OPINION article Front. Astron. Space Sci., 08 April 2024Sec. Space Physics Volume 11 - 2024 | https://doi.org/10.3389/fspas.2024.1391990

AbstractThe Iterative Driver Estimation and Assimilation (IDEA) data assimilation technique was used with the Whole Atmosphere Model (WAM) to improve neutral density specification in the upper thermosphere. Two different neutral density data sources were examined to enhance the capability of simulating the global thermospheric state. The first were accelerometer estimates of neutral density from the Challenging Mini‐Satellite Payload (CHAMP) satellite. The second were neutral density estimates from the Global Ultraviolet Imager (GUVI) limb‐scan airglow observations aboard the Thermosphere Ionosphere Mesosphere Energetics and Dynamics satellite. Due to the intensity of the November 2003 storm, two changes were necessary in WAM. The first was allowing the Kp geomagnetic index to exceed 9 and the second was changing the relationship between Kp and the solar wind parameters used to drive the model. With these changes, results show that IDEA effectively captures the thermospheric neutral density at the CHAMP satellite altitude and follows the time‐dependence through the November 2003 storm period. Furthermore, a cross‐comparison was conducted with the GUVI dayside limb scan measurements. GUVI neutral densities within 270–320 km show the closest agreement with WAM when CHAMP data was assimilated by IDEA. We speculate on the potential for observations from GUVI at 300 km to be used as a data source in the IDEA‐WAM simulations. These simulations demonstrate the utility of the IDEA data assimilation technique with physical models and that using either accelerometer observations or ultraviolet airglow limb measurement during extreme storm periods could be used.

A chain of magnetometers has been placed in Antarctica for comparisons with magnetic field measurements taken in the Northern Hemisphere. The locations were chosen to be on magnetic field lines that connect to magnetometers on the western coast of Greenland, despite the difficulty of reaching and working at such remote locations. We report on some basic comparisons of the similarities and differences in the conjugate measurements. Our results presented here confirm that the conjugate sites do have very similar (symmetric) magnetic perturbations in a handful of cases, as expected. Sign reversals are required for two components in order to obtain this agreement, which is not commonly known. More frequently, a strong Y component of the Interplanetary Magnetic Field (IMF) breaks the symmetry, as well as the unequal conductivities in the opposite hemispheres, as shown in two examples. In one event the IMF Y component reversed signs twice within 2 hours, while the magnetometer chains were approaching local noon. This switch provided an opportunity to observe the effects at the conjugate locations and to measure time lags. It was found that the magnetic fields at the most poleward sites started to respond to the sudden IMF reversals 20 min after the IMF reaches the bow shock, a measure of the time it takes for the electromagnetic signal to travel to the magnetopause, and then along magnetic field lines to the polar ionospheres. An additional 9–14 min is required for the magnetic perturbations to complete their transition.

Abstract We present results from a study of the time lags between changes in the energy flow into the polar regions and the response of the thermosphere to the heating. Measurements of the neutral density from the Challenging Mini‐satellite Payload (CHAMP) and Gravity Recovery and Climate Experiment (GRACE) missions are used, along with calculations of the total Poynting flux entering the poles. During two major geomagnetic storms in 2003, these data show increased densities are first seen on the dayside edge of the auroral ovals after a surge in the energy input. At lower latitudes, the densities reach their peak values on the dayside earlier than on the night side. A puzzling response seen in the CHAMP measurements during the November 2003 storm was that the density at a fixed location near the “Harang discontinuity” remained at unusually low levels during three sequential orbit passes, while elsewhere the density increased. The entire database of measurements from the CHAMP and GRACE missions were used to derive maps of the density time lags across the globe. The maps show a large gradient between short and long time delays between 60° and 30° geographic latitude. They confirm the findings from the two storm periods, that near the equator, the density on the dayside responds earlier than on the nightside. The time lags are longest near 18–20 hr local time. The time lag maps could be applied to improve the accuracy of empirical thermosphere models, and developers of numerical models may find these results useful for comparisons with their calculations.

Abstract Solar eclipses present a rare glimpse into the impact of ionospheric electrodynamics on the magnetosphere independent of other well studied seasonal influences. Despite decades of study, we still do not have a complete description of the conditions for geomagnetic substorm onset. We present herein a mutual information based study of previously published substorm onsets and the past two decades of eclipses which indicates the likelihood of co‐occurrence is greater than random chance. A plausible interpretation for this relation suggests that the abrupt fluctuations in ionospheric conductivity during an eclipse may influence the magnetospheric preconditions of substorm initiation. While the mechanism remains unclear, this study presents strong evidence of a link between substorm onset and solar eclipses.

