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One of the definitions of space weather describes it as the time-varying space environment that may be hazardous to technological systems in space and/or on the ground and/or endanger human health or life. The ring current has its contributions to space weather effects, both in terms of particles, ions and electrons, which constitute it, and magnetic and electric fields produced and modified by it at the ground and in space. We address the main aspects of the space weather effects from the ring current starting with brief review of ring current discovery and physical processes and the Dst-index and predictions of the ring current and storm occurrence based on it. Special attention is paid to the effects on satellites produced by the ring current electrons. The ring current is responsible for several processes in the other inner magnetosphere populations, such as the plasmasphere and radiation belts which is also described. Finally, we discuss the ring current influence on the ionosphere and the generation of geomagnetically induced currents (GIC).

Prediction efficiency (PE) is a skill score that compares the data‐model metric of mean square error against the variance of the observations (i.e., using the average of the observed values as the “reference model” in the general skill score formula). This has proven to be highly useful for analysis using “continuous metrics”—those data‐model comparison techniques that employ the exact values of the observations and model results—especially when comparing the new model's performance against an existing model. Another major grouping of data‐model comparisons is “event detection analysis” in which all observational and numerical values are converted into a binary yes‐no categorization of being in or out of “event state.” There is, however, no equivalent skill score to PE within the event detection toolkit. This study proposes two such options, one based on the proportion correct metric and another based on the critical success index metric. Like PE, these new skill scores use the observations as the reference model, which provides complete independence of the reference model from the new model. It is demonstrated, reevaluating two space weather data‐model comparison studies, that these skill scores provide context for model evaluation that is unique to other existing metrics and valuable for the assessment, especially with respect to comparing the new model's performance against an existing model. Reference models in existing skill scores are based in part on the new model's performance against the data, which leads to ambiguous interpretation for intermodel comparison usage.

The Hot Electron and Ion Drift Integrator (HEIDI) model was used to simulate all of the intense storms (Dstmin < −100 nT) from solar cycle 23 (1996–2005). These storms were classified according to their heliospheric driving structure, namely, either an interplanetary coronal mass ejection (ICME) or a corotating interaction region and its trailing high‐speed stream (CIR/HSS). Five different HEIDI input combinations were used to create a large collection of numerical results, varying the plasma outer boundary condition and electric field description in the model. Statistical data‐model analyses were conducted on the total energy content, yielding error estimates on the correlation coefficients and root‐mean‐square error values for each run set. The accuracy of each run set depends on the method of comparison and classification of the driver. For the correlation coefficients, the simulations using a local‐time‐dependent outer boundary condition were consistently better than those using a local‐time‐averaged (but high‐time‐resolution) nightside boundary condition, with the simplistic electric field being better than the self‐consistent field description. For the root‐mean‐square error, the results are less conclusive. For the CIR/HSS‐driven storms, those with the high‐time‐resolution boundary condition were systematically better than those with the local‐time‐dependent (but lower‐time‐resolution) boundary condition. For the ICME‐driven storms, those run sets employing the self‐consistent electric field calculation were systematically better than those using the simplistic electric field. The implication, therefore, is that the inner magnetospheric physical response to strong driving is, at least to some degree, fundamentally different depending on the heliospheric structure impacting geospace. Specifically, for an accurate SYMH* comparison, it is found that CIR/HSS events respond strongly to transient spikes in the plasma outer boundary condition, while ICME passages exhibit a more highly structured electric field. Key Points We have conducted simulations of all intense storms of solar cycle 23 The inner magnetospheric physical response is different depending on solar wind driver The best fit model result changes with choice of error statistic

This study quantifies several factors controlling the probability of a pickup oxygen ion to escape from the Mars upper atmosphere. It is commonly presumed that ions with sufficient kinetic energy are able to escape to space. To test the validity of this simple assumption, we examined results from our Monte Carlo model, which monitors the motion of billions of test particles due to gravity and the Lorentz force through the electromagnetic fields of a magnetohydrodynamic model solution. It is shown that the electromagnetic fields are the dominant factor, surpassing the deceleration of gravity, in controlling ion transport and thus determine whether particles ultimately escape Mars or return to the planet. The particle kinetic energy and the local time of the crustal fields are also important factors greatly influencing the escape probability. In a simulation case in which the strongest crustal fields face the Sun at nominal solar minimum conditions, on average, only 45% of isotropically distributed newborn particles at ∼400 km altitude are able to escape, even with a sufficiently high initial energy of ∼10 eV. Furthermore, there is a distinct hemispheric asymmetry in the escape probability distribution, as defined by the upstream convection electric field direction (Esw). In the above case, the particles produced in the −Esw hemisphere have a much smaller chance to escape, on average, about 17%. These findings imply that one has to be careful when using satellite periapsis measurements to estimate atmospheric loss, where ion densities are high but escape chances may be very low.

