Dynamical heating and cooling are prominent features of planetary atmospheres resulting in thermospheric structures on Venus, Earth and Mars. The purpose of this study is to determine the location and amplitude of localized heating regions in the Martian thermosphere, confirm that they occur in regions of wind convergence, and to compare the observed dynamical heating with that predicted by a global thermospheric model. This investigation uses several years of data from the NASA Mars Atmosphere and Volatile EvolutioN (MAVEN) mission including observations made by the Neutral Gas and Ion Mass Spectrometer (NGIMS) as well as the Extreme Ultraviolet Monitor (EUVM). Specifically, the analysis focuses on several years of horizontal wind, temperature, and composition data. EUVM measurements provide a solar forcing context for the neutral thermosphere datasets and aid in the statistical analysis. Statistical results are compared with two versions of the Mars Global Ionosphere Thermosphere (M-GITM) global circulation model; one that that includes gravity wave parametrization and a version without gravity wave effects. Data analysis indicates that heating features exists around 2-3 and 17-18 local solar time. These locations coincide with regions of converging winds and are in better agreement with M GITM when gravity wave parametrization is included in the model. A migrating oscillation in the observed wind field also results in convergence and a density enhancement near 15 local time. While a similar oscillation is reproduced by the model, the amplitude is much lower than observed and may be a result of modeled zonal winds that are too low. 

Satellite-atmosphere interactions cause large uncertainties in low-Earth orbit determination and prediction. Thus, knowledge of and the ability to predict the space environment, most notably thermospheric mass density, are essential for operating satellites in this domain. Recent progress has been made toward supplanting the existing empirical, operational methods with physics-based data-assimilative models by accounting for the complex relationship between external drivers such as solar irradiance, Joule, and particle heating, and their response in the upper atmosphere. Simultaneously, a new era of CubeSat constellations is set to provide data with which to calibrate our upper-atmosphere models at higher spatial resolution and temporal cadence. With this in mind, we provide an initial method for converting precision orbit determination (POD) solutions from global navigation satellite system (GNSS) enabled CubeSats into timeseries of thermospheric mass density. This information is then fused with a physics-based, data-assimilative technique to provide calibrated model densities.

The High Accuracy Satellite Drag Model (HASDM) is the operational thermospheric density model used by the US Space Force (USSF) Combined Space Operations Center (CSpOC). By using real-time data assimilation, HASDM can provide density estimates with increased accuracy over empirical models. With historical HASDM density data being released publicly for the first time, we can analyze the data to identify dominant modes of variations in the upper atmosphere. As HASDM is a close relative to the Jacchia-Bowman 2008 Empirical Thermospheric Density Model (JB2008), we look at time-matched density data to better understand the models’ characteristics. This model comparison is conducted through the use of Principal Component Analysis (PCA). We then compare both datasets to the CHAllenging Minisatellie Payload (CHAMP) and Gravity Recovery and Climate Experiment (GRACE) accelerometer-derived density estimates. By looking at the principal components and PCA scores from the two models, we confirm the increased complexity of the HASDM dataset while the CHAMP and GRACE comparisons show that HASDM more closely matches the accelerometer-derived densities with mean absolute differences of 23.81% and 30.84% compared to CHAMP and GRACE-A, respectively.

The space weather research community relies heavily on thermospheric density data to understand long-term thermospheric variability, construct assimilative, empirical, and semi-empirical global atmospheric models, and validate model performance. One of the challenges in resolving accurate thermospheric density datasets from satellite orbital drag measurements is modeling appropriate physical aerodynamic drag force coefficients. The drag coefficient may change throughout the thermospheric environment due to model dependencies on composition and altitude. As such, existing drag coefficient model errors may be altitude and solar cycle dependent, with greater errors at higher altitudes around 500 km near the oxygen-to-helium transition region. This can lead to errors in orbit-derived density datasets and models. In this paper, inter-satellite density comparisons at ~500 km are evaluated to constrain drag coefficient modeling assumptions. Density consistency results indicate that drag coefficient models with incomplete energy and momentum accommodation produce the most consistent densities, while the standard diffuse modeling approach may not be appropriate at these altitudes. Models with momentum accommodation between 0.5 - 0.9 and energy accommodation between 0.83 - 0.96 may be the most appropriate at upper thermospheric altitudes. Modeling drag coefficients with diffuse gas-surface interactions could lead to errors in derived density of ~25% and in-track satellite orbit prediction uncertainty during solar maximum conditions on the order of hundreds of meters.

Martian sub-solar electron temperatures obtained below 250 km are examined using data obtained by instruments on the Mars Atmosphere Evolution Mission (MAVEN) during the three sub-solar deep dip campaigns and a one-dimensional fluid model. This analysis was done because of the uncertainty in MAVEN low electron temperature observations at low altitudes and the fact that the Level 2 temperatures reported from the MAVEN Langmuir Probe and Waves (LPW) instrument are more than 400 Kelvin above the neutral temperatures at the lowest altitudes sampled (~120 km). These electron temperatures are well above those expected before MAVEN was launched. We find that an empirical normalization parameter, neutral pressure divided by local electron heating rate, organized the electron temperature data and identified a similar altitude (~160 km) and time scale (~2,000 s) for all three deep dips. We show that MAVEN data are not consistent with a plasma characterized by electrons in thermal equilibrium with the neutral population at 100 km. Because of the lack data below 120 km and the uncertainties of the data and the cross sections used in the one dimensional fluid model above 120 km, we cannot use MAVEN observations to prove that the electron temperature converges to the neutral temperature below 100 km. However, the lack of our understanding the electron temperature altitude profile below 120 km does not impact our understanding of the role of electron temperature in determining ion escape rates because ion escape is determined by electron temperatures above 180 km.

The SET HASDM density database is available for scientific studies through a SQL database with open community access. The information in the SET HASDM density database covers the period from January 1, 2000 through December 31, 2019. Data records exist every 3 hours during solar cycles 23 and 24. The database has a grid size of 10{degree sign} × 15{degree sign} (latitude, longitude) with 25 km altitude steps between 175-825 km. A description of the source of the database, its validation, its information content, and its accessibility are provided.

Atmospheric drag describes the main perturbing force of the atmosphere on the orbital trajectories of near-Earth orbiting satellites. The ability to accurately model atmospheric drag is critical for precise satellite orbit determination and collision avoidance. Assuming we know atmospheric winds and satellite velocity, area and mass, the primary sources of uncertainty in atmospheric drag include mass density of the space environment and the spacecraft drag coefficient, CD. Historically, much of the focus has been on physically or empirically estimating mass density, while CD is treated as a fitting parameter or fixed value. Presently, CD can be physically modeled through energy and momentum exchange processes between the atmospheric gas particles and the satellite surface. However, physical CD models rely on assumptions regarding the scattering and adsorption of atmospheric particles, and these responses are driven by atmospheric composition and temperature. Modifications to these assumptions can cause CD to change by up to ~40%. The nature and magnitude of these changes also depend on the shape of the spacecraft. We can check the consistency of the CD model assumptions by comparing densities derived from satellite drag measurements and computed CD values for satellites of different shapes orbiting in the same space environment. Since all of the satellites should see the same density, offsets in the derived densities should be attributable to inconsistencies in the CD model. Adjusting the CD model scattering assumptions can improve derived density consistency among the different satellites and inform the physics behind CD modeling. In turn, these efforts will help to reduce uncertainty in CD, leading to improved atmospheric drag estimates.