MIST

By Sophia Zomerdijk-Russell (Imperial College London)

The interior structure of a magnetised planet can be determined by using electromagnetic induction processes that results from solar-wind-driven magnetopause variability. To determine a profile of conductivity through depth within a planet, a broad spectrum of inducing fields is needed, as each discrete frequency will probe to a certain depth.

In preparation for the arrival of BepiColombo at Mercury in 2025, we have identified the opportunity to use Helios data to assess how solar wind ram pressure forcing can drive magnetopause variability at Mercury, as Helios took measurements during a similar phase of the Solar Cycle that BepiColombo is expected to see on its arrival. We find that Mercury’s magnetosphere is bombarded by a highly variable and unpredictable solar wind with a broad range of frequency signals and that the inducing field generated in response to the variable solar wind ram pressure is non-uniform across the planet’s surface.

A solar wind ram pressure time series from Helios measurements and the KT17 Hermean magnetospheric field model (Korth et al., 2017) were then used to generate a ram pressure driven inducing field spectra at two points on Mercury’s surface. In power spectra of these example inducing field spectra, frequency signals were found to peak between ~5.510^-5 and 1.510^-2 Hz. Heyner et al. (2021) determined that signals with these frequencies should penetrate into Mercury’s crust and mantle.

Particular orbital configurations of the BepiColombo mission will have MPO inside Mercury’s magnetosphere and Mio measuring the upstream solar wind, see Figure 1. Therefore, the dual spacecraft nature of the BepiColombo mission will be well suited to investigate Mercury’s magnetosphere’s response to external solar wind variability and allow a conductivity profile through to the mantle to be derived from observations of solar wind driven inducing field spectra with timescales seen in this work.

Original article for further detail:

Zomerdijk-Russell, S., Masters, A., Korth, H., & Heyner, D. (2023). Modeling the time-dependent magnetic fields that BepiColombo will use to probe down into Mercury's mantle. Geophysical Research Letters, 50, e2022GL101607. https://doi.org/10.1029/2022GL101607

References: 

Heyner, D., Auster, H.-U., Fornaçon, K.-H., Carr, C., Richter, I., Mieth, J. Z. D., Kolhey, P., Exner, W., Motschmann, U., Baumjohann, W., Matsuoka, A., Magnes, W., Berghofer, G., Fischer, D., Plaschke, F., Nakamura, R., Narita, Y., Delva, M., Volwerk, M., … Glassmeier, K.-H. (2021). The BepiColombo Planetary Magnetometer MPO-MAG: What Can We Learn from the Hermean Magnetic Field? Space Science Reviews, 217(4), 52. https://doi.org/10.1007/s11214-021-00822-x

Korth, H., Johnson, C. L., Philpott, L., Tsyganenko, N. A., & Anderson, B. J. (2017). A Dynamic Model of Mercury’s Magnetospheric Magnetic Field. Geophysical Research Letters, 44(20), 10,147-10,154. https://doi.org/10.1002/2017GL074699

By Tom Daggitt (British Antarctic Survey, University of Cambridge)

Geostationary satellites may cross the magnetopause during highly active times when it can be compressed inside geostationary orbit. At this time the satellite will switch from observing Earth’s magnetic field to the interplanetary magnetic field, which is usually opposing Earth’s field during compressions. The satellite will observe a drop in electron flux as it goes from measuring trapped electrons in Earth’s radiation belts to electrons in the interplanetary medium.

We compare observations from the GOES-13 and GOES-15 satellites during geomagnetic storms. We attempt to predict magnetopause crossings using models of the last closed drift shell (LCDS), the outermost stable orbit for an electron trapped in Earth’s magnetic field. The LCDS is modelled as the largest L* value that calculated by the IRBEM magnetic field modelling library (Boscher et al., 2013), using a method derived from Albert et al. (2018).

