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Abstract Submarine cables have experienced problems during extreme geomagnetic disturbances because of geomagnetically induced voltages adding or subtracting from the power feed to the repeaters. This is still a concern for modern fiber‐optic cables because they contain a copper conductor to carry power to the repeaters. This paper provides a new examination of geomagnetic induction in submarine cables and makes calculations of the voltages experienced by the TAT‐8 trans‐Atlantic submarine cable during the March 1989 magnetic storm. It is shown that the cable itself experiences an induced electromotive force (emf) and that induction in the ocean also leads to changes of potential of the land at each end of the cable. The process for calculating the electric fields induced in the sea and in the cable from knowledge of the seawater depth and conductivity and subsea conductivity is explained. The cable route is divided into 9 sections and the seafloor electric field is calculated for each section. These are combined to give the total induced emf in the cable. In addition, induction in the seawater and leakage of induced currents through the underlying resistive layers are modeled using a transmission line model of the ocean and underlying layers to determine the change in Earth potentials at the cable ends. The induced emf in the cable and the end potentials are then combined to give the total voltage change experienced by the cable power feed equipment. This gives results very close to those recorded on the TAT‐8 cable in March 1989. 

Submarine cables have become a vital component of modern infrastructure, but past submarine cable natural hazard studies have mostly focused on potential cable damage from landslides and tsunamis. A handful of studies examine the possibility of space weather effects in submarine cables. The main purpose of this study is to develop a computational model, using Python , of geomagnetic induction on submarine cables. The model is used to estimate the induced voltage in the submarine cables in response to geomagnetic disturbances. It also utilizes newly acquired knowledge from magnetotelluric studies and associated investigations of geomagnetically induced currents in power systems. We describe the Python-based software, its working principle, inputs/outputs based on synthetic geomagnetic field data, and compare its operational capabilities against analytical solutions. We present the results for different model inputs, and find: 1) the seawater layer acts as a shield in the induction process: the greater the ocean depth, the smaller the seafloor geoelectric field; and 2) the model is sensitive to the Ocean-Earth layered conductivity structure. 

Abstract Interplanetary (IP) shock‐driven sudden compression of the Earth's magnetosphere produces electromagnetic disturbances in the polar ionosphere. Several studies have examined the effects of IP shock on magnetosphere‐ionosphere coupling systems using all‐sky cameras and radars. In this study, we examine responses and drivers of the polar ionosphere following an IP shock compression on 16 June 2012. We observe the vertical drift and concurrent horizontal motion of the plasma. Observations from digisonde located at Antarctic Zhongshan station (ZHO) showed an ionospheric thickEregion ionization and associated vertical downward plasma motion atFregion. In addition, horizontal ionospheric convection reversals were observed on the Super Dual Auroral Radar Network ZHO and McMurdo radar observations. Findings suggest that the transient convective reversal breaks the original shear equilibrium, it is expected that the IP shock‐induced electric field triggers an enhanced velocity shear mapping to theEregion. The horizontal motion of the plasma was attributed to only the dusk‐to‐dawn electric field that existed during the preliminary phase of sudden impulse. We also found that ionospheric convection reversals were driven by a downward field‐aligned current. The results of these observations reveal, for the first time, the immediate and direct cusp ionosphere response to the IP shock, which is critical for understanding the global response of the magnetosphere following an abrupt change in Interplanetory Magnetic Field (IMF) and solar wind conditions. 

The Super Dual Auroral Radar Network (SuperDARN) is an international network of high frequency coherent scatter radars that are used for monitoring the electrodynamics of the Earth’s upper atmosphere at middle, high, and polar latitudes in both hemispheres. pyDARN is an open-source Python-based library developed specifically for visualizing SuperDARN radar data products. It provides various plotting functions of different types of SuperDARN data, including time series plot, range-time parameter plot, fields of view, full scan, and global convection map plots. In this paper, we review the different types of SuperDARN data products, pyDARN’s development history and goals, the current implementation of pyDARN, and various plotting and analysis functionalities. We also discuss applications of pyDARN, how it can be combined with other existing Python software for scientific analysis, challenges for pyDARN development and future plans. Examples showing how to read, visualize, and interpret different SuperDARN data products using pyDARN are provided as a Jupyter notebook. 

Abstract. Ionospheric variability produces measurable effects in Doppler shift of HF (high-frequency, 3–30 MHz) skywave signals. These effects are straightforward to measure with low-cost equipment and are conducive to citizen science campaigns. The low-cost Personal Space Weather Station (PSWS) network is a modular network of community-maintained, open-source receivers, which measure Doppler shift in the precise carrier signals of time standard stations. The primary goal of this paper is to explain the types of measurements this instrument can make and some of its use cases, demonstrating its role as the building block for a large-scale ionospheric and HF propagation measurement network which complements existing professional networks. Here, data from the PSWS network are presented for a period of time spanning late 2019 to early 2022. Software tools for the visualization and analysis of this living dataset are also discussed and provided. These tools are robust to data interruptions and to the addition, removal or modification of stations, allowing both short- and long-term visualization at higher density and faster cadence than other methods. These data may be used to supplement observations made with other geospace instruments in event-based analyses, e.g., traveling ionospheric disturbances and solar flares, and to assess the accuracy of the bottomside estimates of ionospheric models by comparing the oblique paths obtained by ionospheric ray tracers with those obtained by these receivers. The data are archived at https://doi.org/10.5281/zenodo.6622111 (Collins, 2022). 