Empirical models have for long made significant contributions to both basic research and predictive applications.However, dedicated support for empirical modeling has faded over time in support of physics-based models.Recently, exploitation of Machine Learning methods for modeling has also significantly advanced capabilities for empirical modeling.Additionally, the numerical techniques employed in empirical modeling is losing its footing in graduate school curriculums.We recommend consideration of dedicated investment across agencies in support of research and educational efforts towards empirical modeling of Geospace.

Models for space weather forecasting will never be complete/valid without accounting for interhemispheric asymmetries in Earth's magnetosphere, ionosphere and thermosphere.This whitepaper is a strategic vision for understanding these asymmetries from a global perspective of geospace research and space weather monitoring.It includes both observation and modeling studies related to the topic.The importance of investigating inter-hemispheric asymmetries space physics research needs to be shown in the decadal survey.It is crucial to understand how each source of asymmetry interacts with each other and how asymmetric structures incorporate this information into real-time monitoring and predictive models of space weather impacts globally.Future challenges of inter-hemispheric asymmetries for Heliophysics investigations are discussed and so are potential solutions.Recently, interhemispheric asymmetry has gained significant interest with the community, both major funding agencies officially supporting relevant programs: the NSF Geospace Environment Modeling (GEM) is currently hosting a focus group "Interhemispheric Approaches to Understanding M-I Coupling (IHMIC)" for the years 2018-2023.A similar topic is also a focused science topic in NASA's Living With a Star (LWS) program, "Causes and Consequences of Hemispherical Asymmetries in the Magnetosphere-Ionosphere-Thermosphere System".The NASA's Drive Center program once hosted a center called "Center for Unified Studies for Interhemispheric Asymmetries (CUSIA)".The European agency, International Space Science Institute (ISSI), funded two international teams carrying out research focusing on the asymmetries (2014 and current).The community consensus, however, is that there are still extensive data gaps in the southern hemisphere and the current models do not sufficiently address the asymmetric features of the geospace environments.

This paper demonstrates a simulation framework that collects data about connected vehicles' locations and surroundings in a realistic traffic scenario. Our focus lies on the capability to detect parking spots and their occupancy status. We use this data to train machine learning models that predict parking occupancy levels of specific areas in the city center of San Francisco. By comparing their performance to a given ground truth, our results show that it is possible to use simulated connected vehicle data as a base for prototyping meaningful AI-based applications.

Abstract The Mass Spectrometer and Incoherent Scatter radar (MSIS) model family has been developed and improved since the early 1970's. The most recent version of MSIS is the Naval Research Laboratory (NRL) MSIS 2.0 empirical atmospheric model. NRLMSIS 2.0 provides species density, mass density, and temperature estimates as function of location and space weather conditions. MSIS models have long been a popular choice of thermosphere model in the research and operations community alike, but—like many models—does not provide uncertainty estimates. In this work, we develop an exospheric temperature model based in machine learning that can be used with NRLMSIS 2.0 to calibrate it relative to high‐fidelity satellite density estimates directly through the exospheric temperature parameter. Instead of providing point estimates, our model (called MSIS‐UQ) outputs a distribution which is assessed using a metric called the calibration error score. We show that MSIS‐UQ debiases NRLMSIS 2.0 resulting in reduced differences between model and satellite density of 25% and is 11% closer to satellite density than the Space Force's High Accuracy Satellite Drag Model. We also show the model's uncertainty estimation capabilities by generating altitude profiles for species density, mass density, and temperature. This explicitly demonstrates how exospheric temperature probabilities affect density and temperature profiles within NRLMSIS 2.0. Another study displays improved post‐storm overcooling capabilities relative to NRLMSIS 2.0 alone, enhancing the phenomena that it can capture.

Modeling of the upper atmosphere, specifically the thermosphere mass density, remains the primary source of uncertainty in satellite drag and orbital operations in low Earth orbit (LEO). The variations in mass density are dominated by changes in solar irradiance on the timescales of the solar cycle, however, short-term space weather changes can significantly impact the state of the thermosphere, especially during geomagnetic storms. Because of our limited understanding of such variations and the resulting inaccurate modeling, quantifying the uncertainty in density specification and forecasting becomes critical for space operations including decision making for collision avoidance and safeguarding of our space assets. The Naval Research Laboratory’s MSIS model is one of the most widely used models in operations, especially in the commercial industry. Several different versions of the models have been developed, the most recent being MSIS 2.0. A new methodology for calibration of the MSIS model with exospheric temperatures inverted using accelerometer-derived density estimates has recently been developed. In this work, we apply a similar but updated methodology to the MSIS 2.0 model and use machine learning, specifically a neural network, to develop a version of the MSIS 2.0 model calibrated to the accelerometer-derived density estimates that also provides reliable uncertainty estimates.