Figure 1(A) shows the B_z component of the field measured by the GOES magnetometers, demonstrating independent magnetopause crossings when the B_z component is negative. Each satellite only sees a magnetopause crossing when it is nearer local noon.

1(B) shows the satellite L* and the LCDS derived from the TS05 field model (Tsyganenko & Sitnov, 2005). Neither satellite crosses the LCDS during their magnetopause crossings, showing that brief crossings cannot be predicted with this method.

1(C) shows the >0.8MeV flux measured by the GOES satellites, showing rapid decreases in the measured flux associated with each magnetopause crossing. GOES-13 also shows a large decrease in flux as it moves into the nightside, likely due to distortion in the magnetotail.

The difference in the observed flux profiles demonstrates that choice of satellite may have a large effect when using satellite data to drive radiation belt models. Data from multiple satellites should be used to ensure constant measurements of the trapped flux on the dayside when driving radiation belt models.

References:

Boscher, D., Bourdarie, S., O’Brien, P., & Guild, T. (2013). Irbem library v4.3, 2004-2008. https://spacepy.github.io/irbempy.html.

Albert, J. M., Selesnick, R., Morley, S. K., Henderson, M. G., & Kellerman, A. (2018). Calculation of last closed drift shells for the 2013 gem radiation belt challenge events. Journal of Geophysical Research: Space Physics, 123(11), 9597– 9611. https://doi.org/10.1029/2018JA025991

Tsyganenko, N., & Sitnov, M. (2005). Modeling the dynamics of the inner magnetosphere during strong geomagnetic storms. Journal of Geophysical Research, 110(A3), A03208. https://doi.org/10.1029/2004JA010798

Associated Paper:

Daggitt, T. A., Horne, R. B., Glauert, S. A., Del Zanna, G., & Freeman, M. P. (2022). Variations in observations of geosynchronous magnetopause and last closed drift shell crossings with magnetic local time. Space Weather, 20, e2022SW003105. https://doi.org/10.1029/2022SW003105

By Kaine Bunting (Aberystwyth University)

Tomography is used to gain the 3D plasma density of the solar corona (between 4-8Rs) using white light coronagraph observations (Morgan and Cook 2019). In this work, we use the density at 8Rs (e.g. figure 1a) to estimate velocity, providing an inner boundary condition for a heliospheric solar wind model. The results show that tomography can provide a valid (or improved) alternative to traditional photospheric-based magnetic models.

A simple inverse linear relationship converts densities to velocities. Extracted from Earth's latitude, the velocities form an inner boundary condition for the Time-dependent Heliospheric Upwind Extrapolation (HUXt) model (Owens et al. 2020). The model is then simulated for one Carrington rotation (CR), giving predicted velocities at Earth. The modelled velocities are compared to OMNI in-situ data, and the efficiency of HUXt allows an iterative fitting method optimises the parameters of the density-velocity conversion model by minimising a ‘distance’ metric using Dynamic Time Warping (DTW, Samara et al. 2022).

Figure 1 b) compares the output of HUXt based on Tomography-HUXt and MAS inner boundaries (Linker et al. 1999), where the MAS velocities have been optimised similarly to the tomography velocities for CR2210 (Nov 2018). Tomography-HUXt model outperforms the MAS-HUXt model: tomography-HUXt gives a MAE of 47.9 km s^-1 compared to the 53.58 km s^-1 of the MAS-HUXt model, and a higher Pearson correlation coefficient of 0.74 compared to that of MAS-HUXt (0.56). This work shows that inner boundary conditions derived using tomography are a valid alternative to magnetic models such as MAS.

Our current work investigates a more advanced relationship between coronal densities and velocities, and provides improved solar wind predictions over a whole solar cycle. This scheme is implemented as part of the SWEEP software suite for the UK Met Office's space weather forecasting capabilities from 2023 onwards.