Theoretical and numerical modifications to an inner magnetosphere model—Hot Electron Ion Drift Integrator (HEIDI)—were implemented, in order to accommodate for a nondipolar arbitrary magnetic field. While the dipolar solution for the geomagnetic field during quiet times represents a reasonable assumption in the near‐Earth closed field region, during storm activity this assumption becomes invalid. HEIDI solves the time‐dependent, gyration‐ and bounce‐averaged kinetic equation for the phase space density of one or more ring current species. New equations are derived for the bounce‐averaged coefficients for the distribution function, and their numerical implementation is discussed. Also, numerically solving all the bounce‐averaged coefficients for the dipole case does not change the results significantly from the analytical approximation of Ejiri (1978). However, distorting the magnetic field changes all bounce‐averaged coefficients that make up the kinetic equation. Initial simulations show that changing the magnetic field changes the whole topology of the ring current. This is because the drifts are altered due to dayside compression and nightside stretching of the field. Therefore, at certain locations, the nondipolar magnetic drifts can dominate the convective drifts, considerably altering the pressure distribution in the equatorial plane. Key Points New bounce average formalism for arbitrary magnetic field Distorting the magnetic field changes all bounce averaged coefficients Under certain disturbed conditions, the magnetic drift becomes the dominat drift

The Geospace Environment Modeling (GEM) program regularly issues “community challenges” in which researchers examine a particular space physics phenomenon or geomagnetic activity event, often running numerical models to assess dominant processes and understand the timing and relationship of observed signatures. The GEM Methods and Validation Resource Group helps those GEM focus group leaders running challenges to maximize participation and optimize scientific return from the significant time investment of these endeavors. This article gives a brief history of GEM community challenges and details those best practices that lead to an inclusive and valuable experience. Plain Language Summary Over 30 years ago, a group of space scientists set out to coordinate efforts toward the creation of a community‐wide numerical modeling resource. This led to the formation of the Geospace Environment Modeling program, and one of the regular activities of this program is the instigation of “community challenges.” These challenges typically select a particular geospace activity interval or a physical process and then rally the research community to participate in the analysis of this phenomenon. The practice has led to substantial new knowledge of Earth's space environment and significant advancements in numerical modeling capabilities of this region. Here, we describe the history of these community challenges, highlight the lessons learned, and collect the best practices that maximize participation and optimize scientific return. Key Points A history is presented of the “community challenges” conducted over the past 3 decades within the Geospace Environment Modeling Program Key recommendations and lessons learned from past challenge leaders, as well as suggestions from the research community, are presented Additional resources that might aid in the successful running of a community challenge are given, including a summary of metrics options

This study presents a multistream superthermal electron transport model for the Mars space environment. This model includes the magnetic inhomogeneity effects, which is vital to understand electron motion around Mars. The convergence tests on the step sizes of variables are carried out and appropriate grid setups are determined. In addition, we have examined three physical parameters, F10.7 values, thermal electron/plasma density, and neutral densities. Through the investigation of F10.7 values, an interesting fact about the Hinteregger model is found that the photon flux of each wavelength is scaled differently. The resultant photoelectron fluxes also show a nonuniform percentage of increase. The results of plasma density and neutral densities tests are consistent with previous theories, such as the expected degradation of fluxes in the low‐energy range with increased thermal electron/plasma density, and the elevated peak altitude of photoelectron fluxes with increased neutral densities. The examination of these physical parameters indicates the model's ability to simulate various environments and verifies the model's performance. Finally, a data‐model comparison is carried out and the modeled omnidirectional fluxes agree well (within a factor of 2) with the observation. Key Points Presents a time‐dependent, multistream hot electron transport model for Mars Conducted convergence tests of this model for closed magnetic field lines Validated the model's performance by examining several physical parameters

