belt. Utilizing electric field and particle measurements by Van Allen Probes and near‐Earth solar wind measurements, we have conducted a statistical analysis to investigate the responses of electric field pulses and particle dynamics to enhanced solar wind dynamic pressure under varied interplanetary magnetic field (IMF) directions. On Earth's dayside, the generation of electric field pulses is independent of the IMF direction. In contrast, on the nightside, electric field pulses are more easily excited under northward IMF than under southward IMF. Interacted with the electric field pulses, the responses of electrons and protons show a day‐night asymmetry under southward IMF. The statistical results also indicate that proton flux variations mostly cluster on the post‐dawnside (MLT ∼ 6–12), while electron flux variations show a slight preference for the pre‐duskside (MLT ∼ 12–18) under some conditions. Moreover, the occurrence rate of proton flux variation is lower than that of electrons, and a clear change in proton flux usually appears in lower energy channels. Our study provides new insights into understanding the interaction between solar winds and magnetospheres of the Earth and other magnetized planets.

Abstract Accurately predicting the horizontal component of ground magnetic field perturbation (d), a key quantity for calculating the geomagnetically induced currents (GICs), is crucial for assessing the space weather impact of geomagnetic disturbances. The current operational first‐principles Michigan Geospace model provides effective forecasts of d, but requires significant computational resources to achieve real‐time speeds. Existing data‐driven methods tend to underpredict d and lack uncertainty quantification, which is either overlooked or treated as secondary. In this work, we introduce GeoDGP, a novel and efficient data‐driven model based on the deep Gaussian process. GeoDGP provides global probabilistic forecasts of d with a lead time of at least 1 hr, at 1‐min time cadence, and at arbitrary spatial locations. The model takes solar wind measurements, the Dst index, and the prediction location in solar magnetic coordinate system as inputs, and is trained on 28 years of data from SuperMAG global magnetometer stations. Additionally, GeoDGP is also trained to predict the north (d) and east (d) components of perturbations. We evaluate GeoDGP's performance at over 200 stations worldwide during 24 geomagnetic storms, including the Gannon extreme storm of May 2024. Comparisons with the first‐principles Michigan Geospace model and the data‐driven DAGGER model revealed that GeoDGP significantly outperforms both across multiple performance metrics.

In this study, we present a method for comparing hybrid plasma simulations with spacecraft measurement data, with a focus on Mercury’s interaction with the solar wind. Utilizing the 3D global hybrid simulation model Adaptive Ion-Kinetic Electron-Fluid (AIKEF), we derived ion energy distributions in Mercury’s magnetosheath using different solar wind input scenarios. We introduce three concepts for counting the simulated particles and assess the systematic and stochastic variations. Furthermore, we evaluate the effect of different phase space resolutions within the model. We find that a lower phase space resolution influences the distribution peaks, thus emphasizing the need for adequate particle resolution. Dedicated simulation runs for BepiColombo’s second and third flyby maneuvers at Mercury, based on solar wind measurement data from the BepiColombo fluxgate magnetometer on board the Mio spacecraft (Mio-MGF) and Planetary Ion Camera (PICAM) instruments, enable quantitative comparison of simulated data with ion spectrometer measurements. The energy flux distributions measured by PICAM fall within the confidence intervals of the modeled data. Additionally, we find that systematic deviations dominate the stochastic deviations. Our analysis shows that our simulation is an effective tool for understanding magnetospheric conditions, and it can assist in interpreting the in situ spacecraft measurements from BepiColombo upon its arrival.

