The Hunga Tonga Volcano eruption launched a myriad of atmospheric waves that have been observed to travel around the world several times. These waves generated Traveling Ionospheric Disturbances (TIDs) in the ionosphere, which are known to adversely impact radio applications such as Global Navigation Satellite Systems (GNSS). One such GNSS application is Precise Point Positioning (PPP), which can achieve cm-level accuracy using a single receiver, following a typical convergence time of 30 mins to 1 hour. A network of ionosondes located throughout the Australian region were used in combination with GNSS receivers to explore the impacts of the Hunga-Tonga Volcano eruption on the ionosphere and what subsequent impacts they had on PPP. It is shown that PPP accuracy was not significantly impacted by the arrival of the TIDs and Spread-F, provided that PPP convergence had already been achieved. However, when the PPP algorithm was initiated from a cold start either shortly before or after the TID arrivals, the convergence times were significantly longer. GNSS stations in northeastern Australia experienced increases in convergence time of more than 5 hours. Further analysis reveals increased convergence times to be caused by a super equatorial plasma bubble (EPB), the largest observed over Australia to date. The EPB structure was found to be ~42 TECU deep and ~300 km across, traveling eastwards at 30 m/s. The Hunga Tonga Volcano eruption serves as an excellent example of how ionospheric variability can impact real-world applications and the challenges associated with modeling the ionosphere to support GNSS.

This editorial aims to improve awareness of the current best practices in open research, and stimulate discussion on the practical implementation of AGU's data and software policy in key areas of space weather research. We also further aim to encourage authors to take additional steps to ensure clear credit to all contributors to the work, whether that is underlying data, key software, or direct contributions to the manuscript.

Geomagnetically induced currents (GICs) at middle latitudes have received increased attention after reported power-grid disruptions due to geomagnetic disturbances. However, quantifying the risk to the electric power grid at middle latitudes is difficult without understanding how the GIC sensors respond to geomagnetic activity on a daily basis. Therefore, in this study the question “Do measured GICs have distinguishable and quantifiable long- and short-period characteristics?” is addressed. The study focuses on the long-term variability of measured GIC, and establishes the extent to which the variability relates to quiet-time geomagnetic activity. GIC quiet-day curves (QDCs) are computed from measured data for each GIC node, covering all four seasons, and then compared with the seasonal variability of Thermosphere-Ionosphere- Electrodynamics General Circulation Model (TIE-GCM)-simulated neutral wind and height-integrated current density. The results show strong evidence that the middle-latitude nodes routinely respond to the tidal-driven Sq variation, with a local time and seasonal dependence on the the direction of the ionospheric currents, which is specific to each node. The strong dependence of GICs on the Sq currents demonstrates that the GIC QDCs may be employed as a robust baseline from which to quantify the significance of GICs during geomagnetically active times and to isolate those variations to study independently. The QDC-based significance score computed in this study provides power utilities with a node-specific measure of the geomagnetic significance of a given GIC observation. Finally, this study shows that the power grid acts as a giant sensor that may detect ionospheric current systems.

The next decadal survey process for space and solar physics will start soon with white papers due during the second half of 2022 and the committees and panels working over all of 2023. Space weather science and operations will play an essential role in this survey. Therefore, the community is invited to prepare white papers and get involved in advancing space weather research and capabilities in the upcoming decades. A summary of the recommendations related to space weather from the last two decadal surveys is also provided.

Atmospheric mass density (AMD) plays a vital role in the drag calculation for space objects in low Earth orbit (LEO). Many empirical AMD models have been developed and used for orbit prediction and efforts continue to improve their accuracy in forecasting high-altitude atmospheric conditions. Previous studies have assessed these models at the height of 200 km to 600 km. A new empirical AMD model, dubbed as the SERC model, was developed by accounting for ion contribution based on the International Reference Ionosphere 2016 model, including many more ions that are not accounted for in other AMD models. This new model has been assessed in orbit prediction by using a new data source of COSMIC satellite ephemerides at the height of 800 km, where the contribution of ions in the total AMD is more significant. More specifically, two periods of forty days were chosen in 2014–2015 and 2018–2019, representing the solar maximum and minimum periods, respectively, to assess the SERC model and four other state-of-the-art AMD models. Thorough analyses were conducted to compare OP results using different AMD models with precise reference ephemerides of COSMIC satellites and based on various space weather indices. It is indicated that the SERC model outperforms all other AMD models in terms of OP errors during the solar maximum period and yields comparable OP results during the solar minimum period.