@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}
}
@misc{HasenbringLevenigHallneretal.2018,
  author    = {Hasenbring, Monika Ilona and Levenig, Claudia and Hallner, D. and Puschmann, Anne-Katrin and Weiffen, A. and Kleinert, Jens and Belz, J. and Schiltenwolf, Marcus and Pfeifer, A. -C. and Heidari, Jahan . and Kellmann, M. and Wippert, Pia-Maria},
  title     = {Psychosoziale Risikofaktoren f{\"u}r chronischen R{\"u}ckenschmerz in der Allgemeingesellschaft und im Leistungssport},
  series = {Manuelle Medizin},
  volume    = {56},
  journal   = {Manuelle Medizin},
  number    = {5},
  publisher = {Springer},
  address   = {Heidelberg},
  issn      = {0025-2514},
  doi       = {10.1007/s00337-018-0450-1},
  pages     = {359 -- 373},
  year      = {2018},
  abstract  = {Hintergrund Lumbale Ruckenschmerzen und ihre Neigung zur Chronifizierung stellen nicht nur in der Allgemeinbevolkerung, sondern auch im Leistungssport ein bedeutendes Gesundheitsproblem dar. Im Gegensatz zu Nichtathleten ist die Erforschung psychosozialer Risikofaktoren sowie von Screeningfragebogen, die moglichst fruhzeitig die Entwicklung chronischer Schmerzen erkennen und vorhersagen konnen, im Leistungssport noch in den Anfangen. Das vorliegende systematische Review gibt einen uberblick uber den Stand der Risikofaktorenforschung in beiden Feldern und untersucht die pradiktive Qualitat verschiedener Screeningfragebogen bei Nichtathleten. Methodik Die Literatursuche erfolgte zwischen Marz und Juni 2016 in den Datenbanken MEDLINE, PubMed und PsycINFO mit den Suchbegriffen psychosocial screening, low back pain, sciatica und prognosis, athletes. Eingeschlossen wurden prospektive Studien an Patienten mit lumbalen Ruckenschmerzen mit und ohne Ausstrahlung in das Bein, 18Jahre und mit einem Follow-up von mindestens 3-monatiger Dauer. Ergebnisse In das Review zu Screeninginstrumenten wurden 16Studien einbezogen. Alle waren an klinischen Stichproben der Allgemeingesellschaft durchgefuhrt worden. Zu den am haufigsten publizierten Screeningfragebogen gehoren der orebro Musculoskeletal Pain Screening Questionnaire (oMPSQ) mit einer zufriedenstellenden Fruherkennung der Wiederherstellung der Arbeitsfahigkeit sowie das STarT Back Screening Tool (SBT) mit guter Vorhersage schmerzbedingter Beeintrachtigung. Fur die Vorhersage kunftiger Schmerzen eignen sich die Risikoanalyse der Schmerzchronifizierung (RISC-R) und der Heidelberger Kurzfragebogen (HKF). Schlussfolgerungen Psychosoziale Risikofaktoren fur chronische Ruckenschmerzen, wie z.B. chronischer Stress, ungunstige Schmerzverarbeitung und depressive Stimmungslagen, werden zunehmend auch im Leistungssport erkannt. Screeninginstrumente, die sich in der Allgemeingesellschaft als hinreichend vorhersagestark erwiesen haben, werden aktuell im MiSpEx-Forschungsverbund auf ihre Eignung uberpruft.},
  language  = {de}
}
@phdthesis{GamezLopez2006,
  author    = {G{\´a}mez L{\´o}pez, Antonio Juan},
  title     = {Application of nonlinear dimensionality reduction to climate data for prediction},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus-10956},
  school      = {Universit{\"a}t Potsdam},
  year      = {2006},
  abstract  = {This Thesis was devoted to the study of the coupled system composed by El Ni{\~n}o/Southern Oscillation and the Annual Cycle. More precisely, the work was focused on two main problems: 1. How to separate both oscillations into an affordable model for understanding the behaviour of the whole system. 2. How to model the system in order to achieve a better understanding of the interaction, as well as to predict future states of the system. We focused our efforts in the Sea Surface Temperature equations, considering that atmospheric effects were secondary to the ocean dynamics. The results found may be summarised as follows: 1. Linear methods are not suitable for characterising the dimensionality of the sea surface temperature in the tropical Pacific Ocean. Therefore they do not help to separate the oscillations by themselves. Instead, nonlinear methods of dimensionality reduction are proven to be better in defining a lower limit for the dimensionality of the system as well as in explaining the statistical results in a more physical way [1]. In particular, Isomap, a nonlinear modification of Multidimensional Scaling methods, provides a physically appealing method of decomposing the data, as it substitutes the euclidean distances in the manifold by an approximation of the geodesic distances. We expect that this method could be successfully applied to other oscillatory extended systems and, in particular, to meteorological systems. 2. A three dimensional dynamical system could be modeled, using a backfitting algorithm, for describing the dynamics of the sea surface temperature in the tropical Pacific Ocean. We observed that, although there were few data points available, we could predict future behaviours of the coupled ENSO-Annual Cycle system with an accuracy of less than six months, although the constructed system presented several drawbacks: few data points to input in the backfitting algorithm, untrained model, lack of forcing with external data and simplification using a close system. Anyway, ensemble prediction techniques showed that the prediction skills of the three dimensional time series were as good as those found in much more complex models. This suggests that the climatological system in the tropics is mainly explained by ocean dynamics, while the atmosphere plays a secondary role in the physics of the process. Relevant predictions for short lead times can be made using a low dimensional system, despite its simplicity. The analysis of the SST data suggests that nonlinear interaction between the oscillations is small, and that noise plays a secondary role in the fundamental dynamics of the oscillations [2]. A global view of the work shows a general procedure to face modeling of climatological systems. First, we should find a suitable method of either linear or nonlinear dimensionality reduction. Then, low dimensional time series could be extracted out of the method applied. Finally, a low dimensional model could be found using a backfitting algorithm in order to predict future states of the system.},
  subject      = {Nichtlineare Dynamik},
  language  = {en}
}