Under periods of strong solar wind driving, the magnetopause can become compressed, playing a significant role in draining electrons from the outer radiation belt. Also termed "magnetopause shadowing," this loss process has traditionally been attributed to a combination of magnetospheric compression and outward radial diffusion of electrons. However, the drift paths of relativistic electrons and the location of the magnetopause are usually calculated from statistical models and, as such, may not represent the time-varying nature of this highly dynamic process. In this study, we construct a database ∼20,000 spacecraft crossings of the dayside magnetopause to quantify the accuracy of the commonly used Shue et al. (1998, https://doi.org/10.1029/98JA01103) model. We find that, for the majority of events (74%), the magnetopause model can be used to estimate magnetopause location to within ±1 R E . However, if the magnetopause is compressed below 8 R E , the observed magnetopause is greater than 1 R E inside of the model location on average. The observed magnetopause is also significantly displaced from the model location during storm sudden commencements, when measurements are on average 6% closer to the radiation belts, with a maximum of 42%. We find that the magnetopause is rarely close enough to the outer radiation belt to cause direct magnetopause shadowing, and hence rapid outward radial transport of electrons is also required. We conclude that statistical magnetopause parameterizations may not be appropriate during dynamic compressions. We suggest that statistical models should only be used during quiescent solar wind conditions and supplemented by magnetopause observations wherever possible.

The auroral electrojets (AEJs) are complex and dynamic horizontal ionospheric electric currents which form ovals around Earth's poles, being controlled by the morphology of the main magnetic field and the energy input from the solar wind interaction with the magnetosphere. The strength and location of the AEJ varies with solar wind conditions and the solar cycle but should also be controlled on decadal timescales by main field secular variation. To determine the AEJ climatology, we use data from four polar Low Earth Orbit magnetic satellite missions: POGO, Magsat, CHAMP, and Swarm. A simple estimation of the AEJ strength and latitude is made from each pass of the satellites, from peaks in the along‐track gradient of the magnetic field intensity after subtracting a core and crustal magnetic field model. This measure of the AEJ activity is used to study the response in different sectors of magnetic local time (MLT) during different seasons and directions of the interplanetary magnetic field (IMF). We find a season‐dependent hemispherical asymmetry in the AEJ response to IMF By, with a tendency toward stronger (weaker) AEJ currents in the north than the south during By>0 (By<0) around local winter. This effect disappears during local summer when we find a tendency toward stronger currents in the south than the north. The solar cycle modulation of the AEJ and the long‐term shifting of its position and strength due to the core field variation are presented as challenges to internal field modeling.

&lt;p&gt;Under steady-state conditions the magnetopause location is described as a pressure balance between internal magnetic pressures and the external dynamic pressure of the solar wind. The question is, does this approximation hold during more dynamic solar wind features?&lt;/p&gt;&lt;p&gt;Under more extreme solar wind driving, such as high solar wind pressures or strong southward-directed interplanetary magnetic fields, this boundary is significantly more compressed than in steady-state, playing a significant role in the depletion of magnetospheric plasma from the Van Allen Radiation Belts, via magnetopause shadowing. Large step-changes in solar wind conditions enable the real magnetopause to have a significant time-dependence which empirical models cannot capture.&lt;/p&gt;&lt;p&gt;We use a database of ~20,000 magnetopause crossings, to determine how the measured magnetopause differs from a statistical model, and under which conditions. We find that observed magnetopause is on average 6% closer to the radiation belts,&amp;#160; with a maximum of 42%, during periods of sudden dynamic pressure enhancement, such as during storm sudden commencement. Our results demonstrate that empirical magnetopause models such as the Shue et al. [1998] model should be used cautiously to interpret energetic electron losses by magnetopause shadowing.&amp;#160;&lt;/p&gt;

&lt;p&gt;SwarmPAL is a new Python package under development with support from Swarm DISC (Data, Innovation, and Science Cluster). This project aims to provide the research community with a suite of tools to rapidly access, analyse, and visualise data from Swarm (and related data sources). By relying on the VirES system [1] for data access and other utilties, this greatly reduces the complexity required within SwarmPAL. By making use of HAPI [2], we can also connect to many other data sources to retrieve data from beyond Swarm. This is part of an overall strategy for integrating with the wider Python (and Jupyter) ecosystem, while being cognisant of the particular scientific landscape occupied by Swarm [3].&lt;/p&gt; &lt;p&gt;SwarmPAL is developed by researchers and research software engineers: this ensures a close fit between the development process and the needs of researchers, as well as fostering software skills within the research community. Through several other DISC activities we bring different research teams together, working on different areas of Swarm science, to collaboratively work on the SwarmPAL system. Namely, these activities currently include the TFA (time frequency analysis), and DSECS (dipolar spherical elementary current systems) toolboxes. These toolboxes provide tools to rapidly and configurably apply analyses to Swarm data, while the SwarmPAL package provides the home for these, together with all the maintenance and documentation that this implies. The development process is made with a strong focus on sustainability and open source [4].&lt;/p&gt; &lt;p&gt;[1] https://vires.services&lt;br /&gt;[2] https://hapi-server.org&lt;br /&gt;[3] https://doi.org/10.3389/fspas.2022.1002697&lt;br /&gt;[4] https://github.com/Swarm-DISC/SwarmPAL&lt;/p&gt;

ESA’s Swarm mission is a constellation probing both Earth’s interior and geospace, delivering magnetic and plasma measurements which are used to generate many derived data products. From empirical magnetic field models of the core, crust, ionosphere, and magnetosphere, to multi-point estimates of ionospheric currents and in-situ plasma properties, these are challenging to navigate, process, and visualize. The VirES for Swarm platform (https://vires.services) has been built to tackle this problem, providing tools to increase usability of Swarm data products. The VirES (Virtual environments for Earth Scientists) platform provides both a graphical web interface and an API to access and visualise Swarm data and models. This is extended with a cloud-hosted development environment powered by JupyterHub (the “Virtual Research Environment/VRE”). VirES provides two API’s: the full VirES API for which a dedicated Python client is provided, viresclient, and the more interoperable Heliophysics API (HAPI). The VRE is furnished with a bespoke Python environment containing thematic libraries supporting science with Swarm. This service aims to ease the pathway for scientists writing computer code to analyze Swarm data products, increase opportunities for collaboration, and leverage cloud technologies. Beyond simply providing data and model access to Python users, it is extremely helpful to provide higher-level analysis and visualization tools, and ready-to-use code recipes that people can explore and extend. Critically for space physics, this involves crossover with many other datasets and so it is highly valuable to embed such tools within the wider data and software ecosystems. Through Swarm DISC (Data, Innovation, and Science Cluster), we are tackling this through cookbooks and Python libraries. Cookbooks are built and presented using Jupyter technologies, and tested to work within the VRE. A new library we are building is SwarmPAL, which includes tools for time-frequency analysis and inversion of magnetic field measurements for electric current systems, among others, while relying on the VirES server to provide data portability and other utilities. This paper reviews the current state of these tools and services for Swarm, particularly in the context of the Python in Heliophysics Community, and the wider heliophysics and geospace data environment.