Machine learning (ML) models are universal function approximators and—if used correctly—can summarize the information content of observational data sets in a functional form for scientific and engineering applications. A benefit to ML over parametric models is that there are no a priori assumptions about particular basis functions which can potentially limit the phenomena that can be modeled. In this work, we develop ML models on three data sets: the Space Environment Technologies High Accuracy Satellite Drag Model (HASDM) density database, a spatiotemporally matched data set of outputs from the Jacchia‐Bowman 2008 Empirical Thermospheric Density Model (JB2008), and an accelerometer‐derived density data set from CHAllenging Minisatellite Payload (CHAMP). These ML models are compared to the Naval Research Laboratory Mass Spectrometer and Incoherent Scatter radar (NRLMSIS 2.0) model to study the presence of post‐storm cooling in the middle‐thermosphere. We find that both NRLMSIS 2.0 and JB2008‐ML do not account for post‐storm cooling and consequently perform poorly in periods following strong geomagnetic storms (e.g., the 2003 Halloween storms). Conversely, HASDM‐ML and CHAMP‐ML do show evidence of post‐storm cooling indicating that this phenomenon is present in the original data sets. Results show that density reductions up to 40% can occur 1–3 days post‐storm depending on the location and strength of the storm.

We have used empirical models for electric potentials and the magnetic fields both in space and on the ground to obtain maps of the height-integrated Pedersen and Hall ionospheric conductivities at high latitudes. This calculation required use of both "curl-free" and "divergencefree" components of the ionospheric currents, with the former obtained from magnetic fields that are used in a model of the field-aligned currents. The second component is from the equivalent current, usually associated with Hall currents, derived from the ground-level magnetic field. Conductances were calculated for varying combinations of the interplanetary magnetic field (IMF) magnitude and orientation angle, as well as the dipole tilt angle. The results show that reversing the sign of the Y component of the IMF produces substantially different conductivity patterns. The Hall conductivities are largest on the dawn side in the upward, Region 2 field-aligned currents. Low electric field strengths in the Harang discontinuity lead to inconclusive results near midnight. Calculations of the Joule heating, obtained from the electric field and both components of the ionospheric current, are compared with the Poynting flux in space. The maps show some differences, while their integrated totals match to within 1 %. Some of the Poynting flux that enters the polar cap is dissipated as Joule heating within the auroral ovals, where the conductivity is greater.

Abstract The EXospheric TEMperatures on a PoLyhedrAl gRid (EXTEMPLAR) method predicts the neutral densities in the thermosphere. The performance of this model has been evaluated through a comparison with the Air Force High Accuracy Satellite Drag Model (HASDM). The Space Environment Technologies (SET) HASDM database that was used for this test spans the 20 years 2000 through 2019, containing densities at 3 hr time intervals at 25 km altitude steps, and a spatial resolution of 10° latitude by 15° longitude. The upgraded EXTEMPLAR that was tested uses the newer Naval Research Laboratory MSIS 2.0 model to convert global exospheric temperature values to neutral density as a function of altitude. The revision also incorporated time delays that varied as a function of location, between the total Poynting flux in the polar regions and the exospheric temperature response. The density values from both models were integrated on spherical shells at altitudes ranging from 200 to 800 km. These sums were compared as a function of time. The results show an excellent agreement at temporal scales ranging from hours to years. The EXTEMPLAR model performs best at altitudes of 400 km and above, where geomagnetic storms produce the largest relative changes in neutral density. In addition to providing an effective method to compare models that have very different spatial resolutions, the use of density totals at various altitudes presents a useful illustration of how the thermosphere behaves at different altitudes, on time scales ranging from hours to complete solar cycles.

The community has leveraged satellite accelerometer data sets in previous years to estimate neutral mass density and exospheric temperatures. We utilize derived temperature data and optimize a nonlinear machine‐learned (ML) regression model to improve upon the performance of the linear EXospheric TEMPeratures on a PoLyhedrAl gRid (EXTEMPLAR) model. The newly developed EXTEMPLAR‐ML model allows for exospheric temperature predictions at any location with one model and provides performance improvements over its predecessor. We achieve reductions in mean absolute error of 2 K on an independent test set while providing similar error standard deviation values. Comparing the performance of both EXTEMPLAR models and the Naval Research Laboratory Mass Spectrometer and Incoherent Scatter radar Extended model (NRLMSISE‐00) across different solar and geomagnetic activity levels shows that EXTEMPLAR‐ML has the lowest mean absolute error across 80% of conditions tested. A study for spatial errors demonstrated that at all grid locations, EXTEMPLAR‐ML has the lowest mean absolute error for over 60% of the polyhedral grid cells on the test set. Like EXTEMPLAR, our model's outputs can be utilized by NRLMSISE‐00 (exclusively) to more closely match satellite accelerometer‐derived densities. We conducted 10 case studies where we compare the accelerometer‐derived temperature and density estimates from four satellites to NRLMSISE‐00, EXTEMPLAR, and EXTEMPALR‐ML during major storm periods. These comparisons show that EXTEMPLAR‐ML generally has the best performance of the three models during storms. We use principal component analysis on EXTEMPLAR‐ML outputs to verify the physical response of the model to its drivers.

