@phdthesis{Smirnov2023,
  author    = {Smirnov, Artem},
  title     = {Understanding the dynamics of the near-earth space environment utilizing long-term satellite observations},
  doi       = {10.25932/publishup-61371},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-613711},
  school      = {Universit{\"a}t Potsdam},
  pages     = {xxxvi, 286},
  year      = {2023},
  abstract  = {The near-Earth space environment is a highly complex system comprised of several regions and particle populations hazardous to satellite operations. The trapped particles in the radiation belts and ring current can cause significant damage to satellites during space weather events, due to deep dielectric and surface charging. Closer to Earth is another important region, the ionosphere, which delays the propagation of radio signals and can adversely affect navigation and positioning. In response to fluctuations in solar and geomagnetic activity, both the inner-magnetospheric and ionospheric populations can undergo drastic and sudden changes within minutes to hours, which creates a challenge for predicting their behavior. Given the increasing reliance of our society on satellite technology, improving our understanding and modeling of these populations is a matter of paramount importance. In recent years, numerous spacecraft have been launched to study the dynamics of particle populations in the near-Earth space, transforming it into a data-rich environment. To extract valuable insights from the abundance of available observations, it is crucial to employ advanced modeling techniques, and machine learning methods are among the most powerful approaches available. This dissertation employs long-term satellite observations to analyze the processes that drive particle dynamics, and builds interdisciplinary links between space physics and machine learning by developing new state-of-the-art models of the inner-magnetospheric and ionospheric particle dynamics. The first aim of this thesis is to investigate the behavior of electrons in Earth's radiation belts and ring current. Using ~18 years of electron flux observations from the Global Positioning System (GPS), we developed the first machine learning model of hundreds-of-keV electron flux at Medium Earth Orbit (MEO) that is driven solely by solar wind and geomagnetic indices and does not require auxiliary flux measurements as inputs. We then proceeded to analyze the directional distributions of electrons, and for the first time, used Fourier sine series to fit electron pitch angle distributions (PADs) in Earth's inner magnetosphere. We performed a superposed epoch analysis of 129 geomagnetic storms during the Van Allen Probes era and demonstrated that electron PADs have a strong energy-dependent response to geomagnetic activity. Additionally, we showed that the solar wind dynamic pressure could be used as a good predictor of the PAD dynamics. Using the observed dependencies, we created the first PAD model with a continuous dependence on L, magnetic local time (MLT) and activity, and developed two techniques to reconstruct near-equatorial electron flux observations from low-PA data using this model. The second objective of this thesis is to develop a novel model of the topside ionosphere. To achieve this goal, we collected observations from five of the most widely used ionospheric missions and intercalibrated these data sets. This allowed us to use these data jointly for model development, validation, and comparison with other existing empirical models. We demonstrated, for the first time, that ion density observations by Swarm Langmuir Probes exhibit overestimation (up to ~40-50\%) at low and mid-latitudes on the night side, and suggested that the influence of light ions could be a potential cause of this overestimation. To develop the topside model, we used 19 years of radio occultation (RO) electron density profiles, which were fitted with a Chapman function with a linear dependence of scale height on altitude. This approximation yields 4 parameters, namely the peak density and height of the F2-layer and the slope and intercept of the linear scale height trend, which were modeled using feedforward neural networks (NNs). The model was extensively validated against both RO and in-situ observations and was found to outperform the International Reference Ionosphere (IRI) model by up to an order of magnitude. Our analysis showed that the most substantial deviations of the IRI model from the data occur at altitudes of 100-200 km above the F2-layer peak. The developed NN-based ionospheric model reproduces the effects of various physical mechanisms observed in the topside ionosphere and provides highly accurate electron density predictions. This dissertation provides an extensive study of geospace dynamics, and the main results of this work contribute to the improvement of models of plasma populations in the near-Earth space environment.},
  language  = {en}
}
@article{HaasShpritsAllisonetal.2022,
  author    = {Haas, Bernhard and Shprits, Yuri Y. and Allison, Hayley and Wutzig, Michael and Wang, Dedong},
  title     = {Which parameter controls ring current electron dynamics},
  series = {Frontiers in astronomy and space sciences},
  volume    = {9},
  journal   = {Frontiers in astronomy and space sciences},
  publisher = {Frontiers Media},
  address   = {Lausanne},
  issn      = {2296-987X},
  doi       = {10.3389/fspas.2022.911002},
  pages     = {11},
  year      = {2022},
  abstract  = {Predicting the electron population of Earth's ring current during geomagnetic storms still remains a challenging task. In this work, we investigate the sensitivity of 10 keV ring current electrons to different driving processes, parameterised by the Kp index, during several moderate and intense storms. Results are validated against measurements from the Van Allen Probes satellites. Perturbing the Kp index allows us to identify the most dominant processes for moderate and intense storms respectively. We find that during moderate storms (Kp < 6) the drift velocities mostly control the behaviour of low energy electrons, while loss from wave-particle interactions is the most critical parameter for quantifying the evolution of intense storms (Kp > 6). Perturbations of the Kp index used to drive the boundary conditions at GEO and set the plasmapause location only show a minimal effect on simulation results over a limited L range. It is further shown that the flux at L \& SIM; 3 is more sensitive to changes in the Kp index compared to higher L shells, making it a good proxy for validating the source-loss balance of a ring current model.},
  language  = {en}
}
@article{UsanovaShprits2017,
  author    = {Usanova, Maria E. and Shprits, Yuri Y.},
  title     = {Inner magnetosphere coupling},
  series = {Journal of geophysical research : Space physics},
  volume    = {122},
  journal   = {Journal of geophysical research : Space physics},
  number    = {1},
  publisher = {American Geophysical Union},
  address   = {Washington},
  issn      = {2169-9380},
  doi       = {10.1002/2016JA023614},
  pages     = {102 -- 104},
  year      = {2017},
  abstract  = {The dynamics of the inner magnetosphere is strongly governed by the interactions between different plasma populations that are coupled through large-scale electric and magnetic fields, currents, and wave-particle interactions. Inner magnetospheric plasma undergoes self-consistent interactions with global electric and magnetic fields. Waves excited in the inner magnetosphere from unstable particle distributions can provide energy exchange between different particle populations in the inner magnetosphere and affect the ring current and radiation belt dynamics. The ionosphere serves as an energy sink and feeds the magnetosphere back through the cold plasma outflow. The precipitating inner magnetospheric particles influence the ionosphere and upper atmospheric chemistry and affect climate. Satellite measurements and theoretical studies have advanced our understanding of the dynamics of various plasma populations in the inner magnetosphere. However, our knowledge of the coupling processes among the plasmasphere, ring current, radiation belts, global magnetic and electric fields, and plasma waves generated within these systems is still incomplete. This special issue incorporates extended papers presented at the Inner Magnetosphere Coupling III conference held 23-27 March 2015 in Los Angeles, California, USA, and includes modeling and observational contributions addressing interactions within different plasma populations in the inner magnetosphere (plasmasphere, ring current, and radiation belts), coupling between fields and plasma populations, as well as effects of the inner magnetosphere on the ionosphere and atmosphere.},
  language  = {en}
}