Ionospheric Multi-Spacecraft Analysis Tools

This open access book provides a comprehensive toolbox of analysis techniques for ionospheric multi-satellite missions. The immediate need for this volume was motivated by the ongoing ESA Swarm satellite mission, but the tools that are described are general and can be used for any future ionospheric multi-satellite mission with comparable instrumentation. In addition to researching the immediate plasma environment and its coupling to other regions, such a mission aims to study the Earth’s main magnetic field and its anomalies caused by core, mantle, or crustal sources. The parameters for carrying out this kind of work are examined in these chapters. Besides currents, electric fields, and plasma convection, these parameters include ionospheric conductance, Joule heating, neutral gas densities, and neutral winds.

Complex systems methods characterizing nonlinear processes in the near-Earth electromagnetic environment: recent advances and open challenges

Learning from successful applications of methods originating in statistical mechanics, complex systems science, or information theory in one scientific field (e.g., atmospheric physics or climatology) can provide important insights or conceptual ideas for other areas (e.g., space sciences) or even stimulate new research questions and approaches. For instance, quantification and attribution of dynamical complexity in output time series of nonlinear dynamical systems is a key challenge across scientific disciplines. Especially in the field of space physics, an early and accurate detection of characteristic dissimilarity between normal and abnormal states (e.g., pre-storm activity vs. magnetic storms) has the potential to vastly improve space weather diagnosis and, consequently, the mitigation of space weather hazards. This review provides a systematic overview on existing nonlinear dynamical systems-based methodologies along with key results of their previous applications in a space physics context, which particularly illustrates how complementary modern complex systems approaches have recently shaped our understanding of nonlinear magnetospheric variability. The rising number of corresponding studies demonstrates that the multiplicity of nonlinear time series analysis methods developed during the last decades offers great potentials for uncovering relevant yet complex processes interlinking different geospace subsystems, variables and spatiotemporal scales

Ionosphere Monitoring with Remote Sensing

This book focuses on the characterization of the physical properties of the Earth’s ionosphere, contributing to unveiling the nature of several processes responsible for a plethora of space weather-related phenomena taking place in a wide range of spatial and temporal scales. This is made possible by the exploitation of a huge amount of high-quality data derived from both remote sensing and in situ facilities such as ionosondes, radars, satellites and Global Navigation Satellite Systems receivers

Radial Diffusion Models of Earth’s Outer Radiation Belt using Stochastic Parameterizations

Earth’s outer radiation belt is very dynamic and contains high-energy particles which are hazardous to spacecraft. Radial diffusion is the process by which energetic electrons undergo bulk transport and energization, driven by interactions with ultralow frequency (ULF) waves. Modelled by a Fokker-Planck equation, all of the physics to describe the strength of radial diffusion is contained in the radial diffusion coefficient, DLL, typically modelled proportionally to ULF wave power as a function of electron drift-shell (L ∗ ) and geomagnetic activity. A number of parameterizations for DLL exist but can vary by orders of magnitude. State of the art radial diffusion coefficient models therefore carry great uncertainty. All modern DLL parameterizations are deterministic and based on median ULF wave power spectral density. In this Thesis we investigate the impact on radial diffusion when DLL is modelled as an ensemble which encompasses the probabilistic distribution of ULF wave power. The underlying factors which contribute to variability in ULF wave power distributions are extensive and we concentrate on three of the largest: the variability of L ∗ with an observation’s location when mapping ULF wave power to adiabatic space, the shape of ULF wave power distributions as measured on board spacecraft as a function of L ∗ , local time and ULF wave frequency, and finally the mapping of ground-based magnetic wave power to space-based electric field power to infer a key component of DLL. We find that L ∗ varies in physical space significantly as a function of magnetic field model and geomagnetic activity, with uncertainties between magnetic field models unable to be completely mitigated. Further, shapes of space-based power approximations are either log-symmetric or log-skewed when separated into L ∗ and wave frequency, although there are characteristic differences across local time. Finally, we find that while mapping ground-based power with a stochastic ULF wave resonance width better aligns with space-based power distributions compared to the state-of-the-art analytic mapping, stochastic parameterizations of other key wave parameters are necessary to recover the full distribution. Combining the sources of variability which quantify the ULF wave power distributions into a stochastically parameterized DLL, we model an ensemble of radial diffusion and compare with a number of deterministic radial diffusion coefficients. In most cases a stochastic DLL results in more diffusion, with the spread of resulting phase space densities in the ensemble rarely enclosing those from the deterministic parameterizations. In addition, ensembles are collectively more diffusive when DLL is sampled more frequently in time and on shorter scale-lengths in L∗. Overall, this thesis demonstrates the importance of variability for impacting rates of radial transport. Future work could extend the stochastic approaches used to here to account for yet to be determined spatio-temporal ULF wave power variability

