In this section
@article{Klingenstein:2025:10.5194/angeo-43-835-2025,
author = {Klingenstein, L and Grimmich, N and Shprits, YY and P枚ppelwerth, A and Plaschke, F},
doi = {10.5194/angeo-43-835-2025},
journal = {Annales Geophysicae},
pages = {835--854},
title = {Parameterization of the subsolar standoff distance of Earth’s magnetopause based on results from machine learning},
url = {http://dx.doi.org/10.5194/angeo-43-835-2025},
volume = {43},
year = {2025}
}
TY - JOUR
AB - The subsolar standoff distance r<inf>0</inf> of Earth’s magnetopause is a key parameter in understanding the interaction between the solar wind and the magnetosphere. Despite decades of modeling efforts, significant uncertainties persist between model predictions and satellite observation of the magnetopause location. This study introduces a new data-driven parameterization of r<inf>0</inf>, based on a dataset containing over 220,000 dayside magnetopause crossings obtained by the THEMIS (2007–2022) and Cluster (2001–2020) missions. Each crossing is paired with high-resolution upstream solar wind parameters from the OMNI database. Four established empirical models are benchmarked against this dataset, yielding root-mean-square errors (RMSE) of & 1 R<inf>E</inf> globally and & 0.8 R<inf>E</inf> in the subsolar region. To determine the primary physical factors of r<inf>0</inf>, an XGBoost regression model is trained and interpreted using SHapley Additive exPlanation (SHAP) values. The solar wind dynamic pressure is found to be the dominant contributor, followed by geomagnetic indices (AE, SYMH), interplanetary magnetic field (IMF) magnitude, dipole tilt angle, and IMF cone angle. The IMF B<inf>z</inf> component contributes only marginally when geomagnetic indices are included. A support vector regression (SVR) model using the six most influential parameters achieves a RMSE of 0.68 R<inf>E</inf>, improving on the best analytic model by approximately 17 %. A second-order polynomial expression with 14 terms is derived, providing a compact, interpretable, and accurate representation of r<inf>0</inf>. The SVR model and the polynomial representation is not able to predict r<inf>0</inf> for extreme input conditions, e.g., during the passage of interplanetary coronal mass ejections. Accordingly, the parameter ranges that define the validity doma
AU - Klingenstein,L
AU - Grimmich,N
AU - Shprits,YY
AU - P枚ppelwerth,A
AU - Plaschke,F
DO - 10.5194/angeo-43-835-2025
EP - 854
PY - 2025///
SN - 0992-7689
SP - 835
TI - Parameterization of the subsolar standoff distance of Earth’s magnetopause based on results from machine learning
T2 - Annales Geophysicae
UR - http://dx.doi.org/10.5194/angeo-43-835-2025
VL - 43
ER -