Accurate modeling of thermospheric impact of high solar and geomagnetic activities is crucial for safeguarding our space-based infrastructure. However, current modeling capabilities are still unable to accurately predict thermospheric density, which is a key parameter for calculating satellite drags. In this paper, we performed global sensitivity analysis (GSA) for nitric oxide (NO)-related chemical reaction rates using the Global Ionosphere Thermosphere Model (GITM) during solar maximum and solar minimum conditions. We have performed GSA and uncertainty quantification (UQ) for the first time in GITM. GITM is a computationally expensive model; therefore, we employed a Gaussian process (GP)-based surrogate model to approximate the thermospheric states of GITM and inexpensively generate samples for Monte-Carlo-based Sobol analysis. We computed first-order (main effect) and total-order (total effect) Sobol’ sensitivity indices to quantify how the uncertainty associated with NO-related chemical reaction rate coefficients in GITM influences the variance of the NO density, NO cooling rate, temperature, and neutral density. Our study identified the most influential reaction rates the uncertainty of which contribute to the most uncertainty in estimating thermospheric states in GITM and provided important information for UQ within GITM to accurately estimate the thermospheric density. Our findings suggest that reducing the uncertainty in the reaction rates, particularly for RR43 ($NO + hv ightarrow N ({}^4 S) + O$), RR44 ($N({}^4 S) + O_2 ightarrow NO + O$), and RR5 ($N_2^+ + O ightarrow NO^+ ({}^2 D) + N ({}^2 D)$), should be prioritized to fix GITM’s response to variations in F10.7 solar flux.

We present simulation results of the vertical structure of Large Scale Traveling Ionospheric Disturbances (LSTIDs) during synthetic geomagnetic storms. These data are produced using a one-way coupled SAMI3/GITM model, where GITM (Global Ionosphere Thermosphere Model) provides thermospheric information to SAMI3 (SAMI3 is Another Model of the Ionosphere), allowing the production and propagation of LSTIDs. We show simulation results which demonstrate that the traveling atmospheric disturbances (TADs) generated in GITM propagate to the topside ionosphere in SAMI3 as LSTIDs. The speed and wavelength (900 m/s and 10º-20º latitude) are consistent with LSTID observations in storms of similar magnitudes. We demonstrate the LSTIDs reach altitudes beyond the topside ionosphere with amplitudes of <5% over background which will facilitate the use of plasma measurements from the topside ionosphere to supplement measurements from GNSS in the study of TIDs. Additionally, we demonstrate the dependence of the characteristics of these TADs and TIDs on longitude.

It is well known that the primary solar wind energy dissipation mechanism in the Earth’s upper atmosphere is Joule heating. Two of the most commonly used physics-based Global Circulation Models (GCM) of the Earth’s upper atmosphere are the Global Ionosphere/ Thermosphere Model (GITM) and the Thermosphere-Ionosphere-Electrodynamics General Circulation Model (TIE-GCM). At the same time, a number of empirical formulations have been derived to provide estimates of Joule heating rates based on indices of solar and geomagnetic activity. In this paper, a comparison of the evolution of the globally-integrated Joule heating rates between the two GCMs and various empirical formulations is performed during the solar storm of 17 March 2015. It is found that all empirical formulations on average underestimate Joule heating rates compared to both GITM and TIE-GCM, whereas TIE-GCM calculates lower heating rates compared to GITM. It is also found that Joule heating is primarily correlated with the auroral electrojet in GITM, whereas Joule heating in TIE-GCM is correlated better with the Dst index and with prolonged southward turnings of the Interplanetary Magnetic Field component, Bz. By calculating the heating rates separately in the northern and southern hemispheres it is found that in GITM higher Joule heating rates are observed in the northern hemisphere, whereas in TIE-GCM higher Joule heating rates are observed in the southern hemisphere. The differences and similarities between the two global circulation models and the various empirical models are outlined and discussed.

A document by Daniel Brandt. Click on the document to view its contents.

This paper introduces the new Venus Global Ionosphere Thermosphere Model (V-GITM) which incorporates the terrestrial GITM framework with Venus-specific parameters, ion-neutral chemistry, and radiative processes in order to simulate some of the observable features regarding the temperatures, composition, and dynamical structure of the Venus atmosphere from 70 km to 170 km. Atmospheric processes are included based upon formulations used in previous Venus GCMs, several augmentations exist, such as improved horizontal and vertical momentum equations and tracking exothermic chemistry. Explicitly solving the momentum equations allows for the exploration of its dynamical effects on the day-night structure. In addition, V-GITM’s use of exothermic chemistry instead of a strong heating efficiency accounts for the heating due to the solar EUV while producing comparable temperatures to empirical models. V-GITM neutral temperatures and neutral-ion densities are compared to upper atmosphere measurements obtained from Pioneer Venus and Venus Express. V-GITM demonstrates asymmetric horizontal wind velocities through the cloud tops to the middle thermosphere and explains the mechanisms for sustaining the wind structure. In addition, V-GITM produces reasonable dayside ion densities and shows that the neutral winds can carry the ions to the nightside via an experiment advecting O$_2^+$.

