Sarah N. Bentley

A geomagnetic storm is a significant and prolonged disturbance of the Earth's magnetic field caused by an enhancement of the ring current. Storms are typically identified using magnetic field measurements from multiple ground‐based magnetometers, and the corresponding dates and times of multiple storms can be collated to form a storm list. There is currently no quantitative definition of a geomagnetic storm, so the contents of a particular storm list are highly dependent on the identification criteria that were used to generate it. The different definitions and identification methods can cause the physical properties of storms in different lists to vary, and these variations may be significant. We take several storm lists with different identification methods, and we compare the probability distributions of a range of solar wind variables and geomagnetic indices between those lists. We also compare the temporal behavior of the storm lists, and discuss how the differences may affect the way geomagnetic storms are defined and studied. We find that changing the definition of the quiet time has a direct impact on the start and end times of the storms, and this can result in a storm being significantly shorter when defined using a more negative quiet time definition. We also find that changes to the threshold value of minimum magnetic disturbance have a greater impact on the properties of the identified storms than changes to the quiet time definition. We provide recommendations on which storm lists to use for different scenarios.

Quasilinear diffusion coefficients can be used to model the response of charged particles to resonant wave‐particle interactions. The calculation of these coefficients is sufficiently complicated and arduous to render it prohibitive to many potential users, because of the expense in time spent developing the code. The PIRAN software package (”Particles In ResonANce”) is written using Python, and allows the user to calculate local and bounce‐averaged relativistic diffusion coefficients in energy and pitch‐angle space via the two main current proposed methods in the literature. The code is predominantly based upon the formalisms and methods presented in Glauert and Horne (2005, https://doi.org/10.1029/2004JA010851 ) and Cunningham (2023, https://doi.org/10.1029/10.1029/2023JA031703 ). We solve for diffusion coefficients using exact relativistic formulae. We use Gaussian spectra in wave frequency and in tangent of the wave normal angle and solve the full cold‐plasma dispersion relation. At present the code supports fully tested calculations for electron diffusion coefficients based on whistler‐mode waves in a fully ionized proton‐electron cold plasma. However the codebase architecture is built such that future developments to include other wave modes and other plasma compositions should involve incremental additions. The initial release of PIRAN may not have the same number of features as some other numerical codes, but is has the advantages of being a fully open‐source diffusion coefficient code that: (a) supports calculation of both local and bounce‐averaged diffusion coefficients via both of the two proposed methods; (b) is written fully in Python; (c) has detailed user pages, commit history and changelog on GitHub.

Wave-particle interactions are a fundamental mechanism to control irreversible plasma energization and energy transport throughout the Heliosphere, and universally throughout astrophysical plasma domains. The most tractable paradigm to model the plasma response to perturbations by plasma waves is the 60 year old quasilinear diffusion theory. This paradigm predominates in our understanding, but within the last two decades there has been a sustained resurgence and emergence of fundamental new questions motivated by the discovery of highly variable, intense/energetic and structured electromagnetic plasma waves and wave-particle interaction plasma physics processes by single and multi-point missions. These interactions act and control plasma energization and energy transport from microscale (gyroradius/kinetic) through to the macroscale (system scale), and in addition crucially link these scales via complex coupled fluid/mesoscale plasma physics processes. We discuss recent advances, and highlight some open, fundamental questions for wave-particle interactions that the Plasma Observatory Mission can solve via multiscale observations.

A geomagnetic storm is a significant and prolonged disturbance of the Earth’s magnetic field caused by an enhancement of the ring current. Storms are typically identified using magnetic field measurements from multiple ground-based magnetometers, and the corresponding dates and times of multiple storms can be collated to form a storm list. There is currently no quantitative definition of a geomagnetic storm, so the contents of a particular storm list are highly dependent on the identification criteria that were used to generate it. The different definitions and identification methods can cause the physical properties of storms in different lists to vary, and these variations may be significant. We take several storm lists with different identification methods, and we compare the probability distributions of a range of solar wind variables and geomagnetic indices between those lists. We also compare the temporal behaviour of the storm lists, and discuss how the differences may affect the way geomagnetic storms are defined and studied. We find that changing the definition of the quiet time has a direct impact on the start and end times of the storms, and this can result in a storm being significantly shorter when defined using a more negative quiet time definition. We also find that changes to the threshold value of minimum magnetic disturbance have a greater impact on the properties of the identified storms than changes to the quiet time definition. We provide recommendations on which storm lists to use for different scenarios.

Numerical models of energetic electron behavior in the outer radiation belt require descriptions of the wave‐particle interactions across the inner magnetosphere. Quasilinear diffusion coefficients describe gyro‐resonant wave‐particle interactions over large time‐ and length‐scales but these must be constrained by observations to construct realistic radiation belt models. Recent work indicates the importance of identifying and including realistic spatiotemporal variation of diffusion coefficients. In this paper, we study the spatiotemporal variability of whistler‐mode waves outside the plasmasphere, typically referred to as whistler‐mode chorus. We separately consider the probability of (a) parts of the model domain being outside the plasmasphere, and (b) the probability of detecting wave activity should that part of the model domain be outside the plasmasphere. We discover that the spatiotemporal variability of whistler‐mode waves significantly differs across the model domain; we propose that wave power variability in short wave intervals ( min) is a useful characteristic to distinguish between two types of whistler‐mode waves, especially where their frequency ranges overlap. Our novel spatiotemporal variability analysis indicates that low variability waves are dayside exohiss whose typically high occurrence rate () decreases with substorm activity, and high variability waves are sporadic post‐midnight/dawn sector substorm‐driven chorus with a typical occurrence rate of 0.2. Further, although previous studies often combine the occurrence rates and wave characteristics into climatological averages of chorus wave power, this study highlights the importance of separating the study of occurrence rates and power of the waves, since each can have a different relationship with driving factors.