Interplanetary (IP) shocks drive magnetosphere‐ionosphere (MI) current systems that in turn are associated with ground magnetic perturbations. Recent work has shown that IP shock impact angle plays a significant role in controlling the subsequent geomagnetic activity and magnetic perturbations; for example, highly inclined shocks drive asymmetric MI responses due to interhemispherical asymmetric magnetospheric compressions, while almost head‐on shocks drive more symmetric MI responses. However, there are few observations confirming that inclined shocks drive such asymmetries in the high‐latitude ground magnetic response. We use data from a chain of Antarctic magnetometers, combined with magnetically conjugate stations on the west coast of Greenland, to test these model predictions (Oliveira & Raeder, 2015, https://doi.org/10.1002/2015JA021147; Oliveira, 2017, https://doi.org/10.1007/s13538-016-0472-x). We calculate the time derivative of the magnetic field ( ∂B/∂t ) in each hemisphere separately. Next, we examine the ratio of Northern to Southern Hemisphere ∂B/∂t intensities and the time differences between the maximum ∂B/∂t immediately following the impact of IP shocks. We order these results according to shock impact angles obtained from a recently published database with over 500 events and discuss how shock impact angles affect north‐south hemisphere asymmetries in the ground magnetic response. We find that the hemisphere the shock strikes first usually has (1) the first response in ∂B/∂t and (2) the most intense response in ∂B/∂t . Additionally, we show that highly inclined shocks can generate high‐latitude ground magnetic responses that differ significantly from predictions based on models that assume symmetric driving conditions.

The habitability of the surface of any planet is determined by a complex evolution of its interior, surface, and atmosphere. The electromagnetic and particle radiation of stars drive thermal, chemical and physical alteration of planetary atmospheres, including escape. Many known extrasolar planets experience vastly different stellar environments than those in our Solar system: it is crucial to understand the broad range of processes that lead to atmospheric escape and evolution under a wide range of conditions if we are to assess the habitability of worlds around other stars. One problem encountered between the planetary and the astrophysics communities is a lack of common language for describing escape processes. Each community has customary approximations that may be questioned by the other, such as the hypothesis of H-dominated thermosphere for astrophysicists, or the Sun-like nature of the stars for planetary scientists. Since exoplanets are becoming one of the main targets for the detection of life, a common set of definitions and hypotheses are required. We review the different escape mechanisms proposed for the evolution of planetary and exoplanetary atmospheres. We propose a common definition for the different escape mechanisms, and we show the important parameters to take into account when evaluating the escape at a planet in time. We show that the paradigm of the magnetic field as an atmospheric shield should be changed and that recent work on the history of Xenon in Earth's atmosphere gives an elegant explanation to its enrichment in heavier isotopes: the so-called Xenon paradox.

We have used empirical models for electric potentials and the magnetic fields in both space and on the ground to obtain maps of the height-integrated Pedersen and Hall ionospheric conductivities at high latitudes. This calculation required use of both “curl-free” and “divergence-free” components of the ionospheric currents, with the former obtained from magnetic fields that are used in a model of the field-aligned currents. The second component is from the equivalent current, usually associated with Hall currents, derived from the ground-level magnetic field. Conductances were calculated for varying combinations of the Interplanetary magnetic field (IMF) magnitude and orientation angle, as well as the dipole tilt angle. The results show that reversing the sign of the Y component of the IMF produces substantially different conductivity patterns. The Hall conductivities are largest on the dawn side in the upward, Region 2 field-aligned currents. Low electric field strengths in the Harang discontinuity lead to inconclusive results near midnight. Calculations of the Joule heating, obtained from the electric field and both components of the ionospheric current, are compared with the Poynting flux in space. The maps show some differences, while their integrated totals match to within 1%. Some of the Poynting flux that enters the polar cap is dissipated as Joule heating within the auroral ovals, where the conductivity is enhanced, confirming the Poynting Flux theorem proposed by Richmond in 2010, for the first time using realistic electric fields, ionospheric currents, and conductivity.