Abstract Spurious solar-wind effects are a potential noise source in future Laser Interferometer Space Antenna (LISA) measurements. One noise coupling mechanism is constrained by estimating solar-wind effects on acceleration noise in LISA Pathfinder (LPF). While LISA is designed for drag-free differential measurement, predicting the realistic impact both bounds the operational environment and assesses whether LISA could provide serendipitous space-weather observations. Data from NASA’s Advanced Composition Explorer (ACE), situated at the L1 Lagrange point, serves as a reliable source of solar-wind data. The data sets are compared over the 114 d time period from 1 March 2016 to 23 June 2016. This period gives the longest readily-available open data set, without interference from other commissioning activities. To evaluate space weather effects, the data from both satellites are formatted, gap-filled/interpolated, and fast-Fourier transformed for amplitude spectral density and coherence comparisons. Solar wind effects are not seen in a coherence plot between LPF and ACE; modest coherence in the planned LISA observational frequency band can be attributed to chance. This result indicates that measurable correlation due to solar-wind acceleration noise over 3 month timescales will be a negligible noise source. LISA is unlikely to inform solar wind measurements routinely. Another source of noise from the Sun, solar radiation pressure, is estimated to impart greater acceleration noise, but has yet to be analyzed.

Abstract The WISPR imager on Parker Solar Probe (PSP) provides a unique view of the young solar wind as it flies through solar wind structures at high speed. It is of interest to use WISPR image sequences to measure the velocity of both large features (such as coronal mass ejections) and the background, ambient wind. However, WISPR’s close-up, rapidly moving perspective makes the usual methods for measuring velocities from images difficult or impossible to apply, as most apparent motion through the image is due to the motion or rotation of the imager. In this work, we propose a new method of looking for features at the “stationary point”—a direction from which some plasma parcels appear to approach the spacecraft, remaining at a constant direction in the image sequence. This direction is a function of the plasma’s radial velocity, the encounter geometry, and the spacecraft velocity, allowing the former two to be inferred. We demonstrate the technique with forward-modeled images, and we apply it to WISPR observations, inferring the speed and trajectory of a particular density feature. This method promises to enable speed measurements of the young solar wind in an important acceleration region, from a close-up perspective and at latitudes well outside the PSP orbital plane. And while we present this method in a solar wind context, it is broadly applicable to any situation of a moving viewpoint traveling through an expanding cloud of features.

Abstract Over the past decades, missions at the L1 point have been providing solar wind and interplanetary magnetic field measurements that are necessary for forecasting space weather at Earth with high accuracy and a lead time of a few tens of minutes. Improving the lead time, while maintaining a relatively high level of accuracy, can be achieved with missions sunward of L1, so‐called sub‐L1 monitors. However, too much is unknown to plan for sub‐L1 monitors as operational missions: both the orbital requirements of such missions, and the achievable accuracy of forecasts based on their measurements have not been quantitatively defined. We review here some proposed mission concepts and explain the knowledge gaps related to coronal mass ejections (CMEs) that require a space weather research or science mission. We first show how STEREO‐A measurements in 2023 can be used as a proof of concept of the use of sub‐L1 monitor slightly off the Sun‐Earth line to forecast the Dst index. We then highlight that separations of are needed to ensure that CMEs measured by a sub‐L1 monitor impact Earth. Next, we show that measurements with angular separations of have negligible errors but separations of a few degrees can result in significant errors in lead time and in the forecasted magnetic field strength of CMEs. We also discuss how CME evolution over the last 0.05–0.2 au before impacting Earth is strongly under‐constrained and needs to be better understood before using measurements of sub‐L1 monitors for real‐time space weather forecasting.