Abstract. During minor to moderate geomagnetic storms, caused by corotatinginteraction regions (CIRs) at the leading edge of high-speed streams (HSSs), solar windAlfvén waves modulated the magnetic reconnection at the daysidemagnetopause. The Resolute Bay Incoherent Scatter Radars (RISR-C andRISR-N), measuring plasma parameters in the cusp and polar cap, observedionospheric signatures of flux transfer events (FTEs) that resulted in theformation of polar cap patches. The patches were observed as they moved over the RISR, and the Canadian High-Arctic Ionospheric Network (CHAIN)ionosondes and GPS receivers. The coupling process modulated the ionospheric convection and the intensity of ionospheric currents, including the auroral electrojets. The horizontal equivalent ionospheric currents (EICs) are estimated from ground-based magnetometer data using an inversion technique. Pulses of ionospheric currents that are a source of Joule heating in the lower thermosphere launched atmospheric gravity waves, causing travelingionospheric disturbances (TIDs) that propagated equatorward. The TIDs wereobserved in the SuperDual Auroral Radar Network (SuperDARN) high-frequency (HF) radar groundscatter and the detrended total electron content (TEC) measured by globallydistributed Global Navigation Satellite System (GNSS) receivers. 

Abstract We introduce a new framework called Machine Learning (ML) based Auroral Ionospheric electrodynamics Model (ML‐AIM). ML‐AIM solves a current continuity equation by utilizing the ML model of Field Aligned Currents of Kunduri et al. (2020,https://doi.org/10.1029/2020JA027908), the FAC‐derived auroral conductance model of Robinson et al. (2020,https://doi.org/10.1029/2020JA028008), and the solar irradiance conductance model of Moen and Brekke (1993,https://doi.org/10.1029/92gl02109). The ML‐AIM inputs are 60‐min time histories of solar wind plasma, interplanetary magnetic fields (IMF), and geomagnetic indices, and its outputs are ionospheric electric potential, electric fields, Pedersen/Hall currents, and Joule Heating. We conduct two ML‐AIM simulations for a weak geomagnetic activity interval on 14 May 2013 and a geomagnetic storm on 7–8 September 2017. ML‐AIM produces physically accurate ionospheric potential patterns such as the two‐cell convection pattern and the enhancement of electric potentials during active times. The cross polar cap potentials (Φ_PC) from ML‐AIM, the Weimer (2005,https://doi.org/10.1029/2004ja010884) model, and the Super Dual Auroral Radar Network (SuperDARN) data‐assimilated potentials, are compared to the ones from 3204 polar crossings of the Defense Meteorological Satellite Program F17 satellite, showing better performance of ML‐AIM than others. ML‐AIM is unique and innovative because it predicts ionospheric responses to the time‐varying solar wind and geomagnetic conditions, while the other traditional empirical models like Weimer (2005,https://doi.org/10.1029/2004ja010884) designed to provide a quasi‐static ionospheric condition under quasi‐steady solar wind/IMF conditions. Plans are underway to improve ML‐AIM performance by including a fully ML network of models of aurora precipitation and ionospheric conductance, targeting its characterization of geomagnetically active times. 

Abstract Ionospheric conductance is a crucial factor in regulating the closure of magnetospheric field‐aligned currents through the ionosphere as Hall and Pedersen currents. Despite its importance in predictive investigations of the magnetosphere‐ionosphere coupling, the estimation of ionospheric conductance in the auroral region is precarious in most global first‐principles‐based models. This impreciseness in estimating the auroral conductance impedes both our understanding and predictive capabilities of the magnetosphere‐ionosphere system during extreme space weather events. In this article, we address this concern, with the development of an advanced Conductance Model for Extreme Events (CMEE) that estimates the auroral conductance from field‐aligned current values. CMEE has been developed using nonlinear regression over a year's worth of 1‐min resolution output from assimilative maps, specifically including times of extreme driving of the solar wind‐magnetosphere‐ionosphere system. The model also includes provisions to enhance the conductance in the aurora using additional adjustments to refine the auroral oval. CMEE has been incorporated within the Ridley Ionosphere Model (RIM) of the Space Weather Modeling Framework (SWMF) for usage in space weather simulations. This paper compares performance of CMEE against the existing conductance model in RIM, through a validation process for six space weather events. The performance analysis indicates overall improvement in the ionospheric feedback to ground‐based space weather forecasts. Specifically, the model is able to improve the prediction of ionospheric currents, which impact the simulateddB/dtandΔB, resulting in substantial improvements indB/dtpredictive skill.