Space weather describes the dynamic conditions in near-Earth space, mostly driven by the variable interaction between the continuous flow of the solar wind and the Earth’s magnetic field. Extreme space weather has the potential to disrupt or damage key infrastructure on which we rely, for example through the generation of large, anomalous Geomagnetically Induced Currents (GICs) in power networks and transformers. Accurately forecasting a risk of large GICs would enable key actions to be taken to mitigate their impact.Given the sparsity of direct GIC measurements, and their inherent specificity to the contemporaneous network properties and configuration, we turn to forecasting the driving factor: the changing ground magnetic field (R). In this talk we discuss a recent model developed to forecast whether the rate of change of the ground magnetic field (R) will exceed specific, high thresholds in the United Kingdom. The model uses a common space weather forecasting framework: an interval of data from the upstream solar wind is used to make a prediction as to future conditions at the Earth. We will use this model as an example to discuss forecasting performance, particularly with respect to different magnetospheric driving and processes. We demonstrate the use of techniques such as SHAP (Shapley Additive exPlanations) to investigate how and why the model is making the predictions that it does. What physical processes can this model set up capture? Where do we need to go in the future?

Van Allen radiation belt electron dynamics are governed by a multitude of physical processes that can simultaneously drive acceleration, transport and loss. However, each individual process can be linked to a specific energy‐dependent pitch angle distribution (PAD). We employ a new, unsupervised machine learning technique on 7‐year of Van Allen Probe Relativistic Electron‐Proton Telescope data and discover that six PADs successfully describe 93% of outer belt relativistic electrons, two each of: pancake, butterfly, and flattop. We investigate the occurrence and storm‐time evolution of each PAD through 45 geomagnetic storms. We find new populations of PADs, including: “shadowing‐like” and wave‐particle interaction signatures at low‐L, and radial diffusion and substorm injections at higher‐L, as well as determining that wave‐particle interaction dominated PADs are swamped by radial diffusion processes through geomagnetic storms. Our results clearly demonstrate that PAD characterization is a key component of understanding Van Allen radiation belt electron dynamics.

Van Allen radiation belt electron dynamics are governed by a wide range of physical processes that can simultaneously drive acceleration, transport and loss. However, each individual process can be linked to a specific energy-dependent pitch angle distribution (PAD). We employ a new, unsupervised machine learning technique on 7-years of Van Allen Probe Relativistic Electron-Proton Telescope data and discover that six PADs, two each of: pancake, butterfly, and flattop, successfully describe >70% of classified relativistic PADs. We investigate the occurrence and storm-time evolution of each PAD through 45 geomagnetic storms. We find new populations of PADs, including: “shadowing-like” and wave-particle interaction signatures at low-L, and radial diffusion and substorm injections at higher-L, as well as determining that wave-particle interaction dominated PADs are swamped by radial diffusion processes through geomagnetic storms. Our results clearly demonstrate that PAD characterisation is a key component of understanding Van Allen radiation belt electron dynamics

Forecasting the aurora and its location accurately is important to mitigate any potential harm to vital infrastructure like communications and electricity grid networks. Current auroral prediction models rely on our understanding of the interaction between the magnetosphere and the solar wind or geomagnetic indices. Both approaches do well in predicting but have limitations concerning forecasting (geomagnetic indices-based model) or because of the underlying assumptions driving the model (due to a simplification of the complex interaction). By applying machine learning algorithms to this problem, gaps in our understanding can be identified, investigated, and closed. Finding the important time scales for driving empirical models provides the necessary basis for our long-term goal of predicting the aurora using machine learning.Periodicities of the Earth’s magnetic field have been extensively studied on a global scale or in regional case studies. Using a suite of different time series analysis techniques including frequency analysis and investigation of long-scale changes of the median/ mean, we examine the dominant periodicities of ground magnetic field measurements at selected locations. A selected number of stations from the SuperMAG network (Gjerloev, 2012), which is a global network of magnetometer stations across the world, are the focus of this investigation.The periodicities retrieved from the different magnetic field components are compared to each other as well as to other locations. In the context of auroral predictions, an analysis of the dominating periodicities in the auroral boundary data derived from the IMAGE satellite (Chisham et al., 2022) provides a counterpart to the magnetic field periodicities.Ultimately, we can constrain the length of time history sensible for forecasting.

Geomagnetically Induced Currents (GICs) are a severe space weather hazard, driven through coupling between the solar wind and magnetosphere. GICs are rarely measured directly, instead the ground magnetic field variability is often used as a proxy. Recently space weather models have been developed to forecast whether the magnetic field variability (R) will exceed specific, extreme thresholds. We test an example machine learning‐based model developed for the northern United Kingdom. We evaluate its performance (discriminative skill and calibration) as a function of magnetospheric state, solar wind input and magnetic local time. We find that the model's performance is highest during active conditions, for example, geomagnetic storms, and lowest during isolated substorms and “quiet” intervals, despite these conditions dominating the training data set. Correspondingly, the performance is high when the solar wind conditions are elevated (i.e., high velocity, large total magnetic field strength, and the interplanetary magnetic field oriented southward), and at a minimum when the north‐south component of the magnetic field is highly variable or around zero. Regarding magnetic local time, performance is highest within the dusk and night sectors, and lowest during the day. The model appears to capture multiple modes of magnetospheric activity, including substorms and viscous interactions, but poorly predicts impulsive phenomena (i.e., storm sudden commencements) and longer timescale coupling processes. Future models of mid‐latitude magnetic field variability will need to effectively use longer time intervals of unpropagated (i.e., observations from L1) solar wind to more completely describe the magnetospheric conditions and response.