Yulu Peng1, Shasha Zou1, Zihan Wang2, Xiantong Wang1, Aaron Ridley1, Grace Kwon1 and Mary Smirnova11Department of Climate and Space Sciences and Engineering, University of Michigan, Ann Arbor, MI, USA2Department of Physics, University of Texas at Arlington, Arlington, TX, USACorresponding author: Yulu Peng (yulupeng@umich.edu)

Assessing space weather modeling capability is a key element in improving existing models and developing new ones. In order to track improvement of the models and investigate impacts of forcing, from the lower atmosphere below and from the magnetosphere above, on the performance of ionosphere-thermosphere models, we expand our previous assessment for 2013 March storm event [Shim et al., 2018]. In this study, we evaluate new simulations from upgraded models (Coupled Thermosphere Ionosphere Plasmasphere Electrodynamics (CTIPe) model version 4.1 and Global Ionosphere Thermosphere Model (GITM) version 21.11) and from NCAR Whole Atmosphere Community Climate Model with thermosphere and ionosphere extension (WACCM-X) version 2.2 including 8 simulations in the previous study. A simulation of NCAR Thermosphere-Ionosphere-Electrodynamics General Circulation Model version 2 (TIE-GCM 2) is also included for comparison with WACCM-X. TEC and foF2 changes from quiet-time background are considered to evaluate the model performance on the storm impacts. For evaluation, we employ 4 skill scores: Correlation coefficient (CC), root-mean square error (RMSE), ratio of the modeled to observed maximum percentage changes (Yield), and timing error(TE). It is found that the models tend to underestimate the storm-time enhancements of foF2 (F2-layer critical frequency) and TEC (Total Electron Content) and to predict foF2 and/or TEC better in the North America but worse in the Southern Hemisphere. The ensemble simulation for TEC is comparable to results from a data assimilation model (Utah State University-Global Assimilation of Ionospheric Measurement (USU-GAIM)) with differences in skill score less than 3% and 6% for CC and RMSE, respectively.

Ionospheric conductance is a crucial factor in regulating the closure of magnetospheric field-aligned currents through the ionosphere as Hall and Pedersen currents. Despite its importance in predictive investigations of the magnetosphere - ionosphere coupling, the estimation of ionospheric conductance in the auroral region is precarious in most global first-principles based models. This impreciseness in estimating the auroral conductance impedes both our understanding and predictive capabilities of the magnetosphere-ionosphere system during extreme space weather events. In this article, we address this concern, with the development of an advanced Conductance Model for Extreme Events (CMEE) that estimates the auroral conductance from field aligned current values. CMEE has been developed using nonlinear regression over a year’s worth of one-minute resolution output from assimilative maps, specifically including times of extreme driving of the solar wind-magnetosphere-ionosphere system. The model also includes provisions to enhance the conductance in the aurora using additional adjustments to refine the auroral oval. CMEE has been incorporated within the Ridley Ionosphere Model (RIM) of the Space Weather Modeling Framework (SWMF) for usage in space weather simulations. This paper compares performance of CMEE against the existing conductance model in RIM, through a validation process for six space weather events. The performance analysis indicates overall improvement in the ionospheric feedback to ground-based space weather forecasts. Specifically, the model is able to improve the prediction of ionospheric currents which impact the simulated dB/dt and ΔB, resulting in substantial improvements in dB/dt predictive skill.

Despite significant developments in global modeling, the determination of ionospheric conductance in the auroral region remains a challenge in the space science community. With advances in adiabatic kinetic theory and numerical couplings between global magnetohydrodynamic models and ring current models, the dynamic prediction of individual sources of auroral conductance have improved significantly. However, the individual impact of these sources on the total conductance and ionospheric electrodynamics remains understudied. In this study, we have investigated individual contributions from four types of auroral precipitation - electron & ion diffuse, monoenergetic & Alfven wave-driven - on ionospheric electrodynamics using a novel modeling setup. The setup encompasses recent developments within the University of Michigan’s Space Weather Modeling Framework (SWMF), specifically through the use of the MAGNetosphere - Ionosphere - Thermosphere auroral precipitation model and dynamic two-way coupling with the Global Ionosphere-Thermosphere Model. This modeling setup replaces the empirical idealizations traditionally used to estimate conductance in SWMF, with a physics-based approach capable of resolving 3-D high-resolution mesoscale features in the ionosphere-thermosphere system. Using this setup, we have simulated an idealized case of southward Bz 5nT & the April 5-7 “Galaxy15” Event. Contributions from each source of precipitation are compared against the OVATION Prime Model, while auroral patterns and hemispheric power during Galaxy15 are compared against observations from DMSP SSUSI and the AE-based FTA model. Additionally, comparison of field aligned currents (FACs) and potential patterns are also conducted against AMPERE, SuperDARN & AMIE estimations. Progressively applying conductance sources, we find that diffuse contributions from ions and electrons provide ~75% of the total energy flux and Hall conductance in the auroral region. Despite this, we find that Region 2 FACs increase by ~11% & cross-polar potential reduces by ~8.5% with the addition of monoenergetic and broadband sources, compared to <1% change in potential for diffuse additions to the conductance. Results also indicate a dominant impact of ring current on the strength and morphology of the precipitation pattern.

