Principal component analysis as a tool to extract Sq variation from the geomagnetic field observations: conditions of applicability

In this paper, we analyze the applicability of the principal component analysis (PCA) as a tool to extract the Sq variation of the geomagnetic field. We tested different geomagnetic field components and used data measured at different levels of the solar and geomagnetic activity and during different months. Geomagnetic field variations obtained with PCA were classified as SqPCA using two types of reference series: SqIQD series calculated using geomagnetically quiet days and simulations of the ionospheric field with models. The results for the X and Y and Z components are essentially different. The Sq variation is always filtered to the first PCA mode for the Y and Z components. Thus, PCA can automatically extract the Sq variation from the observations of the Y and Z components of the geomagnetic field. For the X component, the automatic extraction of the Sq variation is not possible, and a complimentary analysis, like a comparison to a reference series, is always needed. We tested two types of reference series: the mean SqIQD and the outputs of the CM5 and DIFI3 models. Our results show that both the data-based and model-based reference series can be used but the DIFI3 model performs better. We also recommend estimating the similarity of the series not with the correlation analysis but using metrics that account for possible local stretching/compressing of the compared series, for example, the dynamic time warping (DTW) distance.Comment: 32 pages, 18 figures, 4 tables, 9 SM. Re-submitted to MethodsX in July 2022. arXiv admin note: substantial text overlap with arXiv:2104.0039

The time derivative of the geomagnetic field has a short memory

Solar eruptions and other types of space weather effects can pose a hazard to the high voltage power grids via geomagnetically induced currents (GICs). In worst cases, they can even cause large-scale power outages. GICs are a complex phenomenon, closely related to the time derivative of the geomagnetic field. However, the behavior of the time derivative is chaotic and has proven to be tricky to predict. In our study, we look at the dynamics of the geomagnetic field during active space weather. We try to characterize the magnetic field behavior, to better understand the drivers behind strong GIC events. We use geomagnetic data from the IMAGE (International Monitor for Auroral Geomagnetic Effect) magnetometer network between 1996 and 2018. The measured geomagnetic field is primarily produced by currents in the ionosphere and magnetosphere, and secondarily by currents in the conducting ground. We use the separated magnetic field in our analysis. The separation of the field means that the measured magnetic field is computationally divided into external and internal parts corresponding to the ionospheric and telluric origin, respectively. We study the yearly directional distributions of the baseline subtracted, separated horizontal geomagnetic field, Delta H, and its time derivative, d Delta H/dt. The yearly distributions do not have a clear solar cycle dependency. The internal field distributions are more scattered than the external field. There are also clear, station-specific differences in the distributions related to sharp conductivity contrasts between continental and ocean regions or to inland conductivity anomalies. One of our main findings is that the direction of d Delta H/dt has a very short "reset time", around 2 min, but Delta H does not have this kind of behavior. These results hold true even with less active space weather conditions. We conclude that this result gives insight into the time scale of ionospheric current systems, which are the primary driver behind the time derivative's behavior. It also emphasizes a very short persistence of d Delta H/dt compared to Delta H, and highlights the challenges in forecasting d Delta H/dt (and GIC).Peer reviewe

Enhanced power system resiliency to high-impact, low-frequency events with emphasis on geomagnetic disturbances

Various reliability procedures have been developed to protect the power systems against common reliability issues that threaten the grid frequently. However, these procedures are unlikely to be sufficient for high-impact low-frequency (HILF) events. This thesis proposes several techniques to enhance resiliency with respect to HILF events. In particular, we focus on cyber-physical attacks and geomagnetic disturbances (GMDs). Corrective control through generation redispatch is proposed to protect the system from cyber-physical attacks. A modification of the optimal power flow (OPF) is proposed which optimizes the system resiliency instead of the generation cost. For larger systems, the burden of solving the resilience-oriented OPF is reduced through a fast greedy algorithm which utilizes proper heuristics to narrow the search space. Moreover, an effective line switching algorithm is developed to minimize the GMD impact for large-scale power systems. The algorithm uses linear sensitivity analysis to find the best switching strategy and minimizes the GIC-saturated reactive power loss. The resiliency may be improved through power system monitoring and situational awareness. Power system data is growing rapidly with the everyday installation of different types of sensors throughout the network. In this thesis, various data analytics tools are proposed to effectively employ the sensor data for enhancing resiliency. In particular, we focus on the application of real data analysis to improve the GMD models. We identify common challenges in dealing with real data and develop effective tools to tackle them. A frequent issue with model validation is that for a real system, the parameters of the model to be validated may be inaccurate or even unavailable. To handle this, two approaches are proposed. The first approach is to develop a validation framework which is independent of the model parameters and completely relies on the measurements. Although this technique successfully handles the system uncertainties and offers a robust validation tool, it does not provide the ability to utilize the available network parameters. Sometimes, the network parameters are partially available with some degree of accuracy and it is desired to take advantage of this additional information. The second validation framework provides this capability by first modifying the model to account for the missing or inaccurate parameters. Then a suitable validation framework is built upon that model. Another common issue that is widely encountered in data analysis techniques is incomplete data when part of the required data is missing or is invalid. Examples of missing data are provided through real case studies, and advanced imputation tools are developed to handle them

