Improved Neutral Density Predictions Through Machine Learning Enabled Exospheric Temperature Model

Abstract

AbstractThe community has leveraged satellite accelerometer data sets in previous years to estimate neutral mass density and exospheric temperatures. We utilize derived temperature data and optimize a nonlinear machine‐learned (ML) regression model to improve upon the performance of the linear EXospheric TEMPeratures on a PoLyhedrAl gRid (EXTEMPLAR) model. The newly developed EXTEMPLAR‐ML model allows for exospheric temperature predictions at any location with one model and provides performance improvements over its predecessor. We achieve reductions in mean absolute error of 2 K on an independent test set while providing similar error standard deviation values. Comparing the performance of both EXTEMPLAR models and the Naval Research Laboratory Mass Spectrometer and Incoherent Scatter radar Extended model (NRLMSISE‐00) across different solar and geomagnetic activity levels shows that EXTEMPLAR‐ML has the lowest mean absolute error across 80% of conditions tested. A study for spatial errors demonstrated that at all grid locations, EXTEMPLAR‐ML has the lowest mean absolute error for over 60% of the polyhedral grid cells on the test set. Like EXTEMPLAR, our model's outputs can be utilized by NRLMSISE‐00 (exclusively) to more closely match satellite accelerometer‐derived densities. We conducted 10 case studies where we compare the accelerometer‐derived temperature and density estimates from four satellites to NRLMSISE‐00, EXTEMPLAR, and EXTEMPALR‐ML during major storm periods. These comparisons show that EXTEMPLAR‐ML generally has the best performance of the three models during storms. We use principal component analysis on EXTEMPLAR‐ML outputs to verify the physical response of the model to its drivers.