A new transformative framework for data assimilation and calibration of physical ionosphere-thermosphere models

TL;DR

This paper introduces a novel framework combining a quasi-physical reduced order model with data assimilation for real-time, accurate calibration and prediction of the ionosphere-thermosphere environment, enhancing space weather modeling.

Contribution

It develops a dynamic reduced order model for the IT system and introduces a parameter estimation approach for calibration, improving real-time prediction and uncertainty quantification.

Findings

Validated with CHAMP and GOCE data

Achieved real-time operational updates

Enhanced model calibration without continuous data

Abstract

Accurate specification and prediction of the ionosphere-thermosphere (IT) environment, driven by external forcing, is crucial to the space community. In this work, we present a new transformative framework for data assimilation and calibration of the physical IT models. The framework has two main components: (i) the development of a quasi-physical dynamic reduced order model (ROM) that uses a linear approximation of the underlying dynamics and effect of the drivers, and (ii) data assimilation and calibration of the ROM through estimation of the ROM coefficients that represent the model parameters. A reduced order surrogate for thermospheric mass density from the Thermosphere Ionosphere Electrodynamic General Circulation Model (TIE-GCM) was developed in previous work. This work concentrates on the second component of the framework - data assimilation and calibration of the TIE-GCM ROM. The new framework has two major advantages: (i) a dynamic ROM that combines the speed of empirical models for real-time capabilities with the predictive capabilities of physical models which has the potential to facilitate improved uncertainty quantification (UQ) using large ensembles, and (ii) estimation of model parameters rather than the driver(s)/input(s) which allows calibration of the model, thus avoiding degradation of model performance in the absence of continuous data. We validate the framework using accelerometer-derived density estimates from CHAMP and GOCE. The framework is a first of its kind, simple yet robust and accurate method with high potential for providing real-time operational updates to the state of the upper atmosphere in the context of drag modeling for Space Situational Awareness and Space Traffic Management.