The latitudinal and temporal variation of atomic oxygen (O) is opposite between the empirical model, MSIS and the whole atmosphere model, WACCM-X at 97-100 km. The [O] from WACCM-X has maxima at solstices and summer mid-high latitudes, similar to [O] from SABER. We use the densities and dynamics from WACCM-X to drive the Global Ionosphere Thermosphere Model (GITM) at its lower boundary, and compare it with the MSIS driven GITM. We focus on the differences in the modeling of the thermospheric and ionospheric semiannual oscillation (T-I SAO). Our results reveal that driving GITM with WACCM-X shifts the phase of T-I SAO to maximize around solstices. Nudging the dynamics in GITM towards WACCM-X, reduces the amplitude of the oppositely-phased SAO but does not completely correct its phase. We find that during solstices, WACCM-X driven GITM has a smaller temperature gradient between the hemispheres and weaker meridional and vertical winds in the summer hemisphere. This leads to accumulation of [O] at lower latitudes due to weaker meridional transport, resulting in solstitial maxima in global means. WACCM-X itself has the right phase of SAO in the upper thermosphere but wrong at lower altitudes. The exact mechanisms that can correct the phase of SAO in IT models while using SABER-like [O] in the MLT are currently unknown and warrant further investigation. We suggest mechanisms that can reduce the solstitial maxima in the lower thermosphere, for example, stronger interhemispheric meridional winds, stronger residual circulation, seasonal variation in eddy diffusion, and momentum from breaking gravity waves.

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 April 5 - 7, 2010 “Galaxy15” space weather event. Comparison of auroral fluxes show good agreement with observational datasets 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 conditions, providing for up to 61% of the total hemispheric power. The study also indicates a dominant role played by broadband precipitation in ionospheric electrodynamics which accounts for ~31% of the Pedersen conductance.

We present latest results from the Conductance Model for Extreme Events (CMEE) and the Magnetosphere-Ionosphere-Thermosphere (MAGNIT) Conductance Model. Both models have been integrated into the Space Weather Modeling Framework (SWMF) to couple dynamically with the BATS-R-US MHD model, the Rice Convection Model (RCM) of the ring current & the Ridley Ionosphere Model (RIM) to simulate the April 2010 “Galaxy15” Event. The model is used with three grid configurations: the low-resolution configuration currently employed by NOAA’s Space Weather Prediction Center and two additional configurations that decrease the minimum grid resolution from ¼ RE to ⅛ and 1/16 RE. In addition, the simulation is driven with and without the dynamic coupling with RCM to study the impact of the ring current’s pressure correction in the inner magnetospheric domain. Using this model setup for a Maxwellian distribution, aforementioned precipitation sources are progressively applied and compared against the DMSP SSUSI observations. Finally, data-model comparisons against AMPERE Field-Aligned Currents, geomagnetic indices & magnetometer measurements are shown, with additional comparison against the existing conductance model in RIM. Results show remarkable progress in auroral precipitation modeling & MI coupling layouts in global models.

Characterization of the thermospheric horizontal wind is an important challenge in atmospheric modeling, due to its vital role in the transport of densities and energy, associations with the diurnal tide, and interplay with vertical winds that drive changes in the thermosphere neutral composition. The mechanisms and drivers that underlie the physics of thermospheric horizontal winds remain under investigation and, to date, no comprehensive statistical study between thermospheric winds generated by a physics-based atmospheric model and those retrieved from satellite measurements has been performed. Comparisons between cross-track horizontal winds from a 10-month run of GITM and those derived from the Gravity field and steady-state Ocean Circulation Explorer (GOCE) satellite showed that GITM’s modeled horizontal winds best in the polar zone and overestimated them at midlatitudes in the equatorial ionization anomaly region. GITM’s wind response to AE was best at polar noon and worst in the midnight auroral zone, its ability to capture seasonality was best in the northern high latitudes and worst in the southern high latitudes, and GITM displayed less wind variability as a function of F10.7 than GOCE, matching it best for F10.7~150. Discrepancies in GITM’s performance may be explained by inaccurate modeling of ion drift, ion drag, and electron densities.

