@misc{Moehring2021,
  type      = {Master Thesis},
  author    = {M{\"o}hring, Jan},
  title     = {Stochastic inversion for core field modeling using satellite data},
  doi       = {10.25932/publishup-49807},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-498072},
  school      = {Universit{\"a}t Potsdam},
  pages     = {vii, 55},
  year      = {2021},
  abstract  = {Magnetfeldmodellierung mit Kugelfl{\"a}chenfunktionen basiert auf der Inversion nach hunderten bis tausenden von Parametern. Dieses hochdimensionale Problem kann grunds{\"a}tzlich als ein Optimierungsproblem formuliert werden, bei dem ein globales Minimum einer gewissen Zielfunktion berechnet werden soll. Um dieses Problem zu l{\"o}sen, gibt es eine Reihe bekannter Ans{\"a}tze, dazu z{\"a}hlen etwa gradientenbasierte Verfahren oder die Methode der kleinsten Quadrate und deren Varianten. Jede dieser Methoden hat verschiedene Vor- und Nachteile, beispielsweise bez{\"u}glich der Anwendbarkeit auf nicht-differenzierbare Funktionen oder der Laufzeit zugeh{\"o}riger Algorithmen. In dieser Arbeit verfolgen wir das Ziel, einen Algorithmus zu finden, der schneller als die etablierten Verfahren ist und sich auch f{\"u}r nichtlineare Probleme anwenden l{\"a}sst. Solche nichtlinearen Probleme treten beispielsweise bei der Absch{\"a}tzung von Euler-Winkeln oder bei der Verwendung der robusteren L_1-Norm auf. Dazu untersuchen wir die Anwendbarkeit stochastischer Optimierungsverfahren aus der CMAES-Familie auf die Modellierung des geomagnetischen Feldes des Erdkerns. Es werden sowohl die Grundlagen der Kernfeldmodellierung und deren Parametrisierung anhand einiger Beispiele aus der Literatur besprochen, als auch die theoretischen Hintergr{\"u}nde der stochastischen Verfahren gegeben. Ein CMAES-Algorithmus wurde erfolgreich angewendet, um Daten der Swarm-Satellitenmission zu invertieren und daraus das Magnetfeldmodell EvoMag abzuleiten. EvoMag zeigt gute {\"U}bereinstimmung mit etablierten Modellen, sowie mit Observatoriumsdaten aus Niemegk. Wir thematisieren einige beobachtete Schwierigkeiten und pr{\"a}sentieren und diskutieren die Ergebnisse unserer Modellierung.},
  language  = {en}
}
@phdthesis{Pick2020,
  author    = {Pick, Leonie Johanna Lisa},
  title     = {The centennial evolution of geomagnetic activity and its driving mechanisms},
  doi       = {10.25932/publishup-47275},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-472754},
  school      = {Universit{\"a}t Potsdam},
  pages     = {ix, 135},
  year      = {2020},
  abstract  = {This cumulative thesis is concerned with the evolution of geomagnetic activity since the beginning of the 20th century, that is, the time-dependent response of the geomagnetic field to solar forcing. The focus lies on the description of the magnetospheric response field at ground level, which is particularly sensitive to the ring current system, and an interpretation of its variability in terms of the solar wind driving. Thereby, this work contributes to a comprehensive understanding of long-term solar-terrestrial interactions. The common basis of the presented publications is formed by a reanalysis of vector magnetic field measurements from geomagnetic observatories located at low and middle geomagnetic latitudes. In the first two studies, new ring current targeting geomagnetic activity indices are derived, the Annual and Hourly Magnetospheric Currents indices (A/HMC). Compared to existing indices (e.g., the Dst index), they do not only extend the covered period by at least three solar cycles but also constitute a qualitative improvement concerning the absolute index level and the ~11-year solar cycle variability. The analysis of A/HMC shows that (a) the annual geomagnetic activity experiences an interval-dependent trend with an overall linear decline during 1900-2010 of ~5 \% (b) the average trend-free activity level amounts to ~28 nT (c) the solar cycle related variability shows amplitudes of ~15-45 nT (d) the activity level for geomagnetically quiet conditions (Kp<2) lies slightly below 20 nT. The plausibility of the last three points is ensured by comparison to independent estimations either based on magnetic field measurements from LEO satellite missions (since the 1990s) or the modeling of geomagnetic activity from solar wind input (since the 1960s). An independent validation of the longterm trend is problematic mainly because the sensitivity of the locally measured geomagnetic activity depends on geomagnetic latitude. Consequently, A/HMC is neither directly comparable to global geomagnetic activity indices (e.g., the aa index) nor to the partly reconstructed open solar magnetic flux, which requires a homogeneous response of the ground-based measurements to the interplanetary magnetic field and the solar wind speed. The last study combines a consistent, HMC-based identification of geomagnetic storms from 1930-2015 with an analysis of the corresponding spatial (magnetic local time-dependent) disturbance patterns. Amongst others, the disturbances at dawn and dusk, particularly their evolution during the storm recovery phases, are shown to be indicative of the solar wind driving structure (Interplanetary Coronal Mass Ejections vs. Stream or Co-rotating Interaction Regions), which enables a backward-prediction of the storm driver classes. The results indicate that ICME-driven geomagnetic storms have decreased since 1930 which is consistent with the concurrent decrease of HMC. Out of the collection of compiled follow-up studies the inclusion of measurements from high-latitude geomagnetic observatories into the third study's framework seems most promising at this point.},
