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Within the fully integrated magnetosphere-ionosphere system, many electrodynamic processes interact with each other. We review recent advances in understanding three major meso-scale coupling processes within the system: the transient field-aligned currents (FACs), mid-latitude plasma convection, and auroral particle precipitation. (1) Transient FACs arise due to disturbances from either dayside or nightside magnetosphere. As the interplanetary shocks suddenly compress the dayside magnetosphere, short-lived FACs are induced at high latitudes with their polarity successively changing. Magnetotail dynamics, such as substorm injections, can also disturb the current structures, leading to the formation of substorm current wedges and ring current disruption. (2) The mid-latitude plasma convection is closely associated with electric fields in the system. Recent studies have unraveled some important features and mechanisms of subauroral fast flows. (3) Charged particles, while drifting around the Earth, often experience precipitating loss down to the upper atmosphere, enhancing the auroral conductivity. Recent studies have been devoted to developing more self-consistent geospace circulation models by including a better representation of the auroral conductance. It is expected that including these new advances in geospace circulation models could promisingly strengthen their forecasting capability in space weather applications. The remaining challenges especially in the global modeling of the circulation system are also discussed.

 

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 (a) designing satellites; (b) performing adjustments to stay in an intended orbit; and (c) 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 (N₂, O₂, and 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. Numerical experiments, Challenging Minisatellite Payload and Gravity Recovery and Climate Experiment 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.

 

A versatile suite of computational models, already used to forecast magnetic storms and potential power grid and telecommunications disruptions, is preparing to welcome a larger group of users. 

The historical record indicates the possibility of intense coronal mass ejections (CMEs). Energized particles and magnetic fields ejected by coronal mass ejections (CMEs) towards the Earth may disrupt the Earth’s magnetosphere and generate a geomagnetic storm. During a geomagnetic storm, the induced geoelectric field can drive geomagnetically-induced currents (GICs) that flow through ground-based conductors. These GICs have the potential to damage high voltage power transmission systems and cause blackouts. As part of the NSF-funded Comprehensive Hazard Analysis for Resilience to Geomagnetic Extreme Disturbances (CHARGED) project, a solar-wind-to-lithosphere numerical model of the geoelectric field is being developed. The purpose of this new tool is to drive a new generation of GIC forecasting. As a part of that work, Maxwell’s equations, finite-difference time-domain (FDTD) models of the last stage of the Sun-to-Earth propagation path is being coupled to output generated by the Block Adaptive Tree Solarwind Roe-type Upwind Scheme (BATS-R-US) magnetohydrodynamics model and the Ridley Ionosphere Model (RIM) of ionospheric dynamics. Specifically, three-dimensional (3-D) BATS-R-US and RIM-predicted ionospheric currents occurring in the lower ionosphere during and around the time of the March 17, 2015 storm are modeled in 3-D FDTD models of North America. These models start at a depth of 150 km, and they account for ionospheric currents occurring up to an altitude of 115 km. The resolution of the FDTD models is 22 km (East-West) x 11 km (North-South) x 5 km (radially), and they account for 3-D lithosphere conductivities provided by the U.S. Geological Survey. The FDTD-calculated results are compared with surface magnetic fields measured in the region by SuperMAG and INTERMAGNET magnetometers. The FDTD results are also compared with virtual magnetometer data, which calculates the perturbation of the surface magnetic field using output from the BATS-R-US magnetohydrodynamics model. Comparison plots and an analysis of the results will be provided. 

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.

 

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 1‐min 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 simulateddB/dtandΔB, resulting in substantial improvements indB/dtpredictive skill.

 

The first long‐term comparison of day‐to‐day variability (i.e., weather) in the thermospheric winds between a first‐principles model and data is presented. The definition of weather adopted here is the difference between daily observations and long‐term averages at the same UT. A year‐long run of the Global Ionosphere Thermosphere Model is evaluated against a nighttime neutral wind data set compiled from six Fabry‐Perot interferometers at middle and low latitudes. First, the temporal persistence of quiet‐time fluctuations above the background climate is evaluated, and the decorrelation time (the time lag at which the autocorrelation function drops toe⁻¹) is found to be in good agreement between the data (1.8 hr) and the model (1.9 hr). Next, comparisons between sites are made to determine the decorrelation distance (the distance at which the cross‐correlation drops toe⁻¹). Larger Fabry‐Perot interferometer networks are needed to conclusively determine the decorrelation distance, but the current data set suggests that it is ∼1,000 km. In the model the decorrelation distance is much larger, indicating that the model results contain too little spatial structure. The measured decorrelation time and distance are useful to tune assimilative models and are notably shorter than the scales expected if tidal forcing were responsible for the variability, suggesting that some other source is dominating the weather. Finally, the model‐data correlation is poor (−0.07 < ρ < 0.36), and the magnitude of the weather is underestimated in the model by 65%.