In this work, we describe an approach to characterize the ground response to geomagnetic storm drivers and recalculate the scaling factors that are used in the North American Reliability Corporation (NERC) TPL-007 geomagnetic disturbance (GMD) standard based on magnetotelluric (MT) survey measurements. The new ground response scaling factors indicate a significantly wider range of ground responses from the one-dimensional model responses used in the original NERC GMD standards framework. Further, the region Mid-West area west of the great lakes and North-East around New England and areas along the Appalachian Mountains appear to be particularly exposed to the geoelectric field hazard under extreme space weather conditions. A more complete validation of MT data in geomagnetically induced currents applications is warranted before usage of the presented results as a basis for possible updates to the NERC GMD standard.

We examine the size of and outer surface boundary integrals in estimating the surface magnetic field from magnetohydrodynamic (MHD) simulations. Maxwell's equations tell us , which may be violated due to numerical error. MHD models such as the Space Weather Modeling Framework (SWMF) and the Open Geospace General Circulation Model (OpenGGCM) use different techniques to limit . Analyses of MHD simulations typically assume errors are small. Similarly, analyses commonly use the Biot-Savart Law and magnetospheric current density estimates from MHD simulations to determine the magnetic field at a specific point on Earth. This calculation frequently omits the surface integral over the outer boundary of the simulation volume that the Helmholtz decomposition theorem requires. This paper uses SWMF and OpenGGCM simulations to estimate the magnitudes of the and outer boundary integrals compared to Biot-Savart estimates of the magnetic field on Earth. In the simulations considered, the and outer surface integrals are up to 30% of Biot-Savart estimates when the Biot-Savart estimates are large. We conclude rather than using the Biot-Savart Law to estimate the magnetic field from the magnetosphere, it is better and computationally more efficient to use the integral over the inner boundary of the magnetosphere. The conclusions are the same for a simulation involving a simple change in the interplanetary magnetic field and a more complex superstorm simulation.

The 1859 Carrington event is the most intense geomagnetic storm in recorded history, and the literature provides numerous explanations for what drove the negative $H$ perturbation on the Earth. There is debate on what dominated the event. Our analysis shows a combination of causes of similar orders of magnitude. Previous analyses generally rely upon on the observed $H$ perturbation at Colaba, India; historic newspaper reports; and empirical models. We expand the analysis using two Space Weather Modeling Framework simulations to examine what drove the event. We compute contributions from currents and geospace regions to the northward $B$ field on Earth’s surface, $B_N$. We examine magnetospheric currents parallel and perpendicular to the local $B$ field, ionospheric currents, and gap region field–aligned currents (FACs). We also evaluate contributions from the magnetosheath, near–Earth, and neutral sheet regions. A combination of currents and geospace regions significantly contribute to $B_N$ on the Earth’s surface, changing as the storm evolves. At storm onset, magnetospheric currents and gap–region FACs dominate in the equatorial region. At auroral latitudes, gap–region FACs and ionospheric currents are the largest contributors. At storm peak, azimuthal magnetospheric currents and gap–region FACs dominate at equatorial latitudes. Gap–region FACs and ionospheric currents dominate in the auroral zone, down to mid-latitudes. Both the magnetosheath and FACs contribute at storm peak, but are less significant than that from the near–Earth ring current. During recovery, the near–Earth ring current is the largest contributor at equatorial latitudes. Ionospheric currents and gap–region FACs dominate in the auroral zone.

Space weather risk assessment is constrained by the lack of available asset information needed to model geomagnetically induced currents (GICs) in electricity transmission infrastructure. We propose a systematic method that enables risk analysts to collect their own open-source substation data. Using a web browser platform for annotation, we convert OpenStreetMap (OSM) substation locations into high-resolution, component-level mappings of electricity transmission assets. We convert an initial 1,313 high-voltage (>=230 kV) substations to 52,273 components using low-altitude, satellite, and Street View imagery accessed through Google Earth, identifying 7,949 transformers. Compared to the OSM baseline, this approach provides detailed insights on voltage levels and substation configurations. We then construct a geospatial GIC network for the Tennessee Valley Authority (TVA) region, comparing May 2024 results with the University of Illinois Urbana-Champaign 150-bus (UIUC150) synthetic network and with measured ground GICs at 13 monitoring devices. The transformer types at unannotated substations and the grounding resistances are unknown, so we sample both across a Monte Carlo ensemble. This gives a median TVA 95th-percentile peak ground GIC of 29.1 A, with a 90 percent confidence interval of 23.9-36.1 A. The UIUC150 network yields a 95th-percentile peak ground GIC of 35.8 A under the same forcing, falling within this interval, and the modeled time series broadly capture the temporal morphology of the geomagnetic storm at the monitoring sites. This method shows promise for spatially explicit, screening-level GIC assessment without requiring access to operator data.

