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

TL;DR

This paper develops a physics-informed, reduced-order data assimilation model for thermospheric density, improving orbit prediction accuracy by coupling a data-driven model with observational data.

Contribution

It introduces a novel SINDy$_c$-AR reduced-order model coupled with a Kalman filter for efficient thermospheric density estimation from in situ observations.

Findings

Assimilation reduces density estimation error during geomagnetic storms.

SINDy$_c$-AR performs comparably to DMDc on trained orbits and better on unseen orbits.

The model outperforms open-loop predictions and provides improvements over empirical models.

Abstract

Accurate estimation of thermospheric mass density is a prerequisite for orbit prediction and space situational awareness, where the upper atmosphere responds nonlinearly to solar and geomagnetic forcing across several orders of magnitude. Physics-based general circulation models resolve this response but are computationally expensive, while empirical models run cheaply but lack a time-evolving atmospheric state. This work couples a data-driven reduced-order thermospheric model with a Kalman filter that assimilates in situ density observations. An autoregressive Sparse Identification of Nonlinear Dynamics with control (SINDy$_c$-AR) reduced-order model derived from the Thermosphere-Ionosphere-Electrodynamics General Circulation Model (TIE-GCM) captures the dominant modes of variability and their dependence on solar and geomagnetic drivers at a fraction of the parent model's cost. Density observations from CHAMP, GRACE, GRACE-FO, GOCE, and Swarm are assimilated across a range of orbital configurations and geomagnetic conditions, with a linear DMDc model evaluated as a reference. Assimilation reduces density estimation error relative to open-loop predictions, most visibly during geomagnetic storms and under single-satellite coverage. SINDy$_c$-AR and DMDc perform comparably on assimilated orbits; on withheld orbits, SINDy$_c$-AR is more accurate in the in-training scenarios while DMDc is better in the out-of-training 2024 Swarm-C case. Benchmarks against NRLMSIS~2.1 and HASDM (2000--2019, where available) show that empirical references can outperform the assimilated model far from the assimilated track, so results are framed as improvements over the open-loop forecast.