<sec>This study aims to quantify the solar wind charge-exchange (SWCX) X-ray emission factor (denoted as <i>α</i>-value) and its dependence on solar wind parameters, solar activity cycle, and solar wind origin. By analyzing 13-year (1998–2011) <i>in-situ</i> measurements from the advanced composition explorer (ACE) spacecraft, we investigate the statistical correlations between solar wind ionization states, elemental abundances (particularly oxygen), and bulk plasma parameters (proton speed, density). The derived <i>α</i>-values are critical for explaining the data from solar wind and magnetosphere interaction linker explorer (SMILE), and disentangling SWCX foreground emissions from diffuse astrophysical X-ray sources observed by Einstein Probe (EP) and proposed diffuse X-ray explorer (DIXE) payload on Chinese space station. In this work, high-resolution solar wind ion composition data and plasma parameters from ACE are investigated. Events are categorized by solar wind origin (coronal holes, streamers, interplanetary coronal mass ejections (ICMEs)) and solar cycle phase (minimum <i>vs.</i> maximum). The α-value, defined as the total soft X-ray photon emission cross section per solar wind proton, is computed using an updated charge-exchange model that combines the state-resolved cross-section for highly charged ions. The model takes into consideration the velocity-dependent cross-section of solar wind-neutral interaction. Statistical method and bin-averaging techniques are adopted to extract the relations between α, solar wind speed (<i>v</i><sub>sw</sub>), proton density (<i>n</i><sub>p</sub>), and oxygen abundance. The main results are as follows.</sec><sec>1) Ionization state dynamics: A strong anti-correlation exists between solar wind ionization degree and bulk speed: high-speed winds (> 500 km/s) exhibit lower ionization states than slow-speed winds (< 400 km/s).</sec><sec>2) Elemental abundance trends: Oxygen abundance ([O/H]) is inversely correlated with <i>n</i><sub>p</sub>: the [O/H] of denser solar wind plasmas (<i>n</i><sub>p</sub> > 13 cm<sup>–3</sup>) decreases by 30%–50%, indicating the presence of fractionation during plasma acceleration. No significant speed dependence of [O/H] is observed, compared with earlier research results.</sec><sec>3) Emission factor (<i>α</i>-value) behavior: <i>α</i>-value decreases rapidly with the increase of <i>n</i><sub>p</sub> and stabilizes for <i>n</i><sub>p</sub> > 13 cm<sup>−3</sup>. Conversely, <i>α</i>-value increases gradually with <i>v</i><sub>sw</sub> up to 430 km/s, beyond which it plateaus. The ICME-associated <i>α</i> exceeds streamer and coronal hole values by 35%–60%, which is attributed to higher averaged ionic state in transient ejecta. Solar maximum <i>α</i> (2000–2002) is 1.3–2.7 times higher than solar minimum (2008–2010), reflecting cycle-dependent ion composition changes. </sec><sec>By bridging <i>in-situ</i> solar wind measurements and X-ray emission physics, this work enhances the ability to diagnose solar wind-magnetosphere coupling and diffuse X-ray background. The validated <i>α</i>-value will be of benefit to the data analysis for Chinese aerospace projects in the 2020s, such as SMILE, DIXE, and EP.</sec>

Global magnetohydrodynamic (MHD) models play an important role in the infrastructure of space weather forecasting. Validating such models commonly utilizes in situ solar wind measurements made near the Earth’s orbit. The purpose of this study is to test the performance of G3DMHD (a data driven, time-dependent, 3D MHD model of the solar wind) with Parker Solar Probe (PSP) measurements. Since its launch in 2018 August, PSP has traversed the inner heliosphere at different radial distances sunward of the Earth (the closest approach ∼13.3 R ⊙), thus providing a good opportunity to study evolution of the solar wind and to validate heliospheric models of the solar wind. The G3DMHD model simulation is driven by a sequence of maps of the photospheric field extrapolated to the assumed source surface (2.5 R ⊙) using the potential field model from 2018 to 2022, which covers the first 15 PSP orbits. The Pearson correlation coefficient (cc) and the mean absolute scaled error (MASE) are used as the metrics to evaluate the model performance. It is found that the model performs better for both magnetic intensity (cc = 0.75; MASE = 0.60) and the solar wind density (cc = 0.73; MASE = 0.50) than for the solar wind speed (cc = 0.15; MASE = 1.29) and temperature (cc = 0.28; MASE = 1.14). This is due primarily to lack of accurate boundary conditions. The well-known underestimate of the magnetic field in solar minimum years is also present. Assuming that the radial magnetic field becomes uniformly distributed with latitude at or below 18 R ⊙ (the inner boundary of the computation domain), the agreement in the magnetic intensity significantly improves (cc = 0.83; MASE = 0.49).

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.

Abstract Geomagnetically 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.

Abstract The 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.