Reconnection in the magnetotail occurs along so‐called X‐lines, where magnetic field lines tear and detach from plasma on microscopic spatial scales (comparable to particle gyroradii). In 2017–2020, the Magnetospheric MultiScale (MMS) mission detected X‐lines in the magnetotail enabling their investigation on local scales. However, the global structure and evolution of these X‐lines, critical for understanding their formation and total energy conversion mechanisms, remained virtually unknown because of the intrinsically local nature of observations and the extreme sparsity of concurrent data. Here, we show that mining a multi‐mission archive of space magnetometer data collected over the last 26 yr and then fitting a magnetic field representation modeled using flexible basis‐functions faithfully reconstructs the global pattern of X‐lines; 24 of the 26 modeled X‐lines match ( B z = 0 isocontours are within ∼2 Earth radii or R E ) or nearly match ( B z = 2 nT isocontours are within ∼2 R E ) the locations of the MMS encountered reconnection sites. The obtained global reconnection picture is considered in the context of substorm activity, including conventional substorms and more complex events.

Abstract An intriguing aspect of the famous September 2, 1859 geomagnetic disturbance (or “Carrington” event) is the horizontal magnetic (BH) data set measured in Colaba, India (magnetic latitude approximately 20°N). The field exhibits a sharp decrease of over 1,600 nT and a quick recovery of about 1,300 nT, all within a few hours during the daytime. The mechanism behind this has previously been attributed to magnetospheric processes, ionospheric processes or a combination of both. In this study, we outline our efforts to replicate this low‐latitude magnetic field using the Space Weather Modeling Framework. By simulating an extremely high pressure solar wind scenario, we can emulate the low‐latitude surface magnetic signal at Colaba. In our simulation, magnetospheric currents adjacent to the near‐Earth magnetopause and strong Region 1 field‐aligned currents are the main contributors to the large Colaba BH. The rapid recovery of BH in our simulated scenario is due to the retreat of these magnetospheric currents as the magnetosphere expands, as opposed to ring current dynamics. In addition, we find that the scenario that best emulated the surface magnetic field observations during the Carrington event had a minimum calculated Dst value between −431 and −1,191 nT, indicating that Dst may not be a suitable estimate of storm intensity for this kind of event.

Heliophysics data analysis often involves combining diverse science measurements, many of them captured as time series. Although there are now only a few commonly used data file formats, the diversity in mechanisms for automated access to and aggregation of such data holdings can make analysis that requires intercomparison of data from multiple data providers difficult. The Heliophysics Application Programmer's Interface (HAPI) is a recently developed standard for accessing distributed time series data to increase interoperability. The HAPI specification is based on the common elements of existing data services, and it standardizes the two main parts of a data service: the request interface and the response data structures. The interface is based on the REpresentational State Transfer (REST) or RESTful architecture style, and the HAPI specification defines five required REST endpoints. Data are returned via a streaming format that hides file boundaries; the metadata is detailed enough for the content to be scientifically useful, e.g., plotted with appropriate axes layout, units, and labels. Multiple mature HAPI‐related open‐source projects offer server‐side implementation tools and client‐side libraries for reading HAPI data in multiple languages (IDL, Java, MATLAB, and Python). Multiple data providers in the US and Europe have added HAPI access alongside their existing interfaces. Based on this experience, data can be served via HAPI with little or no information loss compared to similar existing web interfaces. Finally, HAPI has been recommended as a COSPAR standard for time series data delivery.

