Machine-Learned HASDM Thermospheric Mass Density Model With Uncertainty Quantification

Abstract

A thermospheric neutral mass density model with robust and reliable uncertainty estimates is developed based on the Space Environment Technologies (SET) High Accuracy Satellite Drag Model (HASDM) density database. This database, created by SET, contains 20 years of outputs from the U.S. Space Force's HASDM, which currently represents the state of the art for density and drag modeling. We utilize principal component analysis for dimensionality reduction, which creates the coefficients upon which nonlinear machine-learned (ML) regression models are trained. These models use three unique loss functions: Mean square error (MSE), negative logarithm of predictive density (NLPD), and continuous ranked probability score. Three input sets are also tested, showing improved performance when introducing time histories for geomagnetic indices. These models leverage Monte Carlo dropout to provide uncertainty estimates, and the use of the NLPD loss function results in well-calibrated uncertainty estimates while only increasing error by 0.25% (<10% mean absolute error) relative to MSE. By comparing the best HASDM-ML model to the HASDM database along satellite orbits, we found that the model provides robust and reliable density uncertainties over diverse space weather conditions. A storm-time comparison shows that HASDM-ML also supplies meaningful uncertainty estimates during extreme geomagnetic events.