Random Forest Model of Ultralow‐Frequency Magnetospheric Wave Power

Abstract

Abstract Models of magnetospheric ultralow‐frequency (ULF) waves can be used to study wave phenomena and to calculate the effect of these waves on the energization and transport of radiation belt electrons. We present a decision tree ensemble (i.e., a random forest) model of ground‐based ULF wave power spectral density driven by solar wind speed vsw, north‐south component of the interplanetary magnetic field Bz, and variance of proton number density var(Np). This model corresponds to four radial locations in the magnetosphere (roughly L ∼ 4.21 to 7.94) and spans 1–15 mHz, with hourly magnetic local time resolution. The decision tree ensembles are easier to use than the previous model generation; they have better coverage, perform better at predicting power, and have reduced error due to intelligently chosen bins in parameter space. We outline the difficulties in extracting physics from parameterized models and demonstrate a hypothesis testing framework to iteratively explore finer driving processes. We confirm a regime change for ULF driving about Bz = 0. We posit that ULF wave power directly driven by magnetopause perturbations corresponds to a latitude‐dependent dawn‐dusk asymmetry, which we see with increasing speed. Model uncertainty identifies where the underlying physics is not fully captured; we find that power due to substorms is less well characterized by Bz > 0, with an effect that is seen globally and not just near midnight. The largest uncertainty is seen for the smallest amounts of solar wind driving, suggesting that internal magnetospheric properties may play a significant role in ULF wave occurrence.

Keywords

• ULF waves
• radiation belt
• machine learning
• space weather
• magnetosphere
• ensemble