This is a database containing electromagnetic (EM) data that can contribute to better understand and quantify the electric fields caused by space weather events at the Earth's surface, and the physical properties of Ireland’s lithosphere. The database is named Space Weather Electromagnetic Database for Ireland (SWEMDI). It contains measured electromagnetic time series using magnetotelluric equipment, electromagnetic tensor relationships, 3D electrical resistivity model of Ireland's lithosphere, modelled electric and magnetic time series for Ireland between 1991 and 2018, documents and publications that used parts of this database, and a series of scripts that were used to generate the database.

The geomagnetic activity index Kp is widely used but is restricted by low time resolution (3‐hourly) and an upper limit. To address this, new geomagnetic activity indices, Hpo, are introduced. Similar to Kp, Hpo expresses the level of planetary geomagnetic activity in units of thirds (0o, 0+, 1−, 1o, 1+, 2−, …) based on the magnitude of geomagnetic disturbances observed at subauroral observatories. Hpo has a higher time resolution than Kp. 30‐min (Hp30) and 60‐min (Hp60) indices are produced. The frequency distribution of Hpo is designed to be similar to that of Kp so that Hpo may be used as a higher time‐resolution alternative to Kp. Unlike Kp, which is capped at 9o, Hpo is an open‐ended index and thus can characterize severe geomagnetic storms more accurately. Hp30, Hp60 and corresponding linearly scaled ap30 and ap60 are available, in near real time, at the GFZ website (https://www.gfz-potsdam.de/en/hpo-index).

Abstract Predictions of the physical parameters of the solar wind at Earth are at the core of operational space weather forecasts. Such predictions typically use line‐of‐sight observations of the photospheric magnetic field to drive a heliospheric model. The models Wang‐Sheeley‐Arge (WSA) and ENLIL for the transport in the heliosphere are commonly used for these respective tasks. Here we analyze the impact of replacing the potential field coronal boundary conditions from WSA with two alternative approaches. The first approach uses a more realistic nonpotential rather than potential approach, based on the Durham Magneto Frictional Code (DUMFRIC) model. In the second approach the ENLIL inner boundary conditions are based on Inter Planetary Scintillation observations (IPS). We compare predicted solar wind speed, plasma density, and magnetic field magnitude with observations from the WIND spacecraft for two 6‐month intervals in 2014 and 2016. Results show that all models tested produce fairly similar output when compared to the observed time series. This is not only reflected in fairly low correlation coefficients (<0.3) but also large biases. For example, for solar wind speed some models have average biases of more than 150 km/s. On a positive note, the choice of coronal magnetic field model has a clear influence on the model results when compared to the other models in this study. Simulations driven by IPS data have a high success rate with regard to detection of the high speed solar wind. Our results also indicate that model forecasts do not degrade for longer forecast times.

Abstract The aurora is a readily visible phenomenon of interest to many members of the public. However, the aurora and associated phenomena can also significantly impact communications, ground‐based infrastructure, and high‐altitude radiation exposure. Forecasting the location of the auroral oval is therefore a key component of space weather forecast operations. A version of the OVATION‐Prime 2013 auroral precipitation model (Newell et al., 2014, https://doi.org/10.1002/2014sw001056) was used by the UK Met Office Space Weather Operations Centre (MOSWOC). The operational implementation of the OVATION‐Prime 2013 model at the UK Met Office delivered a 30‐min forecast of the location of the auroral oval and the probability of observing the aurora. Using weather forecast evaluation techniques, we evaluate the ability of the OVATION‐Prime 2013 model forecasts to predict the location and probability of the aurora occurring by comparing the forecasts with auroral boundaries determined from data from the IMAGE satellite between 2000 and 2002. Our analysis shows that the operational model performs well at predicting the location of the auroral oval, with a relative operating characteristic (ROC) score of 0.82. The model performance is reduced in the dayside local time sectors (ROC score = 0.59) and during periods of higher geomagnetic activity (ROC score of 0.55 for Kp = 8). As a probabilistic forecast, OVATION‐Prime 2013 tends to underpredict the occurrence of aurora by a factor of 1.1–6, while probabilities of over 90% are overpredicted.

The open magnetic flux content of the magnetosphere varies during substorms as a result of dayside and nightside reconnection. The open flux can be calculated from the area of the polar cap, delineated by the open‐closed field line boundary (OCB). This study presents a superposed epoch analysis of the location of the OCB and the change in the magnetic flux content in individual nightside MLT sectors during substorm growth, expansion, and recovery phases. Far ultraviolet (FUV) observations from the IMAGE satellite are used to derive a proxy of the OCB location. In the hour prior to substorm onset, the total nightside flux content increases by up to 0.12 GWb on average, resulting in an equatorward expansion of the OCB. Following substorm onset, the OCB contracts toward the pole as the open magnetic flux content decreases by up to 0.14 GWb on average, but the rate of decrease of the total nightside open flux content differs by 5–66% between the three IMAGE far ultraviolet instruments. The OCB does not contract poleward uniformly in all nightside magnetic local time (MLT) sectors after substorm onset. Close to the substorm onset MLT sector, the OCB contracts immediately following substorm onset; however, the OCB in more dawnward and duskward MLT sectors continues to expand equatorward for up to 120 minutes after substorm onset. Despite the continued increase in flux in these sectors after substorm onset, the total nightside flux content decreases immediately at substorm onset, indicating that the nightside reconnection rate exceeds the dayside rate following substorm onset.