This study investigates the role of fluctuations in the solar wind parameters in triggering a magnetic storm and assesses the storm simulation ability of the Space Weather Modeling Framework (SWMF) through a model–data comparison. The event of 22 September 1999 is examined through global magnetosphere simulations, using as input Advanced Composition Explorer (ACE) observations (4 min temporal resolution) along with running averages of this data with windows of 60, 120, and 180 min. It is noted that for this storm the model produces a two phase, fast then slow recovery phase due to a sudden drop in plasma sheet density during the interval of southward interplanetary magnetic field (IMF). Also, smoothing the input with a window larger than 60 min changes the entire magnetosphere and reduces the plasma sheet density and pressure, therefore a less intense storm develops. It is worth mentioning that the main phase (measured from Storm Sudden Commencement to minimum Dst) for this magnetic storm lasted about 3 h. This explains the change in the Dst profile for the 120 and 180 min averaged input. Averaging only IMF Bz or solar wind density reveals that all input parameters are important for the development of the storm, but Bz is the most significant. Also, comparison with Dst predictions (using the formula of O'Brien and McPherron (2000)) are presented and discussed. For all cases studied, there are no significant differences for Cross Polar Cap Potential (CPCP) in both hemispheres, while the nightside plasma sheet density shows a sharp drop when the input is averaged over 60 min or more. Our results indicate that the magnetosphere responds nonlinearly to the changes in the energy input, suggesting the need for a threshold in the amount of energy transferred to the system in order for the ring current to develop. Further increase of the energy input leads to a saturation limit where the inner magnetosphere response is no longer affected by any additional amount of energy contained within high‐frequency oscillations, because the magnetospheric system acts as a low‐pass filter on the interplanetary magnetic field.

A new model validation and performance assessment tool is introduced, the sliding threshold of observation for numeric evaluation (STONE) curve. It is based on the relative operating characteristic (ROC) curve technique, but instead of sorting all observations in a categorical classification, the STONE tool uses the continuous nature of the observations. Rather than defining events in the observations and then sliding the threshold only in the classifier/model data set, the threshold is changed simultaneously for both the observational and model values, with the same threshold value for both data and model. This is only possible if the observations are continuous and the model output is in the same units and scale as the observations, that is, the model is trying to exactly reproduce the data. The STONE curve has several similarities with the ROC curve—plotting probability of detection against probability of false detection, ranging from the (1,1) corner for low thresholds to the (0,0) corner for high thresholds, and values above the zero‐intercept unity‐slope line indicating better than random predictive ability. The main difference is that the STONE curve can be nonmonotonic, doubling back in both the x and y directions. These ripples reveal asymmetries in the data‐model value pairs. This new technique is applied to modeling output of a common geomagnetic activity index as well as energetic electron fluxes in the Earth's inner magnetosphere. It is not limited to space physics applications but can be used for any scientific or engineering field where numerical models are used to reproduce observations. Plain Language Summary Scientists often try to reproduce observations with a model, helping them explain the observations by adjusting known and controllable features within the model. They then use a large variety of metrics for assessing the ability of a model to reproduce the observations. One such metric is called the relative operating characteristic (ROC) curve, a tool that assesses a model's ability to predict events within the data. The ROC curve is made by sliding the event‐definition threshold in the model output, calculating certain metrics and making a graph of the results. Here, a new model assessment tool is introduced, called the sliding threshold of observation for numeric evaluation (STONE) curve. The STONE curve is created by sliding the event definition threshold not only for the model output but also simultaneously for the data values. This is applicable when the model output is trying to reproduce the exact values of a particular data set. While the ROC curve is still a highly valuable tool for optimizing the prediction of known and preclassified events, it is argued here that the STONE curve is better for assessing model prediction of a continuous‐valued data set. Key Points A new event‐detection‐based metric for model performance appraisal is given with sliding thresholds in both observational and model values The new metric is like the relative operating characteristic curve but uses continuous observational values, not just categorical status The new metric is used on real‐time model predictions of common geomagnetic activity parameters, demonstrating its features and strengths

A new view of the ring current as an active element in the geospace system has emerged in which the ring current responds not only to changing convection electric fields imposed by solar wind interactions but to internal dynamics of the magnetosphere-ionosphere-atmosphere (geospace) system. Variations in the plasma sheet density, temperature and composition, saturation of the polar cap potential drop (and presumably the cross-tail potential drop), modifications to the imposed convection potential in the inner magnetosphere due to ring current shielding effects, the presence of a pre-existing ring current population, storm-substorm coupling, and strong convection with and without accompanying substorm activity all have an impact on the ring current strength, formation and loss. All of these internal processes imply that the geoeffectiveness of a solar wind driver cannot be predicted on the basis of the characteristics of the driver alone but must reflect key aspects of the dynamically changing geospace environment, itself. This review gives a summary of new information on ring current input and decay processes focusing on implications for the global geospace response to solar wind drivers during magnetic storms and on open questions that can be addressed with new ENA imaging techniques.