Estimation of the ionospheric conductance is a crucial step in coupling the magnetosphere & ionosphere (MI). Since the high-latitude ionosphere closes magnetospheric currents, conductance in this region is pivotal to examine & predict MI coupling dynamics, especially during extreme events. In spite of its importance, only recently have impacts of key magnetospheric & ionospheric contributors affecting auroral conductance (e.g., particle distribution, ring current, anomalous heating, etc.) been explored using global models. Addressing these uncertainties require new capabilities in global magnetosphere - ionosphere - thermosphere models, in order to self-consistently obtain the multi-scale, dynamic sources of conductance. This work presents the new MAGNetosphere - Ionosphere - Thermosphere (MAGNIT) auroral conductance model, which delivers the requisite capabilities to fully explore the sources of conductance & their impacts. MAGNIT has been integrated into the Space Weather Modeling Framework to couple dynamically with the BATSRUS magnetohydrodynamic (MHD) model, the Rice Convection Model (RCM) of the ring current, the Ridley Ionosphere Model (RIM) & the Global Ionosphere Thermosphere Model (GITM). This new model is used to address the precise impact of diverse conductance contributors during geomagnetic events. First, the coupled MHD-RIM-MAGNIT model is used to establish diffuse & discrete precipitation using kinetic theory. The key innovation is to include the capability of using distinct particle distribution functions (PDF) in a global model: in this study, we explore precipitation fluxes estimated using isotropic Maxwellian & Kappa PDFs. RCM is then included to investigate the effect of the ring current. Precipitating flux computed on closed field lines by RCM is compared against MAGNIT results, to show that expected results are alike. Lastly, GITM is coupled to study the impact of the ionosphere thermosphere system. Using the MAGNIT model, aforementioned conductance sources are progressively applied in idealized simulations & compared against the OVATION Prime Model. Finally, data-model comparisons against SSUSI, AMPERE & SuperMAG measurements during the March 17, 2013 Storm are shown. Results show remarkable progress of conductance modeling & MI coupling layouts in global models.

While flagship missions such as CHAMP and GOCE have shown us with accelerometer measurements that the thermospheric density in Low Earth Orbit (LEO) can increase by more than 200% during enhanced geomagnetic activity, current empirical models, such as those of the MSISE and Jacchia families, as well as the Drag Temperature Model, fail to reproduce this behavior, limiting the ability to perform orbit prediction and space situational awareness. Several methods have been employed to address this dilemma. One is the High-Accuracy Satellite Drag Model (HASDM), which uses its Dynamic Calibration Atmosphere to employ differential correction across 75 spherical calibration satellites to generate correction parameters to the density that are related to 10.7 cm solar radio flux and ap (Storz et al. 2005). Doornbos et al. 2008 has implemented a method that estimates height-dependent scale factors to the densities from empirical models with respect to densities directly derived from two- line element sets (TLEs). HASDM’s reliance on Space Surveillance Network observations limit its accessibility and detail, and Doornbos’ methods are limited by the fact that TLEs are mean elements; densities derived from them are subject to errors due to smoothing over an entire orbit. In addition, the method of deriving densities from TLEs was initially done only to provide inputs to the SGP4 orbital propagator, which was initially developed without consideration of solar radiation pressure on the trajectory of modeled spacecraft. We present a method to generate new model densities during geomagnetic storms by using an in-house orbital propagator, the Spacecraft Orbital Characterization Kit (SpOCK). This method estimates and applies scale factors to F10.7 and a p to minimize orbit propagation errors with TLEs. The method is tested on a variety of satellites, including CHAMP, GOCE, and the CubeSats of the QB50 and FLOCK constellations. This method proposes to grant insight into storm-time thermospheric density enhancement by modeling the effects of storms on the drag of numerous LEO spacecraft, increasing our understanding of thermospheric dynamics and granting us improved tools for space traffic management and thermospheric research.

Empirical models of the thermospheric neutral density are routinely used by mission planners and systems engineers to perform orbit maintenance, collision avoidance, and estimate time and location of re-entry for spacecraft. These models have characteristic errors in neutral density below 10% during geomagnetic quiet time, but perform worse during intense geomagnetic activity, being unable to reproduce the significant increases in the neutral density that are observed during geomagnetic storms. Underestimation of the density during these conditions translates to errors in orbit propagation that reduce the accuracy of any resulting orbit predictions. These drawbacks directly translate into safety risks for astronauts and orbiting spacecraft, but also limit our understanding of the physics of neutral density enhancements. Numerous CubeSats with publicly available ephemeris in the form of two-line element (TLEs) sets orbit in this region. We present the Multifaceted Optimization Algorithm (MOA), a method to estimate the neutral density by minimizing the error between a modeled trajectory and a set of TLEs. Specifically, the algorithm estimates corrections to the inputs of the NRLMSISE-00 empirical density model, and applies those corrections along-track the SWARM spacecraft orbits. This results in orbit-averaged empirical densities below 10% error in magnitude, compared to errors in excess of 25\% for uncalibrated NLRMSISE-00.