Modeling Radiation Belt Electrons With Information Theory Informed Neural Networks

An empirical model of radiation belt relativistic electrons (μ = 560–875 MeV G−1 and I = 0.088–0.14 RE G0.5) with average energy ∼1.3 MeV is developed. The model inputs solar wind parameters (velocity, density, interplanetary magnetic field (IMF) |B|, Bz, and By), magnetospheric state parameters (SYM-H and AL), and L*. The model outputs the radiation belt electron phase space density (PSD). The model is operational from L* = 3 to 6.5. The model is constructed with neural networks assisted by information theory. Information theory is used to select the most effective and relevant solar wind and magnetospheric input parameters plus their lag times based on their information transfer to the PSD. Based on the test set, the model prediction efficiency (PE) increases with increasing L*, ranging from −0.043 at L* = 3 to 0.76 at L* = 6.5. The model PE is near 0 at L* = 3–4 because at this L* range, the solar wind and magnetospheric parameters transfer little information to the PSD. Using solar wind observations at L1 and magnetospheric index (AL and SYM-H) models solely driven by solar wind, the radiation belt model can be used to forecast PSD 30–60 min ahead. This baseline model can potentially complement a class of empirical models that input data from low earth orbit (LEO)

The Rule of Artificial Neural Network Algorithm in Geomagnetic Storms Prediction

While relativistic electrons can completely destroy a spacecraft when the solar wind-magnetospheric interactions are enhanced, the Dst index is considered to be an indicator of any geomagnetic storm. The more negative the Dst index values, the stronger the magnetic storm.   Every relativistic electron event was associated with a magnetic storm, but, magnetic storms could occur without appreciable enhancement of the relativistic electron fluxes. The problem thus arises, which one should be predicted:  the Dst index or relativistic electron enhancements (REE), in order to be more logic? and which is more effective for prediction: the use of statistical relationships or Artificial Neural Networks? Reproduction (or simulation) of the Dst index using a neural network algorithm would solve the problem. An Artificial Neural Network Algorithm was adopted in the present study for the reproduction of the Dst index of geomagnetic storms having the training concept “Train to Gain” in mind.  The ANN was well trained using a data set of 37 storms of different intensities as input to the network. A well trained ANN would yield an extremely good correlation between the measured Dst and the predicted Dst. The applied ANN algorithm in the present study shows an excellent performance. About 97% of the Dst have been reproduced, at least, for both the main and recovery phases. Efficient forecast of the oncoming relativistic electron flux enhancements (REE) can thus - under certain conditions - be issued. Keywords: Geomagnetic storms, Geosynchronous orbit, Solar cycle-23, Dst index, Relativistic Electron Enhancement, Artificial Neural Network

Forecasting geomagnetic activity indices using the Boyle index through artificial neural networks

Adverse space weather conditions affect various sectors making both human lives and technologies highly susceptible. This dissertation introduces a new set of algorithms suitable for short term space weather forecasts with an enhanced lead-time and better accuracy in predicting Kp, Dst and the AE index over some leading models. Kp is a 3-hour averaged global geomagnetic activity index good for midlatitude regions. The Dst index, an hourly index calculated using four ground based magnetic field measurements near the equator, measures the energy of the Earth's ring current. The Auroral Electrojet indices or AE indices are hourly indices used to characterize the global geomagnetic activity in the auroral zone. Our algorithms can predict these indices purely from the solar wind data with lead times up to 6 hours. We have trained and tested an ANN (Artificial Neural Network) over a complete solar cycle to serve this purpose. Over the last couple of decades, ANNs have been successful for temporal prediction problems amongst other advanced non-linear techniques. Our ANN-based algorithms receive near-real-time inputs either from ACE (Advanced Composition Explorer), located at L1, and a handful of ground-based magnetometers or only from ACE. The Boyle potential, phi = 10-4 &parl0;vkm/sec&parr0;2+ 11.7BnT sin3 (theta/2) kV, or the Boyle Index (BI) is an empirically-derived formula that approximates the Earth's polar cap potential and is easily derivable in real time using the solar wind data from ACE. The logarithms of both 3-hour and 1-hour averages of the Boyle Index correlate well with the subsequent Kp, Dst and AE: Kp = 8.93 log 10<BI> - 12.55. Dst = 0.355<BI> - 6.48, and AE = 5.87<BI> - 83.46. Inputs to our ANN models have greatly benefitted from the BI and its proven record as a forecasting parameter since its initiation in October, 2003. A preconditioning event tunes the magnetosphere to a specific state before an impending geomagnetic storm. The neural net not only improves the predictions but also helps the prediction by capturing the influence of preconditioning. Two of our models have been running in near-real-time forecast mode already, and the BI and Kp predictions can be obtained from http://space.rice.edu/ISTP/wind.html

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

The Challenge of Machine Learning in Space Weather Nowcasting and Forecasting

The numerous recent breakthroughs in machine learning (ML) make imperative to carefully ponder how the scientific community can benefit from a technology that, although not necessarily new, is today living its golden age. This Grand Challenge review paper is focused on the present and future role of machine learning in space weather. The purpose is twofold. On one hand, we will discuss previous works that use ML for space weather forecasting, focusing in particular on the few areas that have seen most activity: the forecasting of geomagnetic indices, of relativistic electrons at geosynchronous orbits, of solar flares occurrence, of coronal mass ejection propagation time, and of solar wind speed. On the other hand, this paper serves as a gentle introduction to the field of machine learning tailored to the space weather community and as a pointer to a number of open challenges that we believe the community should undertake in the next decade. The recurring themes throughout the review are the need to shift our forecasting paradigm to a probabilistic approach focused on the reliable assessment of uncertainties, and the combination of physics-based and machine learning approaches, known as gray-box.Comment: under revie

