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This report briefly summarizes the key mentors in my scientific career and some lessons learned from those influential people. My primary advice to others: it is okay to do something wrong. By doing science we are doing something hard that, by definition, has not been done before. I believe that impostor syndrome is a real threat to researcher wellbeing and we should acknowledge its presence and support each other to get through it. Regarding an approach to science, I encourage you to get started and make something bad. Also, take time for yourself, it really does help your productivity. To lead others, I recommend to be enthusiastic, actively listen, and make connections across disciplines. I think it is important to foster creativity in those around you. I advocate that you actively make the future that you want to have.

The 400 worst‐case severe environments for surface charging detected at Los Alamos National Laboratory satellites during the years of 1990–2005 as binned by the definitions of four criteria developed by Matéo‐Vélez et al. (2018,https://doi.org/10.1002/2017sw001689) and the solar wind and Interplanetary Magnetic Field (IMF) parameters and geomagnetic activity indices are analyzed. The conducted analysis shows that only Auroral Electrojet/Auroral Lower index determines the highest risk for severe environments for surface charging to happen. The presence of a substorm with the southward turning pattern in IMFindicates that the environment can be severe for surface charging to occur but this environment will not depend on whether a substorm was moderate or intense. No clear dependence on IMFis found for risk to a severe environment to occur. Appearances of severe environments for surface charging do not necessarily require high values ofKp(Planetarische Kennziffer) and no storm is needed for such an event to be detected. Among solar wind parameters, solar wind velocityis directly related to the highest risk of severe environments, dependent on thevalue; and number densityis of no importance. Two criteria for severe environment events based on the enhancements of low energy particle fluxes exhibit clearer dependencies on the solar wind and IMF parameters and geomagnetic activity indices with more distinct patterns in their time history.

We apply idealized scatterplot distributions to the sliding threshold of observation for numeric evaluation (STONE) curve, a new model assessment metric, to examine the relationship between the STONE curve and the underlying point‐spread distribution. The STONE curve is based on the relative operating characteristic (ROC) curve but is developed to work with a continuous‐valued set of observations, sweeping both the observed and modeled event identification threshold simultaneously. This is particularly useful for model predictions of time series data as is the case for much of terrestrial weather and space weather. The identical sweep of both the model and observational thresholds results in changes to both the modeled and observed event states as the quadrant boundaries shift. The changes in a data‐model pair's event status result in nonmonotonic features to appear in the STONE curve when compared to an ROC curve for the same observational and model data sets. Such features reveal characteristics in the underlying distributions of the data and model values. Many idealized data sets were created with known distributions, connecting certain scatterplot features to distinct STONE curve signatures. A comprehensive suite of feature‐signature combinations is presented, including their relationship to several other metrics. It is shown that nonmonotonic features appear if a local spread is more than 0.2 of the full domain or if a local bias is more than half of the local spread. The example of real‐time plasma sheet electron modeling is used to show the usefulness of this technique, especially in combination with other metrics.

Previously, Tsurutani and Lakhina (2014,https://doi.org/10.1002/2013GL058825) created estimates for a “perfect” interplanetary coronal mass ejection and performed simple calculations for the response of geospace, including. In this study, these estimates are used to drive a coupled magnetohydrodynamic‐ring current‐ionosphere model of geospace to obtain more physically accurate estimates of the geospace response to such an event. The sudden impulse phase is examined and compared to the estimations of Tsurutani and Lakhina (2014,https://doi.org/10.1002/2013GL058825). The physics‐based simulation yields similar estimates for Dst rise, magnetopause compression, and equatorialvalues as the previous study. However, results diverge away from the equator.values in excess of 30 nT/s are found as low asmagnetic latitude. Under southward interplanetary magnetic field conditions, magnetopause erosion combines with strong region one Birkeland currents to intensify theresponse. Values obtained here surpass those found in historically recorded events and set the upper threshold of extreme geomagnetically induced current activity at Earth.

Rosenqvist and Hall (2019),https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/2018SW002084developed a proof‐of‐concept modeling capability that incorporates a detailed 3D structure of Earth's electrical conductivity in a geomagnetically induced current estimation procedure (GIC‐SMAP). The model was verified based on GIC measurements in northern Sweden. The study showed that southern Sweden is exposed to stronger electric fields due to a combined effect of low crustal conductivity and the influence of the surrounding coast. This study aims at further verifying the model in this region. GIC measurements on a power line at the west coast of southern Sweden are utilized. The location of the transmission line was selected to include coast effects at the ocean‐land interface to investigate the importance of using 3D induction modeling methods. The model is used to quantify the hazard of severe GICs in this particular transmission line by using historic recordings of strong geomagnetic disturbances. To quantify a worst‐case scenario GICs are calculated from modeled magnetic disturbances by the Space Weather Modeling Framework based on estimates for an idealized extreme interplanetary coronal mass ejection. The observed and estimated GIC based on the 3D GIC‐SMAP procedure in the transmission line in southern Sweden are in good agreement. In contrast, 1D methods underestimate GICs by about 50%. The estimated GICs in the studied transmission line exceed 100 A for one of 14 historical geomagnetic storm intervals. The peak GIC during the sudden impulse phase of a “perfect” storm exceeds 300 A but depends on the locality of the station as the interplanetary magnetic cloud hits Earth.