  language  = {en}
}
@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}
}
@phdthesis{Aseev2020,
  author    = {Aseev, Nikita},
  title     = {Modeling and understanding dynamics of charged particles in the Earth's inner magnetosphere},
  doi       = {10.25932/publishup-47921},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-479211},
  school      = {Universit{\"a}t Potsdam},
  pages     = {xxii, 154},
  year      = {2020},
  abstract  = {The Earth's inner magnetosphere is a very dynamic system, mostly driven by the external solar wind forcing exerted upon the magnetic field of our planet. Disturbances in the solar wind, such as coronal mass ejections and co-rotating interaction regions, cause geomagnetic storms, which lead to prominent changes in charged particle populations of the inner magnetosphere - the plasmasphere, ring current, and radiation belts. Satellites operating in the regions of elevated energetic and relativistic electron fluxes can be damaged by deep dielectric or surface charging during severe space weather events. Predicting the dynamics of the charged particles and mitigating their effects on the infrastructure is of particular importance, due to our increasing reliance on space technologies. The dynamics of particles in the plasmasphere, ring current, and radiation belts are strongly coupled by means of collisions and collisionless interactions with electromagnetic fields induced by the motion of charged particles. Multidimensional numerical models simplify the treatment of transport, acceleration, and loss processes of these particles, and allow us to predict how the near-Earth space environment responds to solar storms. The models inevitably rely on a number of simplifications and assumptions that affect model accuracy and complicate the interpretation of the results. In this dissertation, we quantify the processes that control electron dynamics in the inner magnetosphere, paying particular attention to the uncertainties of the employed numerical codes and tools. We use a set of convenient analytical solutions for advection and diffusion equations to test the accuracy and stability of the four-dimensional Versatile Electron Radiation Belt (VERB-4D) code. We show that numerical schemes implemented in the code converge to the analytical solutions and that the VERB-4D code demonstrates stable behavior independent of the assumed time step. The order of the numerical scheme for the convection equation is demonstrated to affect results of ring current and radiation belt simulations, and it is crucially important to use high-order numerical schemes to decrease numerical errors in the model. Using the thoroughly tested VERB-4D code, we model the dynamics of the ring current electrons during the 17 March 2013 storm. The discrepancies between the model and observations above 4.5 Earth's radii can be explained by uncertainties in the outer boundary conditions. Simulation results indicate that the electrons were transported from the geostationary orbit towards the Earth by the global-scale electric and magnetic fields. We investigate how simulation results depend on the input models and parameters. The model is shown to be particularly sensitive to the global electric field and electron lifetimes below 4.5 Earth's radii. The effects of radial diffusion and subauroral polarization streams are also quantified. We developed a data-assimilative code that blends together a convection model of energetic electron transport and loss and Van Allen Probes satellite data by means of the Kalman filter. We show that the Kalman filter can correct model uncertainties in the convection electric field, electron lifetimes, and boundary conditions. It is also demonstrated how the innovation vector - the difference between observations and model prediction - can be used to identify physical processes missing in the model of energetic electron dynamics. We computed radial profiles of phase space density of ultrarelativistic electrons, using Van Allen Probes measurements. We analyze the shape of the profiles during geomagnetically quiet and disturbed times and show that the formation of new local minimums in the radial profiles coincides with the ground observations of electromagnetic ion-cyclotron (EMIC) waves. This correlation indicates that EMIC waves are responsible for the loss of ultrarelativistic electrons from the heart of the outer radiation belt into the Earth's atmosphere.},