Abstract 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), nonlinear (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, for example, 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.

Advances in space weather science and small satellite (SmallSat) technology have proceeded in parallel over the past two decades, but better communication and coordination is needed among the respective worldwide communities contributing to this rapid progress. We identify six areas where improved international coordination is especially desirable, including: (1) orbital debris mitigation; (2) spectrum management; (3) export control regulations; (4) access to timely and low-cost launch opportunities; (5) inclusive data policies; and (6) education. We argue the need for internationally coordinated policies and programs to promote the use of SmallSats for space weather research and forecasting while realizing maximum scientific and technical advances through the integration of these two increasingly important endeavors.

Coronal mass ejections (CMEs) cause the largest geomagnetic disturbances at Earth, which impact satellites, wired communication systems, and power grids. The CME Analysis Tool (CAT) is used to determine a CME's initial longitude, latitude, angular width, and radial speed from coronagraph images. These are the initial conditions for the Wang‐Sheeley‐Arge Enlil solar wind model, along with the ambient solar wind properties derived from magnetograms. However, the coronagraph imagery is limited by field of view. We have incorporated heliospheric imagery (HI) from the Solar Terrestrial Relations Observatory into CAT to create the CME Analysis Tool with Heliospheric Imagery (CAT‐HI). These HI images have a larger field of view, allowing tracking of CMEs to greater distances from the Sun. We have compared the performances of CAT and CAT‐HI by examining the expected arrival times of CMEs at the L1 Lagrange point and found them to be consistent. However, CAT‐HI is advantageous because it could be used to prune ensemble forecasts and issue routine updates for CME arrival time forecasts. Finally, we discuss CAT‐HI in the context of an operational mission at the L4 or L5 Lagrange points.

Historically, gathering data on atmospheric radiation levels during solar particle events has been difficult, as there is little or no time warning of events. Being able to accurately quantify radiation levels within the atmosphere during solar events is of significance to the aviation industry, as described in the International Civil Aviation Organization's (ICAO) Space Weather manual. Particularly during a large ground-level enhancement (GLE) where the ionizing dose to passengers and crew can exceed the recommended general public annual dose limits, set by the International Commission for Radiological Protection (Barlett, Beck, Bilski, Bottollier-Depois, & Lindborg, 2004, https:// doi.org/10.1093/rpd/nch232), in a single flight. The Smart Atmospheric Ionizing RAdiation (SAIRA) Monitoring Network is a new system of handheld radiation detectors that can be carried on aircraft to monitor and record atmospheric radiation levels. The system operates via citizen science volunteers, who record radiation data as they travel for normal purposes. Over 30 flights have been conducted with volunteers to demonstrate that a citizen science network is possible. Volunteers have used a new Android application to record and upload data to a central server to form a database of flight measurements. The demonstration has shown that there is a willingness in public volunteers to use radiation detectors and engage in science outreach. A fully developed system will ideally provide the capability to quantify radiation levels during a solar particle event or ground-level enhancement and the data can be used by relevant organizations to minimize potential risks.

Geoelectric fields at the Earth's surface caused by geomagnetic storms have the potential to disrupt and damage ground‐based infrastructure such as electrical power distribution networks, pipelines, and railways. Here we model geoelectric fields in Ireland and the UK during both quiet and active time intervals of geomagnetic conditions using measurements from magnetic observatories and electromagnetic tensor relationships. The analysis focused on (1) defining periods of the magnetic field variations that are largely affected by the geomagnetic storms, between 30 and 30,000 s; (2) constraining the electromagnetic tensor relationships that defines the Earth's response to magnetic field variations; (3) implementing and validating two approaches for modeling geoelectric fields based on measurements from magnetic observatories and local and interstation electromagnetic transfer functions; and (4) estimating uncertainties when modeling geoelectric fields. The use of interstation tensor relationships allowed us to differentiate between regional and local geomagnetic sources. We found coherence values of 0.5–0.95, signal‐to‐noise ratio of 1–15 dB, normalized root‐mean‐square values of 0.8–3.4, and root‐mean‐square values of 0.7–84 mV/km. Within these ranges of values, sites in close proximity (<100 km) to a magnetic observatory and not affected by local storms will provide the most accurate results, while sites located at further distances and affected by spatially localized features of the storm will be less accurate. These methods enable us to more accurately model geomagnetically induced currents, and their associated uncertainties, in the British and Irish power networks.