Modelling storm-time TEC changes using linear and non-linear techniques

Statistical models based on empirical orthogonal functions (EOF) analysis and non-linear regression analysis (NLRA) were developed for the purpose of estimating the ionospheric total electron content (TEC) during geomagnetic storms. The well-known least squares method (LSM) and Metropolis-Hastings algorithm (MHA) were used as optimization techniques to determine the unknown coefficients of the developed analytical expressions. Artificial Neural Networks (ANNs), the International Reference Ionosphere (IRI) model, and the Multi-Instrument Data Analysis System (MIDAS) tomographic inversion algorithm were also applied to storm-time TEC modelling/reconstruction for various latitudes of the African sector and surrounding areas. This work presents some of the first statistical modeling of the mid-latitude and low-latitude ionosphere during geomagnetic storms that includes solar, geomagnetic and neutral wind drivers.Development and validation of the empirical models were based on storm-time TEC data derived from the global positioning system (GPS) measurements over ground receivers within Africa and surrounding areas. The storm criterion applied was Dst 6 −50 nT and/or Kp > 4. The performance evaluation of MIDAS compared with ANNs to reconstruct storm-time TEC over the African low- and mid-latitude regions showed that MIDAS and ANNs provide comparable results. Their respective mean absolute error (MAE) values were 4.81 and 4.18 TECU. The ANN model was, however, found to perform 24.37 % better than MIDAS at estimating storm-time TEC for low latitudes, while MIDAS is 13.44 % more accurate than ANN for the mid-latitudes. When their performances are compared with the IRI model, both MIDAS and ANN model were found to provide more accurate storm-time TEC reconstructions for the African low- and mid-latitude regions. A comparative study of the performances of EOF, NLRA, ANN, and IRI models to estimate TEC during geomagnetic storm conditions over various latitudes showed that the ANN model is about 10 %, 26 %, and 58 % more accurate than EOF, NLRA, and IRI models, respectively, while EOF was found to perform 15 %, and 44 % better than NLRA and IRI, respectively. It was further found that the NLRA model is 25 % more accurate than the IRI model. We have also investigated for the first time, the role of meridional neutral winds (from the Horizontal Wind Model) to storm-time TEC modelling in the low latitude, northern and southern hemisphere mid-latitude regions of the African sector, based on ANN models. Statistics have shown that the inclusion of the meridional wind velocity in TEC modelling during geomagnetic storms leads to percentage improvements of about 5 % for the low latitude, 10 % and 5 % for the northern and southern hemisphere mid-latitude regions, respectively. High-latitude storm-induced winds and the inter-hemispheric blows of the meridional winds from summer to winter hemisphere have been suggested to be associated with these improvements

Space Science

The all-encompassing term Space Science was coined to describe all of the various fields of research in science: Physics and astronomy, aerospace engineering and spacecraft technologies, advanced computing and radio communication systems, that are concerned with the study of the Universe, and generally means either excluding the Earth or outside of the Earth's atmosphere. This special volume on Space Science was built throughout a scientifically rigorous selection process of each contributed chapter. Its structure drives the reader into a fascinating journey starting from the surface of our planet to reach a boundary where something lurks at the edge of the observable, light-emitting Universe, presenting four Sections running over a timely review on space exploration and the role being played by newcomer nations, an overview on Earth's early evolution during its long ancient ice age, a reanalysis of some aspects of satellites and planetary dynamics, to end up with intriguing discussions on recent advances in physics of cosmic microwave background radiation and cosmology

