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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. 

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. 

Intense geoelectric fields during geomagnetic storms drive geomagnetically induced currents in power grids and other infrastructure, yet there are limited direct measurements of these storm‐time geoelectric fields. Moreover, most previous studies examining storm‐time geoelectric fields focused on single events or small geographic regions, making it difficult to determine the typical source(s) of intense geoelectric fields. We perform the first comparative analysis of (a) the sources of intense geoelectric fields over multiple geomagnetic storms, (b) using 1‐s cadence geoelectric field measurements made at (c) magnetotelluric survey sites distributed widely across the United States. Temporally localized intense perturbations in measured geoelectric fields with prominences (a measure of the relative amplitude of geoelectric field enhancement above the surrounding signal) of at least 500 mV/km were detected during geomagnetic storms with Dst minima (Dst_min) of less than −100 nT from 2006 to 2019. Most of the intense geoelectric fields were observed in resistive regions with magnetic latitudes greater than 55° even though we have 167 sites located at lower latitudes during geomagnetic storms of −200 nT ≤ Dst_min< −100 nT. Our study indicates intense short‐lived (<1 min) and geoelectric field perturbations with periods on the order of 1–2 min are common. Most of these perturbations cannot be resolved with 1‐min data because they correspond to higher frequency or impulsive phenomena that vary on timescales shorter than that sampling interval. The sources of geomagnetic perturbations inducing these intense geoelectric fields include interplanetary shocks, interplanetary magnetic field turnings, substorms, and ultralow frequency waves.

 

The most dynamic electromagnetic coupling between the magnetosphere and ionosphere occurs in the polar upper atmosphere. It is critical to quantify the electromagnetic energy and momentum input associated with this coupling as its impacts on the ionosphere and thermosphere system are global and major, often leading to considerable disturbances in near‐Earth space environments. The current general circulation models of the upper atmosphere exhibit systematic biases that can be attributed to an inadequate representation of the Joule heating rate resulting from unaccounted stochastic fluctuations of electric fields associated with the magnetosphere‐ionosphere coupling. These biases exist regardless of geomagnetic activity levels. To overcome this limitation, a new multiresolution random field modeling approach is developed, and the efficacy of the approach is demonstrated using Super Dual Auroral Radar Network (SuperDARN) data carefully curated for the study during a largely quiet 4‐hour period on February 29, 2012. Regional small‐scale electrostatic fields sampled at different resolutions from a probabilistic distribution of electric field variability conditioned on actual SuperDARN LOS observations exhibit considerably more localized fine‐scale features in comparison to global large‐scale fields modeled using the SuperDARN Assimilative Mapping procedure. The overall hemispherically integrated Joule heating rate is increased by a factor of about 1.5 due to the effect of random regional small‐scale electric fields, which is close to the lower end of arbitrarily adjusted Joule heating multiplicative factor of 1.5 and 2.5 typically used in upper atmosphere general circulation models. The study represents an important step toward a data‐driven ensemble modeling of magnetosphere‐ionosphere‐atmosphere coupling processes.

 

An interplanetary shock can abruptly compress the magnetosphere, excite magnetospheric waves and field‐aligned currents, and cause a ground magnetic response known as a sudden commencement (SC). However, the transient (<∼1 min) response of the ionosphere‐thermosphere system during an SC has been little studied due to limited temporal resolution in previous investigations. Here, we report observations of a global reversal of ionospheric vertical plasma motion during an SC on 24 October 2011 using ∼6 s resolution Super Dual Auroral Radar Network ground scatter data. The dayside ionosphere suddenly moved downward during the magnetospheric compression due to the SC, lasting for only ∼1 min before moving upward. By contrast, the post‐midnight ionosphere briefly moved upward then moved downward during the SC. Simulations with a coupled geospace model suggest that the reversedvertical drift is caused by a global reversal of ionospheric zonal electric field induced by magnetospheric compression during the SC.

 

Intense sunward (westward) plasma flows, named Subauroral Polarization Stream (SAPS), have been known to occur equatorward of the electron auroras for decades, yet their effect on the upper thermosphere has not been well understood. On the one hand, the large velocity of SAPS results in large momentum exchange upon each ion‐neutral collision. On the other hand, the low plasma density associated with SAPS implies a low ion‐neutral collision frequency. We investigate the SAPS effect during non‐storm time by utilizing a Scanning Doppler Imager (SDI) for monitoring the upper thermosphere, SuperDARN radars for SAPS, all‐sky imagers and DMSP Spectrographic Imager for the auroral oval, and GPS receivers for the total electron content. Our observations suggest that SAPS at times drives substantial (>50 m/s) westward winds at subauroral latitudes in the dusk‐midnight sector, but not always. The occurrence of the westward winds varies withAEindex, plasma content in the trough, and local time. The latitudinally averaged wind speed varies from 60 to 160 m/s, and is statistically 21% of the plasma. These westward winds also shift to lower latitude with increasingAEand increasing MLT. We do not observe SAPS driving poleward wind surges, neutral temperature enhancements, or acoustic‐gravity waves, likely due to the somewhat weak forcing of SAPS during the non‐storm time.

 

GPS total electron content (TEC) measurements were used to investigate high‐m ultralow frequency (ULF) waves during the recovery phase of a geomagnetic storm. ULF wave signals in TEC data show high coherence and significant common power in the wavelet coherence and cross wavelet transform analyses with magnetic field radial component data from GOES‐15. They did not cause significant ionospheric scintillation or ground magnetic signatures due to ionospheric screening effects. An automatic identification procedure is developed to identify ULF wave signature in TEC data from 10 GPS receivers on January 25, 2016. The waves were mainly distributed on the dayside and post dusk sector from ∼64° to ∼71° magnetic latitude. This is the first time that the large‐scale 2D spatial structure and temporal evolution of high‐m ULF waves are revealed, which demonstrates TEC measurements as an effective high‐m ULF wave remote sensing tool.