Reduced-Order Data Assimilation for Thermospheric Density Using Physics-informed SINDyc Models

Abstract

Orbit prediction and space situational awareness require accurate thermospheric mass density, which responds nonlinearly to solar and geomagnetic forcing across several orders of magnitude. Physics-based general circulation models resolve that response but are computationally expensive, while empirical models run cheaply and carry no time-evolving atmospheric state. An autoregressive Sparse Identification of Nonlinear Dynamics with control (SINDyc-AR) model, derived from the Thermosphere-Ionosphere-Electrodynamics General Circulation Model (TIE-GCM), captures the dominant modes of variability and their dependence on the drivers at a fraction of the parent model's cost. An extended Kalman filter assimilates in situ observations from CHAMP, GRACE, GRACE-FO, GOCE, and Swarm into that model across several orbital configurations and geomagnetic conditions, with a linear DMDc model as reference. Assimilation reduces density error relative to open-loop prediction, most visibly during geomagnetic storms and under single-satellite coverage. The two reduced-order models perform comparably throughout. NRLMSIS 2.1 and HASDM can outperform the assimilated model far from the assimilated track, so results are framed as improvements over the open-loop forecast. An observation-calibrated global density record spanning August 2000 to December 2025 is released alongside this work.