The Study of Magnetotail Dynamics and their Ionospheric Signatures using Magnetohydrodynamic Simulation Model: OpenGGCM

In-situ measurements in the magnetotail are sparse and limited to single points. In the ionosphere, on the other hand, there is a broad range of observations, including magnetometers, aurora imagers, and radars . Since the ionosphere is the mirror of the plasmasheet, it can be used as a monitor of the magnetotail dynamics. Thus, it is of great importance to understand the coupling process between the ionosphere and the magnetosphere in order to interpret the ionosphere and ground observations properly. In this dissertation, the global magnetohydrodynamic simulation model, OpenGGCM model, is used to investigate two of such coupling processes. The first part focuses on travel time and characteristics of waves produced in the magnetotail. These waves represent the onset of the tail reconnection and substorms in the ionosphere. To investigate signal propagation paths and signal travel times, single impulse or sinusoidal pulsations are launched at different locations of the plasmasheet, and the paths taken by the waves and the time that different waves take to reach the ionosphere is determined. We find that such waves take shorter time than previously assumed, and they generally travel faster through the lobes than through the plasma sheet. It takes approximately about 70 seconds for waves to travel from the midtail plasmasheet to the ionosphere, contrary to previous reports (~ 200 seconds) [Ferdousi and Raeder, 2016]. Other important processes that greatly contribute to convection of the tail are bursty bulk flows (BBFs) which are identifiable as aurora streamers in the ionosphere. The second part of this thesis focuses on mapping such flows from the magnetotail to the ionosphere along the magnetic filed lines for three states of the magnetotail: before the substorm onset, during substorm expansion, and during steady magnetic convection event. We find that the streamers are north-south aligned in midnight area, and they have more east-west orientation in the dawn and dusk regions. The tail and the ionosphere activity increases during SMC event compared to the pre-onset and quiet times. We also find that, the convection background in the tail controls the direction and deflection of the BBFs and orientation of the aurora streamers in the ionosphere

Varying Spacecraft Signatures of Bursty Bulk Flows and Dipolarizing Flux Bundles

Bursty Bulk Flows (BBFs) and Dipolarizing Flux Bundles (DFBs)are commonly observed in the plasma sheet during all types of geomagnetic activity, but they are more common during geomagnetically active times. The typical features are high earthward plasma speed, a rapid change of the magnetic field towards a more dipolar orientation, and a decrease in plasma density, to name a few. BBFs and DFBs are of limited width of the order of a few RE; however, their size distribution is not well constrained because of the limited data. Global MHD simulation have reproduced virtually all of the macroscopic features of BBFs and DFBs. Those simulations also show evidence of remarkable dynamic behavior such as snake-like flows, sideways motion, and DFBs bouncing back from the inner magnetosphere. Here, we present OpenGGCM simulations of BBFs and DFBs during times of varying geomagnetic activity. The simulations indicate that s/c signatures of BBFs and DFBs are not unique but depend on how the s/c encounters the structure. In particular, a s/c can encounter a DFB or BBF in a way by which some of the typical signatures do not show up. Based on the simulations we will present a classifications of possible s/c signatures and show examples that demonstrate how they come about

Subauroral Neutral Wind Driving and Its Feedback to SAPS During the 17 March 2013 Geomagnetic Storm

Subauroral Polarization Streams (SAPS) within the dusk-premidnight subauroral sector are associated with closure of region 2 field-aligned current (R2 FAC) through the low conductivity region. Although SAPS have often been studied from a magnetosphere-ionosphere (M-I) coupling perspective, recent observations suggest strong interaction also exists between SAPS and the thermosphere (T). We focuse on thermospheric wind driving and its impact on SAPS and R2 FAC during the March 17, 2013 geomagnetic storm using both observations and the physics-based RCM-CTIPe model that self-consistently couples the M-I-T system. DMSP-18 and GOCE satellite observations show that, as the storm progresses, sunward ion flows intensify and expand equatorward, and are accompanied by strengthening of subauroral neutral winds with some delay. Our model successfully reproduces time evolution and overall structure of the sunward ion drift and neutral wind. A force term analysis is performed to investigate the momentum transfer to the neutrals from the ions. Contrary to previous studies showing that Coriolis force is the main driver of neutrals, we find that the ion drag is the largest force driving westward neutral wind in the SAPS region where the ion density is low in the trough region. Furthermore, simulations with and without the neutral wind dynamo effect are compared to quantify the effect of the neutral to plasma flow. The comparison shows that the self-consistent active I-T coupling increases the R2 FAC, via the flywheel effect, and the westward ion drift equatorward of the SAPS region, via an increase in overshielding, by 20% and 40%, respectively