Statistical modelling of the near-Earth magnetic field in space weather

Space weather refers to electromagnetic disturbances in the near-Earth environment as a result of the Sun-Earth interaction. Severe space weather events such as magnetic storms can cause disruption to a wide range of technologies and infrastructure, including communications systems, electronic circuits and power grids. Because of its high potential impact, space weather has been included in the UK National Risk Register since 2011. Space weather monitoring and early magnetic storm detection can be used to mitigate risk in sensitive technological systems. The aim of this project is to investigate the electromagnetic disturbances in the near-Earth environment through developing statistical models that quantifies the variations and uncertainties in the near-Earth magnetic field. Data of the near-Earth magnetic field arise from in-situ satellite measurements and computer model outputs. The Cluster II mission (Escoubet et al., 2001a) has four satellites that provide in-situ measurements of the near-Earth magnetic field at time-varying locations along their trajectories. The computer model consists of an internal part that calculates the magnetic field sourced from Earth itself and an external part that estimates the magnetic field resulting from the Sun-Earth interaction. These magnetic fields, termed as the internal field and the external field, add up to the total magnetic field. Numerical estimates of the internal field and the external field are obtained respectively from the IGRF-11 model (Finlay et al., 2010) and the Tysganenko-96 (T96) model (Tsyganenko, 2013) given the times and the locations as inputs. The IGRF model outputs are invariant to space weather conditions whereas the T96 model outputs change with the input space weather parameters. The time-varying space weather parameters for T96 model include the solar wind ram pressure, the y and the z-components of the interplanetary magnetic field, and the disturbance storm time index. These parameters are the estimated time series of the solar wind conditions at the magnetopause, i.e. the boundary of the magnetosphere on the day-side, and the disturbance level at the Earth’s surface. Real-time values of the T96 model input parameters are available at hourly resolution from https://omniweb.gsfc.nasa.gov/. The overall aim of the thesis is to build spatio-temporal models that can be used to understand uncertainties and constraints leveraged from 3D mathematical models of space weather events. These spatio-temporal models can be then used to help understand the design parameters that need to be varied in building a precise and reliable sensor network. Chapter 1 provides an introduction to space weather in terms of the near-Earth magnetic field environment. Beginning with an overview of the near-Earth magnetic field environment, Chapter 2 describes the sources for generating in-situ satellite measurements and computer model outputs, namely the Cluster II mission, the IGRF model, and the T96 model. The process of sampling the magnetic field data from the different data sources and the space-time dependence in the hourly sampled magnetic field data are also included in this Chapter. Converting the space-time structure in the magnetic field data into a time series structure with a function relating the position in space to time, Chapter 3 explores the temporal variations in the sampled in-situ satellite measurements. Through a hierarchical approach, the satellite measurements are related to the computer model outputs. This chapter proposes statistical methods for dealing with the non-stationary features, temporal autocorrelation, and volatility present in the time series data. With the aim of better characterising the electromagnetic environment around the Earth, Chapter 4 develops time-series models of the near-Earth magnetic field utilising in-situ (CLUSTER) magnetic field data. Regression models linking the CLUSTER satellite observations and two physical models of the magnetic field (T96 and IGRF) are fit to each orbit in the period 2003-2013. The time series of model parameter estimates are then analysed to examine any long term patterns, variations and associations to storm indices. In addition to explaining how the two physical models calibrate with the observed satellite measurements, these statistical models capture the inherent volatility in the magnetic field, and allow us to identify other factors associated with the magnetic field variation, such as the relative position of each satellite relative to the Earth and the Sun. Mixed-effect models that include these factors are constructed for parameters estimated from the regression models for evaluating the performance of the two computer models. Following the calibration of the computer models against the satellite measurements, Chapter 5 investigates how these computer models allow us to investigate the association between the variations in near-Earth magnetic field and storms. To identify the signatures of storm onsets in different locations in the magnetosphere, change-point detection methods are considered for time series magnetic field signals generated from the computer models along various feasible satellite orbits. The detection results inform on potential sampling strategies of the near-Earth magnetic field to be predictive of storms through selecting achievable satellite orbits for placing satellite sensors and detecting changes in the time series magnetic signals. Chapter 6 provides of a summary of the main finding within this thesis, identifies some limitations of the work carried out in the main chapters, and include a discussion of future research. An Appendix provides details of coordinate transformation for converting the time and position dependent magnetic field data into an appropriate coordinate system