We present the construction of a new white-light coronal brightness index (CBI) from the entire archive of observations recorded by the Large Angle Spectrometric Coronagraph (LASCO) C2 camera between 1996 and 2017, comprising two full solar cycles. We reduce all fully calibrated daily C2 observations of the white-light corona into a single daily coronal brightness observation for every day of observation recorded by the instrument, with mean daily brightness values binned into 0.1 $\mathrm{R}_{\odot}$ radial $\times1$ degree angular regions from 2.4 – 6.2 $\mathrm{R}_{\odot}$ for a full 360 degrees. As a demonstration of the utility of the CBI, we construct a new solar irradiance proxy that correlates well with a variety of direct solar irradiance observations, with correlations shown to be in the range of 0.77 – 0.89. We also present a correlation mapping technique to show how irradiance correlations depend on, and relate to, coronal structure/locations, and to demonstrate how the LASCO CBI can be used to perform long-term “spatial correlation” studies to investigate relationships between the solar corona and any arbitrary concurrent geophysical index. Using this technique we find possible relationships between coronal brightness and plasma temperature, interplanetary magnetic field magnitude and (very weakly) proton density.

A common assumption used when estimating geomagnetically induced currents (GICs) in a power system given a time series of nearby direct measurements or indirect estimates of the horizontal geoelectric field components E-x(t) and E-y(t) on Earth's surface is that the system is resistive. That is, the approximation GIC(t)=a(o)E(x)(t)+b(o)E(y)(t) (Model 1) is used, where a(o) and b(o) are frequency-independent power system coefficients. A first test of this assumption is made using GIC measurements in a 187 kV transformer connected to a similar to 100 km power line in Memanbetsu, Japan and geoelectric field measurements made at the Memanbetsu Magnetic Observatory similar to 9 km away. A second model (Model 2) is obtained using a frequency domain generalization of Model 1: GIC(omega)=a(omega)E-x(omega)+b(omega)E-y(omega). The coefficients a(omega) and b(omega) are shown to be frequency-dependent, and this model provides significantly better estimates of the measured GIC than Model 1. Based on results using a simulated geoelectric field, it is suggested that the measurement-derived frequency dependence of the system coefficients may be explained by spatial variations in the spectrum of the geoelectric field over the spatial extent of the power system. It is also shown that further improvements over Model 2 can be made using frequency-dependent models with the geomagnetic field as an input.

We present results of an investigation of single-pixel intensity power spectra from a 12-hour time period on 26 June 2013 in a $1600 \times 1600$ -pixel region from four wavelength channels of NASA’s Solar Dynamics Observatory/Atmospheric Imaging Assembly. We extract single-pixel time series from derotated image sequences, fit two models as a function of frequency $[\nu]$ to their computed power spectra, and study the spatial dependence of the model parameters: i) a three-parameter power law + tail, $A\nu^{-n}+C$ , and ii) a power law + tail + three-parameter localized Lorentzian, $A\nu^{-n} + C + \alpha/ (1 + (\ln\nu-\beta)^{2} /\delta^{2} )$ , the latter to model periodicity. Spectra are well described by at least one of these models for all pixel locations, with the spatial distribution of best-fit model parameters shown to provide new and unique insights into turbulent, quiescent, and periodic features in the EUV corona and upper photosphere. Findings include the following: individual model parameters correspond clearly and directly to visible solar features; detection of numerous quasi-periodic three- and five-minute oscillations; observational identification of concentrated magnetic flux as regions of largest power-law indices $[n]$ ; identification of sporadically located five-minute oscillations throughout the corona; detection of the known global ${\approx}\,\mbox{four}$ -minute chromospheric oscillation; 2D spatial mapping of “coronal bullseyes” appearing as radially decaying periodicities over sunspots and sporadic foot-point regions, and of “penumbral periodic voids” appearing as broad rings around sunspots in 1600 and 1700 Å in which spectra contain no statistically significant periodic component.

