Nuggets of MIST science, summarising recent papers from the UK MIST community in a bitesize format.
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By Mai Mai Lam (British Antarctic Survey)
Variations in space weather in the ionized region of the Earth’s atmosphere (the ionosphere) can result in expansion of the atmosphere, increasing the atmospheric drag on objects, such as satellites, in the thermosphere. We aim to significantly improve the forecasting of the effects of atmospheric drag on satellites by more accurate modelling of space weather effects on the motion of ionized particles (plasma) in the ionosphere. We have developed a model of the variation in plasma motion using a small number of solar wind variables. The model was built using a solar cycle’s worth (1997 to 2008 inclusive) of 5-minute resolution Empirical Orthogonal Function (EOF) patterns derived from Super Dual Auroral Radar Network (SuperDARN) line-of-sight observations of the plasma motion in the high-latitude northern hemisphere ionosphere (Shore et al., 2021). The model is driven by four variables: (1) the interplanetary magnetic field component By, (2) the solar wind coupling parameter epsilon, (3) a trigonometric function of the day-of-year, and (4) the monthly solar radio flux at 10.7 cm (the F10.7 index). Our model is good at reproducing the original data set - if 0 indicates that there is no reproduction and 1 indicates exact reproduction, then our model scores 0.7. Data set reproduction is best around the maximum in the solar cycle and worst at solar minimum. This is mainly due to differences in the spatiotemporal data coverage between these times but possibly also due to the model’s specification of the physical processes coupling the Sun to the Earth’s ionosphere. Our model could easily be used to forecast the ionospheric electric field about 1 hour in advance, using the real-time solar wind data available from spacecraft located upstream of the Earth.
Lam, M. M., Shore, R. M., Chisham, G., Freeman, M. P., Grocott, A., Walach, M.-T., & Orr, L. (2023). A model of high latitude ionospheric convection derived from SuperDARN EOF model data. Space Weather, 21, e2023SW003428. https://doi.org/10.1029/2023SW003428
Shore, R. M., Freeman, M., Chisham, G., Lam, M. M., & Breen, P. (2022). Dominant spatial and temporal patterns of horizontal ionospheric plasma velocity variation covering the northern polar region, from 1997.0 to 2009.0 - VERSION 2.0 (Version 2.0) [Dataset]. NERC EDS UK Polar Data Centre. https://doi.org/10.5285/2b9f0e9f-34ec-4467-9e02-abc771070cd9
By Beatriz(University of Leicester)
Space Weather is the discipline that aims at understanding and predicting the state of the Sun, interplanetary medium and its impact on planetary environments. One source of Space Weather is Solar Energetic Particles (SEPs), which are emitted by the Sun and enhance the radiation and particles that flow in space. Predicting the motion of these particles is important but difficult as we need good satellite coverage of the entire inner Solar System, and only a limited number of spacecraft have the necessary instrumentation. Thanks to the European Space Agency flotilla in the solar system, that is, Venus Express, Mars Express, ExoMars-Trace Gas Orbiter, Rosetta, BepiColombo, Solar Orbiter, and Gaia, we performed a feasibility study of the detection of SEP events using engineering sensors in the main body of the spacecraft that were originally placed there to monitor its health during the mission. We explored how much scientific information we can get from these engineering sensors, such as the timing and duration of an SEP impacting the spacecraft, or the minimum energy of those particles to trigger a detection. The results of this study have the potential of providing a good network of solar particle detections at locations where no scientific observations are available.
Please see publication for further details: 2023). Solar energetic particle events detected in the housekeeping data of the European Space Agency's spacecraft flotilla in the Solar System. Space Weather, 21, e2023SW003540. https://doi.org/10.1029/2023SW003540, , , , , , et al. (
By Sandra Chapman (University of Warwick), A. M. Bendito Nunes (undergraduate student, University of Warwick), and J. Gamper (undergraduate student, University of Warwick)
Space weather can have significant impact over a wide range of technological systems including power grids, aviation, satellites and communications. In common with studies across the geophysical sciences, space weather modelling and prediction requires long term space and ground-based parameters and indices that necessarily aggregate multiple observations, the details of which can change with time. The Newcomb-Benford law (NBL) specifies the relative occurrence rates of the leading digit in a sequence of numbers arising from multiple operations under certain conditions, the first non-zero digit in a number is more likely to be 1 than 2, 2 than 3, and so on. In this first application to space weather parameters and indices, we show that the NBL can detect changes in the instrumentation and calibration underlying long-term geophysical records, solely from the processed data records. In space weather, as in other fields such as climate change, it is critical to be able to verify that any observed secular change is not a result of changes in how the data record is constructed. As composite indices are becoming more widespread across the geosciences, the NBL may provide a generic data flag indicating changes in the constituent raw data, calibration or sampling method.