Applying machine learning to heliophysics problems to broaden space-weather understanding

Understanding space-weather phenomena is a growing requisite given our day-today reliance upon space-based infrastructure. This entails identifying the causal factors of space-weather phenomena, quantifying the magnitude of response of space-weather events, and jointly using this information for forecasting. Machine learning (ML), as a set of mathematical and statistical tools, has been successfully used across many fields of research, demonstrating vast potential to improve our understanding of space-weather phenomena. We apply unsupervised ML (dimension-reduction and clustering) to derive robust solar wind classifications – providing further insight into space-weather driving. Our unsupervised techniques are applied to a theoretically-motivated set of ex�tant composition variables - which are non-evolving with solar wind propagation. We demonstrate that solar-wind-speed-based classifications lose latent information regarding solar source regions. Our dimension-reduction suggests a more informative latent-space to represent streamer-belt-origin solar wind. Subsequently, we investigate the outer boundary of the outer radiation belt (OBORB). Modelling of the energetic-electrons in the outer radiation belt is crucial to the effective operation of many Earth-orbiting satellites, and the outer boundary conditions for such models are critical to accurate simulation. We ap�plied simple ML models to a dataset of electron distribution functions, testing a range of potential boundary locations – yielding an empirical identification of the quiet-time boundary location. Next, we employed Bayesian neural networks to construct parameterised, probabilistic models providing synthetic nowcasts of the electron fluxes at the boundary. These models bridge the gap between the empirically identified OBORB location and the information required by modellers to construct the outer boundary conditions. This work showcases how a broad spectrum of ML techniques can be applied to a variety of space-weather related problems. We present novel scientific results with significant implications for future studies into the solar wind and radiation belts, and ultimately, space-weather forecasting

GOCE Gradiometer Measurement Disturbances and their Modelling by Means of Ionospheric Dynamics

We examine the presence of residual non-gravitational signatures in gravitational gradients measured by GOCE Electrostatic Gravity Gradiometer. These signatures are observed over the geomagnetic poles during geomagnetically active days and contaminate the trace of the Gravitational Gradient Tensor by up to three to five times the expected noise level of the instrument (11 mE). We investigate these anomalies in the gradiometer measurements along many satellite tracks and examine possible causes by using external datasets, such as Interplanetary Electric Field observations from the ACE (Advanced Composition Explorer) and WIND spacecraft and Poynting flux (vector) estimated from Equivalent Ionospheric Currents derived from Spherical Elementary Current Systems over North America and Greenland. We show that the variations in the east-west and vertical electrical currents and Poynting flux (vector) components at the satellite position are highly correlated with the disturbances observed in the gradiometer measurements. We identify the relation between the ionospheric dynamics and disturbances and develop a data-driven model to reduce the effects of these disturbances. The results presented in this dissertation discover that the cause of the disturbances are due to intense ionospheric dynamics that are enhanced by increased solar activity which causes a dynamic drag environment. Moreover, using external information about the ionospheric dynamics, we successfully model and remove a high percentage of these disturbances for the first time in GOCE literature and promise improved data for future gravitational field models and studies of the Earth's upper atmosphere

The 1991 Marshall Space Flight Center research and technology

A compilation of 194 articles addressing research and technology activities at the Marshall Space Flight Center (MSFC) is given. Activities are divided into three major areas: advanced studies addressing transportation systems, space systems, and space science activities conducted primarily in the Program Development Directorate; research tasks carried out in the Space Science Laboratory; and technology programs hosted by a wide array of organizations at the Center. The theme for this year's report is 'Building for the Future'