GSFC Heliophysics Science Division 2008 Science Highlights

This report is intended to record and communicate to our colleagues, stakeholders, and the public at large about heliophysics scientific and flight program achievements and milestones for 2008, for which NASA Goddard Space Flight Center's Heliophysics Science Division (HSD) made important contributions. HSD comprises approximately 261 scientists, technologists, and administrative personnel dedicated to the goal of advancing our knowledge and understanding of the Sun and the wide variety of domains that its variability influences. Our activities include Lead science investigations involving flight hardware, theory, and data analysis and modeling that will answer the strategic questions posed in the Heliophysics Roadmap; Lead the development of new solar and space physics mission concepts and support their implementation as Project Scientists; Provide access to measurements from the Heliophysics Great Observatory through our Science Information Systems, and Communicate science results to the public and inspire the next generation of scientists and explorers

Probabilistic forecasts of storm sudden commencements from interplanetary shocks using machine learning

In this study we investigate the ability of several different machine learning models to provide probabilistic predictions as to whether interplanetary shocks observed upstream of the Earth at L1 will lead to immediate (Sudden Commencements, SCs) or longer lasting magnetospheric activity (Storm Sudden Commencements, SSCs). Four models are tested including linear (Logistic Regression), non‐linear (Naive Bayes and Gaussian Process) and ensemble (Random Forest) models, and are shown to provide skillful and reliable forecasts of SCs with Brier Skill Scores (BSSs) of ~ 0:3 and ROC scores > 0:8. The most powerful predictive parameter is found to be the range in the interplanetary magnetic field. The models also produce skillful forecasts of SSCs, though with less reliability than was found for SCs. The BSSs and ROC scores returned are ~0:21 and 0.82 respectively. The most important parameter for these predictions was found to be the minimum observed BZ. The simple parameterization of the shock was tested by including additional features related to magnetospheric indices and their changes during shock impact, resulting in moderate increases in reliability. Several parameters, such as velocity and density, may be able to be more accurately predicted at a longer lead time, e.g. from heliospheric imagery. When the input was limited to the velocity and density the models were found to perform well at forecasting SSCs, though with lower reliability than previously (BSSs ~ 0:16, ROC Scores ~ 0:8), Finally, the models were tested with hypothetical extreme data beyond current observations, showing dramatically different extrapolations

Data driven approaches to improving space weather forecasts for the power industry

Space weather impacts technological infrastructure in space and on Earth. This thesis focuses on impacts on power systems through geomagnetic ac�tivity. During heightened geomagnetic activity, currents can be induced in power lines and cause degradation of transformers. Therefore, it is useful to forecast the severity of geomagnetic storms and the resulting geomag�netically induced currents (GICs) so that mitigating action can be taken. This thesis faces this challenge through three bodies of work. The first two bodies focus on forecasting parameters of geomagnetic storms and the third provides a statistical downscaling scheme to aid forecasting of GICs. In the first body of work, the duration of geomagnetic storms is investigated. A statistical relationship is established between storm intensity and duration. A skilful and reliable forecast of storm duration (given storm peak intensity) is made, using log-normal distributions. In the second body of work, two pattern-matching approaches are taken to forecast the occurrence and intensity of geomagnetic storms in geomagnetic index data. The support vector machine and analogue ensemble are implemented for an historical dataset and evaluated using several metrics. It is found that both methods are skilful with respect to climatology and the best method is dependent on the needs of the end-user. The third body of work provides a downscaling scheme to improve the output of operational magnetospheric models such that a more realistic geoelectric field can be forecast. Using the analogue ensemble approach, a proof-of-concept study is presented which relates variability on a 1-hour timescale to a 1-minute timescale. Implemented using a perfect prognostic approach, the downscaling scheme enables a skilful estimate of geoelectric field with respect to the benchmark of no downscaling

Geomagnetic Response to Rapid Increases in Solar Wind Dynamic Pressure: Event Detection and Large Scale Response

Discontinuities in the solar wind trigger a variety of processes in the magnetosphere-ionosphere system. A rapid increase in solar wind dynamic pressure causes compression of the magnetosphere. This manifests itself as a positive perturbation of the horizontal ground magnetic field at low/mid latitudes. In this study we present a method for detecting these discontinuities in situ solar wind data by using the random forest machine learning algorithm. Each detected event is propagated to Earth and its arrival time is aligned with a corresponding response in the low latitude ground magnetic field. A list of 3,867 events, detected between 1994 and 2019, is presented. We use the list in a superposed epoch analysis of the low/mid latitude response in the ground magnetic field at different local times, and of the high latitude response using the Polar Cap index. A dawn-dusk asymmetry is found at low/mid latitudes with weaker positive perturbations at dawn compared to any other local time sector. This suggests a stronger ring current contribution at dawn assuming the magnetopause contribution to be uniform. During northward IMF the initial response is asymmetric, but returns to symmetry after 30 min. During southward IMF the low/mid latitude response decays rapidly in all local sectors except dawn. After around 30 min the asymmetry has flipped such that the strongest positive perturbation is at dawn. This suggests an amplification of the partial ring current. In addition, a noon-midnight asymmetry is observed during southward IMF with the strongest positive perturbation on the night side suggesting a significant contribution from dipolarization of the geomagnetic field in the near tail. The complex geomagnetic response to rapid increases in solar wind dynamic pressure demonstrates a need for further statistical analyses. Event lists, such as the one presented here, are critical components in such studies.publishedVersio

National Astronomy Meeting 2019 Abstract Book

The National Astronomy Meeting 2019 Abstract Book. Abstracts accepted and presented, including both oral and poster presentations, at the Royal Astronomical Society's NAM2019 conference, held at Lancaster University between 30 June and 4 July 2019