Unsupervised classification of simulated magnetospheric regions

In magnetospheric missions, burst-mode data sampling should be triggered in the presence of processes of scientific or operational interest. We present an unsupervised classification method for magnetospheric regions that could constitute the first step of a multistep method for the automatic identification of magnetospheric processes of interest. Our method is based on self-organizing maps (SOMs), and we test it preliminarily on data points from global magnetospheric simulations obtained with the OpenGGCM-CTIM-RCM code. The dimensionality of the data is reduced with principal component analysis before classification. The classification relies exclusively on local plasma properties at the selected data points, without information on their neighborhood or on their temporal evolution. We classify the SOM nodes into an automatically selected number of classes, and we obtain clusters that map to well-defined magnetospheric regions. We validate our classification results by plotting the classified data in the simulated space and by comparing with k-means classification. For the sake of result interpretability, we examine the SOM feature maps (magnetospheric variables are called features in the context of classification), and we use them to unlock information on the clusters. We repeat the classification experiments using different sets of features, we quantitatively compare different classification results, and we obtain insights on which magnetospheric variables make more effective features for unsupervised classification

Unsupervised classification of simulated magnetospheric regions

In magnetospheric missions, burst-mode data sampling should be triggered in the presence of processes of scientific or operational interest. We present an unsupervised classification method for magnetospheric regions that could constitute the first step of a multistep method for the automatic identification of magnetospheric processes of interest. Our method is based on self-organizing maps (SOMs), and we test it preliminarily on data points from global magnetospheric simulations obtained with the OpenGGCM-CTIM-RCM code. The dimensionality of the data is reduced with principal component analysis before classification. The classification relies exclusively on local plasma properties at the selected data points, without information on their neighborhood or on their temporal evolution. We classify the SOM nodes into an automatically selected number of classes, and we obtain clusters that map to well-defined magnetospheric regions. We validate our classification results by plotting the classified data in the simulated space and by comparing with k-means classification. For the sake of result interpretability, we examine the SOM feature maps (magnetospheric variables are called features in the context of classification), and we use them to unlock information on the clusters. We repeat the classification experiments using different sets of features, we quantitatively compare different classification results, and we obtain insights on which magnetospheric variables make more effective features for unsupervised classification

Global geomagnetic perturbation forecasting using Deep Learning

Geomagnetically Induced Currents (GICs) arise from spatio-temporal changes to Earth's magnetic field which arise from the interaction of the solar wind with Earth's magnetosphere, and drive catastrophic destruction to our technologically dependent society. Hence, computational models to forecast GICs globally with large forecast horizon, high spatial resolution and temporal cadence are of increasing importance to perform prompt necessary mitigation. Since GIC data is proprietary, the time variability of horizontal component of the magnetic field perturbation (dB/dt) is used as a proxy for GICs. In this work, we develop a fast, global dB/dt forecasting model, which forecasts 30 minutes into the future using only solar wind measurements as input. The model summarizes 2 hours of solar wind measurement using a Gated Recurrent Unit, and generates forecasts of coefficients which are folded with a spherical harmonic basis to enable global forecasts. When deployed, our model produces results in under a second, and generates global forecasts for horizontal magnetic perturbation components at 1-minute cadence. We evaluate our model across models in literature for two specific storms of 5 August 2011 and 17 March 2015, while having a self-consistent benchmark model set. Our model outperforms, or has consistent performance with state-of-the-practice high time cadence local and low time cadence global models, while also outperforming/having comparable performance with the benchmark models. Such quick inferences at high temporal cadence and arbitrary spatial resolutions may ultimately enable accurate forewarning of dB/dt for any place on Earth, resulting in precautionary measures to be taken in an informed manner.Comment: 23 pages, 8 figures, 5 tables; accepted for publication in AGU: Spaceweathe

Explosive Magnetotail Activity

Modes and manifestations of the explosive activity in the Earth's magnetotail, as well as its onset mechanisms and key pre-onset conditions are reviewed. Two mechanisms for the generation of the pre-onset current sheet are discussed, namely magnetic flux addition to the tail lobes, or other high-latitude perturbations, and magnetic flux evacuation from the near-Earth tail associated with dayside reconnection. Reconnection onset may require stretching and thinning of the sheet down to electron scales. It may also start in thicker sheets in regions with a tailward gradient of the equatorial magnetic field Bz; in this case it begins as an ideal-MHD instability followed by the generation of bursty bulk flows and dipolarization fronts. Indeed, remote sensing and global MHD modeling show the formation of tail regions with increased Bz, prone to magnetic reconnection, ballooning/interchange and flapping instabilities. While interchange instability may also develop in such thicker sheets, it may grow more slowly compared to tearing and cause secondary reconnection locally in the dawn-dusk direction. Post-onset transients include bursty flows and dipolarization fronts, micro-instabilities of lower-hybrid-drift and whistler waves, as well as damped global flux tube oscillations in the near-Earth region. They convert the stretched tail magnetic field energy into bulk plasma acceleration and collisionless heating, excitation of a broad spectrum of plasma waves, and collisional dissipation in the ionosphere. Collisionless heating involves ion reflection from fronts, Fermi, betatron as well as other, non-adiabatic, mechanisms. Ionospheric manifestations of some of these magnetotail phenomena are discussed. Explosive plasma phenomena observed in the laboratory, the solar corona and solar wind are also discussed