Geomagnetic indices are convenient quantities that distill the complicated physics of some region or aspect of near-Earth space into a single parameter. Most of the best-known indices are calculated from ground-based magnetometer data sets, such as Dst, SYM-H, Kp, AE, AL, and PC. Many models have been created that predict the values of these indices, often using solar wind measurements upstream from Earth as the input variables to the calculation. This document reviews the current state of models that predict geomagnetic indices and the methods used to assess their ability to reproduce the target index time series. These existing methods are synthesized into a baseline collection of metrics for benchmarking a new or updated geomagnetic index prediction model. These methods fall into two categories: (1) fit performance metrics such as root-mean-square error and mean absolute error that are applied to a time series comparison of model output and observations and (2) event detection performance metrics such as Heidke Skill Score and probability of detection that are derived from a contingency table that compares model and observation values exceeding (or not) a threshold value. A few examples of codes being used with this set of metrics are presented, and other aspects of metrics assessment best practices, limitations, and uncertainties are discussed, including several caveats to consider when using geomagnetic indices. Plain Language Summary One aspect of space weather is a magnetic signature across the surface of the Earth. The creation of this signal involves nonlinear interactions of electromagnetic forces on charged particles and can therefore be difficult to predict. The perturbations that space storms and other activity causes in some observation sets, however, are fairly regular in their pattern. Some of these measurements have been compiled together into a single value, a geomagnetic index. Several such indices exist, providing a global estimate of the activity in different parts of geospace. Models have been developed to predict the time series of these indices, and various statistical methods are used to assess their performance at reproducing the original index. Existing studies of geomagnetic indices, however, use different approaches to quantify the performance of the model. This document defines a standardized set of statistical analyses as a baseline set of comparison tools that are recommended to assess geomagnetic index prediction models. It also discusses best practices, limitations, uncertainties, and caveats to consider when conducting a model assessment.

Data‐model validation of ground magnetic perturbation forecasts, specifically of the time rate of change of surface magnetic field, dB/dt, is a critical task for model development and for mitigation of geomagnetically induced current effects. While a current, community‐accepted standard for dB/dt validation exists (Pulkkinen et al., 2013), it has several limitations that prevent more complete understanding of model capability. This work presents recommendations from the International Forum for Space Weather Capabilities Assessment Ground Magnetic Perturbation Working Team for creating a next‐generation validation suite. Four recommendations are made to address the existing suite: greatly expand the number of ground observatories used, expand the number of events included in the suite from six to eight, generate metrics as a function of magnetic local time, and generate metrics as a function of activity type. For each of these, implementation details are explored. Limitations and future considerations are also discussed.

This paper is the primary deliverable of the very first NASA Living With a Star Institute Working Group, Geomagnetically Induced Currents (GIC) Working Group. The paper provides a broad overview of the current status and future challenges pertaining to the science, engineering, and applications of the GIC problem. Science is understood here as the basic space and Earth sciences research that allows improved understanding and physics-based modeling of the physical processes behind GIC. Engineering, in turn, is understood here as the "impact" aspect of GIC. Applications are understood as the models, tools, and activities that can provide actionable information to entities such as power systems operators for mitigating the effects of GIC and government agencies for managing any potential consequences from GIC impact to critical infrastructure. Applications can be considered the ultimate goal of our GIC work. In assessing the status of the field, we quantify the readiness of various applications in the mitigation context. We use the Applications Readiness Level (ARL) concept to carry out the quantification.

The geoelectric field is the primary input used for estimation of geomagnetically induced currents (GICs) in conducting systems. We compare three methods for estimating the geoelectric field given the measured geomagnetic field at four locations in the U.S. during time intervals with average Kp in the range of 2-3 and when the measurements had few data spikes and no baseline jumps. The methods include using (1) a preexisting 1-D conductivity model, (2) a conventional 3-D frequency domain method, and (3) a robust and remote reference 3-D frequency domain method. The quality of the estimates is determined using the power spectrum (in the period range 9.1 to 18,725 s) of estimation errors along with the prediction efficiency summary statistic. It is shown that with respect to these quality metrics, Method 1 produces average out-of-sample electric field estimation errors with a variance that can be equal to or larger than the average measured variance (due to underestimation or overestimation, respectively), and Method 3 produces reliable but slightly lower quality estimates than Method 2 for the time intervals and locations considered.

We consider two types of events, identified by decreases in Dst below a threshold value and increases in the equatorial mass density at geosynchronous altitudes, ρeq, above a threshold value using the Takahashi et al. (2010) data set. From the Dst events and 1 day averages, we find that there is a statistically weak and small‐amplitude difference between ρeq on the day of the event and the days before and after. When hourly averages are considered, a significant peak is found to occur 6 h after event onset, and the primary factor that determines the postonset peak amplitude in ρeq is elevated F10.7. In addition, for hourly averages, ρeq following the onset of a Dst event depends on the north‐south component of the interplanetary magnetic field, Bz, after the time of onset, with higher average Bz 4 h after the event onset corresponding to larger ρeq values 7–11 h after onset. From the ρeq events, we find a weak dependence on Bz after the onset of an event, with higher average Bz 4 h after the event onset corresponding to larger ρeq values 24–36 h after onset.