References:

Linker, J. A., Z. Miki ́c, D. A. Biesecker, R. J. Forsyth, S. E. Gibson, et al. 1999. Magnetohydrodynamic modeling of the solar corona during Whole Sun Month. Journal of Geophysical Research: Space Physics, 104. 10.1029/1998ja900159.

Morgan, H., and A. C. Cook, 2020. The Width, Density, and Outflow of Solar Coronal Streamers. The Astrophysical Journal, 893(1), 57. 10.3847/1538-4357/ab7e32

Owens, M. J., M. Lang, L. Barnard, P. Riley, M. Ben-Nun, et al. 2020. A Computationally Efficient, Time-Dependent Model of the Solar Wind for Use as a Surrogate to Three-Dimensional Numerical Magnetohydrodynamic Simulations. Solar Physics. 10.1007/s11207-020-01605-3

Samara, E., B. Laperre, R. Kieokaew, M. Temmer, C. Verbeke, et al. 2022. Dynamic Time Warping as a Means of Assessing Solar Wind Time Series. The Astrophysical Journal, 927(2), 187. 10.3847/1538-4357/ac4af6

By Clare Watt (Northumbria University)

Our work here combines observations and ensemble numerical simulations to study the effect of temporal variability in radiation belt physics. Kinetic wave-particle interactions in Earth’s outer radiation belt are an effective way to energize and scatter high-energy electrons. A Fokker-Planck equation is often used to model the wave-particle interactions, and we studied the way it responds to the temporal variation of the quasi-linear diffusion coefficient in the case of pitch-angle diffusion due to plasmaspheric hiss. We used stochastic parameterisation [Berner et al., 2017] to describe the temporal evolution of hiss diffusion coefficients in ensemble numerical experiments, guided throughout by observations of wave activity and plasma parameters from the NASA Van Allen Probes. Ensemble solutions of the Fokker-Planck equation depend significantly on the timescale of variation, varied here between minutes and hours. We analysed timescales over which it is useful to construct diffusion coefficients and conclude that there is a useful maximum averaging timescale that should be used to construct a diffusion coefficient from observations. This timescale is likely less than the orbital period of most inner magnetospheric missions. Although arithmetic averaging of inputs to diffusion coefficients is not recommended [Watt et al., 2019], arithmetic drift-averaging of the diffusion coefficients themselves can be appropriate in some cases. We found that in some locations, rare but large values of the diffusion coefficient occur during periods of relatively low number density. Ensemble solutions are sensitive to the presence of these rare values, supporting the need for accurate cold plasma density models in radiation belt descriptions.

Figure 1: Ensemble results for numerical diffusion experiments using D_αα(L* = 3). Each panel shows a column-normalised probability distribution function for the phase space density f just outside the loss-cone α_LC for (A) Δt = 2 min, (B) Δt = 10 min, (C) Δt = 30 min, (D) Δt = 2 h, (E) Δt = 4 h, and (F) Δt = 6 h. Note that each histogram is displayed using the same vertical binning, giving the histograms a pixelated appearance.

References:

Berner, J., et al. (2017) Stochastic parameterization: towards a new view of weather and climate models. Bulletin of the American Meteorological Society, 98 (3). pp. 565-588. ISSN 1520-0477 doi: https://doi.org/10.1175/BAMS-D-15-00268.1

Watt, C. E. J., Allison, H. J., Meredith, N. P., Thompson, R. L., Bentley, S. N., Rae, I. J., et al. (2019). Variability of quasilinear diffusion coefficients for plasmaspheric hiss. J. Geophys. Res. Space Phys. 124, 8488–8506. doi:10.1029/2018ja026401

Associated Paper:

Watt, C. E. J., Allison, H. J., Bentley, S. N., Thompson, R. L., Rae, I. J., Allanson, O., … Killey, S. (2022). Temporal variability of quasi-linear pitch-angle diffusion. Frontiers in Astronomy and Space Sciences. https://www.frontiersin.org/articles/10.3389/fspas.2022.1004634