The eddy diffusion coefficient (Kzz) parameterizes the effects of gravity wave (GW) turbulence in the mesosphere and lower thermosphere (MLT) on the ionosphere and thermosphere (IT), and its spatial variation remains unclear. We use the Global Ionosphere Thermosphere Model (GITM) to understand the impacts of spatially varying MLT Kzz on the IT system. Using the observations from the SABER instrument, studies have observed that GW activity in the MLT exhibits latitudinal variability with seasons. We introduce similar latitudinal bands of increased Kzz at low latitudes during equinoxes and at high latitudes during solstices. The primary effect of non-uniform Kzz is in introducing spatially variability in the IT, and the net change in globally averaged thermospheric quantities is small (∼2-4%). The net effect of Kzz depends on the total area of the turbulent patch and spreads globally when low-latitude Kzz is increased. If however the turbulent conduction is turned off, changes in the IT state are more localized. When low-latitude Kzz is raised during equinoxes, a decrease in global [O], temperature, O/N2, TEC and an increase in [N2] are observed at a constant pressure level, inducing changes in meridional winds across the globe. During solstices, when high-latitude Kzz is raised, the IT state of the winter hemisphere exhibits larger decrease in O/N2, due to more effective composition change of O through vertical advection. If a larger Kzz is introduced in the summer hemisphere, an increase in O/N2 is observed because of the influence of lower background O/N2.

We model an interval of sustained northward interplanetary magnetic field, for which we have a comprehensive set of observational data. This interval is associated with the arrival of an interplanetary coronal mass ejection. The solar wind densities at the time are particularly high and the interplanetary magnetic field is primarily northward. This results in strong auroral emissions within the polar cap in a cusp spot, which we associate with lobe reconnection at the high-latitude magnetopause. We also observe areas of upwards field-aligned current within the summer Northern Hemisphere polar cap that exhibit large current magnitudes. The model is able to reproduce the spatial distribution of the field-aligned currents well, even under changing conditions in the incoming interplanetary magnetic field. Discrepancies exist between the modeled and observed current magnitudes. Notably, the winter Southern Hemisphere exhibits much lower current magnitudes overall. We also model a sharp transition of the location of magnetopause reconnection. This changes rapidly from a subsolar location at the low-latitude magnetopause under southward interplanetary magnetic field conditions, to a high-latitude lobe reconnection location when the field is northward. This occurs during a fast rotation of the IMF at the shock front of a magnetic cloud.

At altitudes below about 600 km, satellite drag is one of the most important and variable forces acting on a satellite. Neutral mass density predictions in the upper atmosphere are therefore critical for (1) designing satellites; (2) performing adjustments to stay in an intended orbit; and (3) collision avoidance maneuver planning. Density predictions have a great deal of uncertainty, including model biases and model misrepresentation of the atmospheric response to energy input. These may stem from inaccurate approximations of terms in the Navier-Stokes equations, unmodeled physics, incorrect boundary conditions, or incorrect parameterizations. Two commonly parameterized source terms are the thermal conduction and eddy diffusion. Both are critical components in the transfer of the heat in the thermosphere. Determining how well the major constituents ($N_2$, $O_2$, $O$) are as heat conductors will have effects on the temperature and mass density changes from a heat source. This work shows the effectiveness of using the retrospective cost model refinement (RCMR) technique at removing model bias caused by different sources within the Global Ionosphere Thermosphere Model (GITM). Numerical experiments, Challenging Minisatellite Payload (CHAMP) and Gravity Recovery and Climate Experiment (GRACE) data during real events are used to show that RCMR can compensate for model bias caused by both inaccurate parameterizations and drivers. RCMR is used to show that eliminating model bias before a storm allows for more accurate predictions throughout the storm.