We present the latest result of a community-wide space weather model validation effort coordinated among the Community Coordinated Modeling Center (CCMC), NOAA Space Weather Prediction Center (SWPC), model developers, and the broader science community. Validation of geospace models is a critical activity for both building confidence in the science results produced by the models and in assessing the suitability of the models for transition to operations. Indeed, a primary motivation of this work is supporting NOAA/SWPC's effort to select a model or models to be transitioned into operations. Our validation efforts focus on the ability of the models to reproduce a regional index of geomagnetic disturbance, the local K-index. Our analysis includes six events representing a range of geomagnetic activity conditions and six geomagnetic observatories representing midlatitude and high-latitude locations. Contingency tables, skill scores, and distribution metrics are used for the quantitative analysis of model performance. We consider model performance on an event-by-event basis, aggregated over events, at specific station locations, and separated into high-latitude and midlatitude domains. A summary of results is presented in this report, and an online tool for detailed analysis is available at the CCMC.

We introduce new quantities for exploratory causal inference between bivariate time series. The quantities, called penchants and leanings, are computationally straightforward to apply, follow directly from assumptions of probabilistic causality, do not depend on any assumed models for the time series generating process, and do not rely on any embedding procedures; these features may provide a clearer interpretation of the results than those from existing time series causality tools. The penchant and leaning are computed based on a structured method for computing probabilities.

The upper atmosphere changes significantly in temperature, density and composition as a result of solar cycle variations, which causes severe storms and flares, and increases in the amount of absorbed solar radiation from solar energetic events. Satellite orbits are consequently affected by this process, especially those in low Earth orbit (LEO). In this paper, we present a model of atmospheric drag effects on the trajectory of two hypothetical LEO satellites of different ballistic coefficients, initially injected at h=450km. We investigate long-term trends of atmospheric drag on LEO satellites due to solar forcing induced atmospheric perturbations and heating at different phases of the solar cycle, and during short intervals of strong geomagnetic disturbances or magnetic storms. We show dependence of orbital decay on the severity of both solar cycle and phase and the extent of geomagnetic perturbations. The result of the model compares well with observed decay profile of some existing LEO satellites and provide a justification of the theoretical considerations used here.

Numerical schemes for ideal magnetohydrodynamics (MHD) that are based on the standard finite volume method (FVM) exhibit pseudo-convergence in which irregular structures no longer exist only after heavy grid refinement. We describe a method for obtaining solutions for coplanar and near-coplanar cases that consist of only regular structures, independent of grid refinement. The method, referred to as Compound Wave Modification (CWM), involves removing the flux associated with non-regular structures and can be used for simulations in two- and three-dimensions because it does not require explicitly tracking an Alfvén wave. For a near-coplanar case, and for grids with 213 points or less, we find root-square-mean-errors (RMSEs) that are as much as 6 times smaller. For the coplanar case, in which non-regular structures will exist at all levels of grid refinement for standard FVMs, the RMSE is as much as 25 times smaller.

Convergent cross-mapping (CCM) is a technique for computing specific kinds of correlations between sets of times series. It was introduced by Sugihara et al. [Science 338, 496 (2012).] and is reported to be ``a necessary condition for causation'' capable of distinguishing causality from standard correlation. We show that the relationships between CCM correlations proposed by Sugihara et al. do not, in general, agree with intuitive concepts of ``driving'' and as such should not be considered indicative of causality. It is shown that the fact that the CCM algorithm implies causality is a function of system parameters for simple linear and nonlinear systems. For example, in a circuit containing a single resistor and inductor, both voltage and current can be identified as the driver depending on the frequency of the source voltage. It is shown that the CCM algorithm, however, can be modified to identify relationships between pairs of time series that are consistent with intuition for the considered example systems for which CCM causality analysis provided nonintuitive driver identifications. This modification of the CCM algorithm is introduced as ``pairwise asymmetric inference'' (PAI) and examples of its use are presented.