  language  = {en}
}
@phdthesis{Zhelavskaya2020,
  author    = {Zhelavskaya, Irina},
  title     = {Modeling of the Plasmasphere Dynamics},
  doi       = {10.25932/publishup-48243},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-482433},
  school      = {Universit{\"a}t Potsdam},
  pages     = {xlii, 256},
  year      = {2020},
  abstract  = {The plasmasphere is a dynamic region of cold, dense plasma surrounding the Earth. Its shape and size are highly susceptible to variations in solar and geomagnetic conditions. Having an accurate model of plasma density in the plasmasphere is important for GNSS navigation and for predicting hazardous effects of radiation in space on spacecraft. The distribution of cold plasma and its dynamic dependence on solar wind and geomagnetic conditions remain, however, poorly quantified. Existing empirical models of plasma density tend to be oversimplified as they are based on statistical averages over static parameters. Understanding the global dynamics of the plasmasphere using observations from space remains a challenge, as existing density measurements are sparse and limited to locations where satellites can provide in-situ observations. In this dissertation, we demonstrate how such sparse electron density measurements can be used to reconstruct the global electron density distribution in the plasmasphere and capture its dynamic dependence on solar wind and geomagnetic conditions. First, we develop an automated algorithm to determine the electron density from in-situ measurements of the electric field on the Van Allen Probes spacecraft. In particular, we design a neural network to infer the upper hybrid resonance frequency from the dynamic spectrograms obtained with the Electric and Magnetic Field Instrument Suite and Integrated Science (EMFISIS) instrumentation suite, which is then used to calculate the electron number density. The developed Neural-network-based Upper hybrid Resonance Determination (NURD) algorithm is applied to more than four years of EMFISIS measurements to produce the publicly available electron density data set. We utilize the obtained electron density data set to develop a new global model of plasma density by employing a neural network-based modeling approach. In addition to the location, the model takes the time history of geomagnetic indices and location as inputs, and produces electron density in the equatorial plane as an output. It is extensively validated using in-situ density measurements from the Van Allen Probes mission, and also by comparing the predicted global evolution of the plasmasphere with the global IMAGE EUV images of He+ distribution. The model successfully reproduces erosion of the plasmasphere on the night side as well as plume formation and evolution, and agrees well with data. The performance of neural networks strongly depends on the availability of training data, which is limited during intervals of high geomagnetic activity. In order to provide reliable density predictions during such intervals, we can employ physics-based modeling. We develop a new approach for optimally combining the neural network- and physics-based models of the plasmasphere by means of data assimilation. The developed approach utilizes advantages of both neural network- and physics-based modeling and produces reliable global plasma density reconstructions for quiet, disturbed, and extreme geomagnetic conditions. Finally, we extend the developed machine learning-based tools and apply them to another important problem in the field of space weather, the prediction of the geomagnetic index Kp. The Kp index is one of the most widely used indicators for space weather alerts and serves as input to various models, such as for the thermosphere, the radiation belts and the plasmasphere. It is therefore crucial to predict the Kp index accurately. Previous work in this area has mostly employed artificial neural networks to nowcast and make short-term predictions of Kp, basing their inferences on the recent history of Kp and solar wind measurements at L1. We analyze how the performance of neural networks compares to other machine learning algorithms for nowcasting and forecasting Kp for up to 12 hours ahead. Additionally, we investigate several machine learning and information theory methods for selecting the optimal inputs to a predictive model of Kp. The developed tools for feature selection can also be applied to other problems in space physics in order to reduce the input dimensionality and identify the most important drivers. Research outlined in this dissertation clearly demonstrates that machine learning tools can be used to develop empirical models from sparse data and also can be used to understand the underlying physical processes. Combining machine learning, physics-based modeling and data assimilation allows us to develop novel methods benefiting from these different approaches.},
  language  = {en}
}