The accurate determination of auroral precipitation in global models has remained a daunting and rather inexplicable obstacle. Understanding the calculation and balance of multiple sources that constitute the aurora, and their eventual conversion into ionospheric electrical conductance, is critical for improved prediction of space weather events. In this study, we present a semi‐physical global modeling approach that characterizes contributions by four types of precipitation—monoenergetic, broadband, electron, and ion diffuse—to ionospheric electrodynamics. The model uses a combination of adiabatic kinetic theory and loss parameters derived from historical energy flux patterns to estimate auroral precipitation from magnetohydrodynamic (MHD) quantities. It then converts them into ionospheric conductance that is used to compute the ionospheric feedback to the magnetosphere. The model has been employed to simulate the 5–7 April 2010Galaxy15space weather event. Comparison of auroral fluxes show good agreement with observational data sets like NOAA‐DMSP and OVATION Prime. The study shows a dominant contribution by electron diffuse precipitation, accounting for ∼74% of the auroral energy flux. However, contributions by monoenergetic and broadband sources dominate during times of active upstream solar conditions, providing for up to 61% of the total hemispheric power. The study also finds a greater role played by broadband precipitation in ionospheric electrodynamics which accounts for ∼31% of the Pedersen conductance.

In this study, the Global Ionosphere Thermosphere Model is utilized to investigate the inter‐hemispheric asymmetry in the ionosphere‐thermosphere (I‐T) system at mid‐ and high‐latitudes (|geographic latitude| > 45°) associated with inter‐hemispheric differences in (a) the solar irradiance, (b) geomagnetic field, and (c) magnetospheric forcing under moderate geomagnetic conditions. Specifically, we have quantified the relative significance of the above three causes to the inter‐hemispheric asymmetries in the spatially weighted averaged E‐region electron density, F‐region neutral mass density, and horizontal neutral wind along with the hemispheric‐integrated Joule heating. Further, an asymmetry index defined as the percentage differences of these four quantities between the northern and southern hemispheres (|geographic latitude| > 45°) was calculated. It is found that: (a) The difference of the solar extreme ulutraviolet (EUV) irradiance plays a dominant role in causing inter‐hemispheric asymmetries in the four examined I‐T quantities. Typically, the asymmetry index for the E‐region electron density and integrated Joule heating at solstices with F10.7 = 150 sfu can reach 92.97% and 38.25%, respectively. (b) The asymmetric geomagnetic field can result in a strong daily variation of inter‐hemispheric asymmetries in the F‐region neutral wind and hemispheric‐integrated Joule heating over geographic coordinates. Their amplitude of asymmetry indices can be as large as 20.81% and 42.52%, which can be comparable to the solar EUV irradiance effect. (c) The contributions of the asymmetric magnetospheric forcing, including particle precipitation and ion convection pattern, can cause the asymmetry of integrated Joule heating as significant as 28.43% and 34.72%, respectively, which can be even stronger than other causes when the geomagnetic activity is intense.

Auroral particle precipitation is the main source of ionization on the nightside, making it a critical factor in geospace physics. This magnetosphere‐ionosphere linkage directly contributes to, even controls, the nonlinear feedback within this coupled system. One study has dominated our understanding of this connection, presenting a pair of equations relating auroral particle precipitation to ionospheric Pedersen and Hall conductance, the famous Robinson formulas. This Commentary examines the history of the development and usage of the Robinson formulas and the recent studies exploring corrections and expansions to it. The conclusion is that more work needs to be done; the space physics research community should take up the task to develop improvements and enhancements to better quantify the connection of auroral precipitation to ionospheric conductance.

We have developed a new procedure for combining lists of substorm onset times from multiple sources. We apply this procedure to observational data and to magnetohydrodynamic (MHD) model output from 1–31 January 2005. We show that this procedure is capable of rejecting false positive identifications and filling data gaps that appear in individual lists. The resulting combined onset lists produce a waiting time distribution that is comparable to previously published results, and superposed epoch analyses of the solar wind driving conditions and magnetospheric response during the resulting onset times are also comparable to previous results. Comparison of the substorm onset list from the MHD model to that obtained from observational data reveals that the MHD model reproduces many of the characteristic features of the observed substorms, in terms of solar wind driving, magnetospheric response, and waiting time distribution. Heidke skill scores show that the MHD model has statistically significant skill in predicting substorm onset times.

An intriguing aspect of the famous September 2, 1859 geomagnetic disturbance (or “Carrington” event) is the horizontal magnetic (B_H) 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 ColabaB_H. The rapid recovery ofB_Hin 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.