Figure 1: The plot shows the NBL goodness of fit parameter for magnetic field observed since 1981 by a series of satellites upstream of the earth. The NBL fit parameter shows a clear decrease when more sophisticated satellites, Wind. and later ACE, became available.
The joint 1st authors of this paper contributed to this research during their final year undergraduate Physics project at Warwick University
See paper for full details:
A. M. Benedito Nunes, J. Gamper, S. C. Chapman, M. Friel, J. Gjerloev, Newcomb-Benford Law as a generic flag for changes in the derivation of long-term solar terrestrial physics timeseries, RAS Techniques and Instruments (2023) https://doi.org/10.1093/rasti/rzad041
By Sachin Reddy (UCL Mullard Space Science Laboratory)
In the nightside ionosphere, plumes of low-density plasma known as Equatorial Plasma Bubbles (EPBs) are prone to form. EPBs can disrupt GNSS signals which depend on quiet ionospheric conditions, but the day-to-day variability of bubbles has made predicting them a considerable challenge. In this study we present AI Prediction of EPBs (APE), a machine learning model that accurately predicts the Ionospheric Bubble Index (IBI) on Swarm. IBI identifies EPBs by correlating (R2) a simultaneous change in the current density and magnetic field.
APE is XGBoost regressor that is trained on data from 2014-2022. It performs well across all metrics, exhibiting a skill, association, and root mean squared error score of 0.96/1, 0.98/1 and 0.08/0 respectively. APE performs best post-sunset, in the American/Atlantic sector, around the equinoxes, and when solar activity is high. This is promising because EPBs are most likely to occur during these periods.
Shapley Value analysis reveals that F10.7 is the most important feature, whilst latitude is the least. Bespoke indices may be required to fully capture the effects of geomagnetic activity which is known to both enhance and suppress EPB formation. The Shapley analysis also reveals that low solar activity, active geomagnetic conditions, and the Earth-Sun perihelion all contribute to an increased EPB likelihood. To the best of our knowledge, this is the first time this exact combination of features has been linked to bubble detection. This showcases the ability of Shapley values to enable new insights into EPB climatology and predictability.
See full paper for details: 2023). Predicting swarm equatorial plasma bubbles via machine learning and Shapley values. Journal of Geophysical Research: Space Physics, 128, e2022JA031183. https://doi.org/10.1029/2022JA031183, , , , , , et al. (
By Shahbaz Chaudhry (University of Warwick)
Space weather poses a risk to infrastructure including satellites and power systems. A key challenge within space weather is predicting the magnetospheric response during storms. In order to understand the dynamics of geomagnetic storms, we can study Pc waves which are field line resonances along closed field lines in the inner magnetosphere. Recently, SuperMAG and Intermagnet have released new second resolution data which allows higher frequency Pc2 (T=5-10s) waves to be resolved and studied globally. Generation mechanisms for Pc2 waves (which we focus on in this paper) include ion-cyclotron resonance at equatorial regions of the magnetosphere.
To better understand geomagnetic storms, we for the first time build a Pc2 wave dynamical network using the full set of 100+ ground-based magnetometer stations. A network graphs the connections (edges) between entities (nodes). An example includes airline networks, where the nodes are airports and edges are flight paths. Here we build dynamical networks where nodes and edges are time varying. Network edges will be built upon the cross-correlation between Pc2 waves observed magnetic field at pairs of ground-based magnetometer stations.
Our first results are a study of the 2015 St. Patrick's Day storm for an 8 hour time window around onset. Using this storm we have identified network parameters and have shown that these track the distinct phases of the storm in terms of spatial coherence of Pc2 wave activity. We show that the network responds to distinct phase of the storm, including southward or northward IMF and does not just track the average Pc2 power. Using these network parameters we can perform statistical studies across many storms and quantitatively benchmark space weather models with observations. In addition, this analysis can be easily extended to other Pc bands which have different generation mechanisms within the magnetosphere.
See paper for full details: 2023). Global dynamical network of the spatially correlated Pc2 wave response for the 2015 St. Patrick's Day storm. Journal of Geophysical Research: Space Physics, 128, e2022JA031175. https://doi.org/10.1029/2022JA031175, , , & (