A New Four‐Component L*‐Dependent Model for Radial Diffusion Based on Solar Wind and Magnetospheric Drivers of ULF Waves

Abstract

Waves which couple to energetic electrons are particularly important in space weather, as they drive rapid changes in the topology and intensity of Earth's outer radiation belt during geomagnetic storms. This includes Ultra Low Frequency (ULF) waves that interact with electrons via radial diffusion which can lead to electron dropouts via outward transport and rapid electron acceleration via inward transport. In radiation belt simulations, the strength of this interaction is specified by ULF wave radial diffusion coefficients. In this paper we detail the development of new models of electric and magnetic radial diffusion coefficients derived from in‐situ observations of the azimuthal electric field and compressional magnetic field. The new models use L∗ ${L}^{\ast }$ as it accounts for adiabatic changes due to the dynamic magnetic field coupled with an optimized set of four components of solar wind and geomagnetic activity, Bz ${B}_{z}$, V $V$, Pnormaldnormalynormaln ${P}_{\mathrm{d}\mathrm{y}\mathrm{n}}$, and normalSnormalynormalm−H $\mathrm{S}\mathrm{y}\mathrm{m}-H$, as independent variables (inputs). These independent variables are known drivers of ULF waves and offer the ability to calculate diffusion coefficients at a higher cadence then existing models based on Kp. We investigate the performance of the new models by characterizing the model residuals as a function of each independent variable and by comparing to existing radial diffusion models during a quiet geomagnetic period and through a geomagnetic storm. We find that the models developed here perform well under varying levels of activity and have a larger slope or steeper gradient as a function of L∗ ${L}^{\ast }$ as compared to existing models (higher diffusion at higher L∗ ${L}^{\ast }$ values).

Rapid Acceleration Bursts in the Van Allen Radiation Belt

“…(2014) and Lejosne (2020), as well as a more complex model that depends on solar wind speed, dynamic pressure, IMF B z , and SYM‐H index by Murphy et al. (2023). Figure 5b shows the comparison of the GPS D LL with event‐specific D LL coefficients provided by Lejosne (2020) (found in the supporting information to their paper) for the three electron energies, that correspond to the selected first adiabatic invariants (see Section 3 for more details).…”

Van Allen Probes Observations of a Three‐Dimensional Field Line Resonance at a Plasmaspheric Plume

“…We use magnetic field power spectral density as derived by Murphy et al. (2023), which uses a Fourier Transform over a 20 min sliding (by 5 min) window. We note that azimuthal electric field observations of the ULF wave activity were not viable for this event, due to high errors in the spin axis electric field from the Electric Fields and Waves instrument (Wygant et al., 2013).…”

On the Performance of a Real-Time Electron Radiation Belt Specification Model

“…However, we chose not to modify the forecast model until a comprehensive analysis of hindcast performance was conducted. Since less observational data is now available to constrain the hindcast via data assimilation, the diffusion simulation should be improved by updating the precomputed diffusion coefficients using more modern methodologies of representing Chorus (e.g., Wong et al, 2024), Hiss (e.g., Agapitov et al, 2020;Watt et al, 2019 ), and ULF waves (e.g., Kyle R. Murphy et al, 2023). The diffusive effects of EMIC waves could also be incorporated (e.g., Ross et al, 2020) to improve the representation of electron loss in the inner magnetosphere.…”

“…Overestimation of PSD in the plasmasphere could be addressed by evaluating more recent diffusion coefficients computed for Plasmaspheric Hiss (e.g., Agapitov et al, 2020;Watt et al, 2019). Improved representation of electron loss at geostationary orbit during the storm main phase could be incorporated by using a dynamic outer boundary of the simulation (Bloch et al, 2021;Staples et al, 2020) and evaluating new radial diffusion coefficients (Kyle R. Murphy et al, 2023). Updating the radial diffusion coefficients could also improve hindcast with sparse realtime data by accurately propagating the effects of assimilated data across L*.…”