Mesospheric nitric oxide model from SCIAMACHY data

We present an empirical model for nitric oxide (NO) in the mesosphere (≈60–90&thinsp;km) derived from SCIAMACHY (SCanning Imaging Absorption spectroMeter for Atmospheric CHartoghraphY) limb scan data. This work complements and extends the NOEM (Nitric Oxide Empirical Model; Marsh et al., 2004) and SANOMA (SMR Acquired Nitric Oxide Model Atmosphere; Kiviranta et al., 2018) empirical models in the lower thermosphere. The regression ansatz builds on the heritage of studies by Hendrickx et al. (2017) and the superposed epoch analysis by Sinnhuber et al. (2016) which estimate NO production from particle precipitation. Our model relates the daily (longitudinally) averaged NO number densities from SCIAMACHY (Bender et al., 2017b, a) as a function of geomagnetic latitude to the solar Lyman-α and the geomagnetic AE (auroral electrojet) indices. We use a non-linear regression model, incorporating a finite and seasonally varying lifetime for the geomagnetically induced NO. We estimate the parameters by finding the maximum posterior probability and calculate the parameter uncertainties using Markov chain Monte Carlo sampling. In addition to providing an estimate of the NO content in the mesosphere, the regression coefficients indicate regions where certain processes dominate.</p

Mesospheric nitric oxide model from SCIAMACHY data

We present an empirical model for nitric oxide (NO) in the mesosphere (≈60–90 km) derived from SCIAMACHY (SCanning Imaging Absorption spectroMeter for Atmospheric CHartoghraphY) limb scan data. This work complements and extends the NOEM (Nitric Oxide Empirical Model; Marsh et al., 2004) and SANOMA (SMR Acquired Nitric Oxide Model Atmosphere; Kiviranta et al., 2018) empirical models in the lower thermosphere. The regression ansatz builds on the heritage of studies by Hendrickx et al. (2017) and the superposed epoch analysis by Sinnhuber et al. (2016) which estimate NO production from particle precipitation. Our model relates the daily (longitudinally) averaged NO number densities from SCIAMACHY (Bender et al., 2017b, a) as a function of geomagnetic latitude to the solar Lyman-α and the geomagnetic AE (auroral electrojet) indices. We use a non-linear regression model, incorporating a finite and seasonally varying lifetime for the geomagnetically induced NO. We estimate the parameters by finding the maximum posterior probability and calculate the parameter uncertainties using Markov chain Monte Carlo sampling. In addition to providing an estimate of the NO content in the mesosphere, the regression coefficients indicate regions where certain processes dominate

Probabilistic Space Weather Modeling and Forecasting for the Challenge of Orbital Drag in Space Traffic Management

In the modern space age, private companies are crowding the already-congested low Earth orbit (LEO) regime with small satellite mega constellations. With over 25,000 objects larger than 10 cm already in LEO, this rapid expansion is forcing us towards the enterprise on Space Traffic Management (STM). STM is an operational effort that focuses on conjunction assessment and collision avoidance between objects. While the equations of motion for objects in orbit are well-known, there are many uncertain parameters that result in the uncertainty of an object\u27s future position. The force that the atmosphere exerts on satellite - known as drag - is the largest source of uncertainty in LEO. This is largely due to the difficulty in predicting mass density in the thermosphere - the neutral region in Earth\u27s upper atmosphere. Presently, most thermosphere models are deterministic and the treatment of uncertainty in density is highly simplified or nonexistent in operations. In this work, four probabilistic thermospheric mass density models are developed using machine learning (ML) to enable the investigation of the impact of model uncertainty on satellite position for the first time. Of these four models, two (HASDM-ML and TIE-GCM ROPE) are reduced order models based on outputs from existing thermosphere models while the other two (CHAMP-ML and MSIS-UQ) are based on in-situ thermosphere measurements. The data and model development are described, and the models\u27 capabilities, including the robustness of their uncertainty quantification (UQ) capabilities, are thoroughly assessed. Existing thermosphere models, and the ones developed here, use different space weather drivers to estimate density. In a forecasting environment, there are algorithms and models that forecast the drivers for a given period in order for a density model to make a forecast. The driver forecast models used by the United States Space Force for the HASDM system are assessed to benchmark our current capabilities. Using the error statistics for each driver, we can perturb the deterministic forecasts. This provides an avenue to use the ML thermosphere models to study the effect of driver uncertainty on satellite position, in addition to model uncertainty, for any period with available driver forecasts. Seven periods are considered with diverse space weather conditions to study the isolated effects of the two density uncertainty sources on a 72-hour satellite orbit. This provides insight into the relative importance of density uncertainty on satellite position for various space weather scenarios. This study also functions as a motivation to reconsider our current methods for STM in order to improve our capabilities and prevent future satellite collisions with increased confidence

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