Improving Ionosphere-Thermosphere Data Assimilation through Physics-Based Dynamic Mode Decomposition

Abstract

In this study, we apply the technique of localized dynamic mode decomposition (DMD) to the TIE-GCM (thermosphere-ionosphere-electrodynamics general circulation model) using the OTHITACS (open time-series of the high-resolution ionosphere-thermosphere aeronomic climate simulation) dataset to unravel the ionosphere-thermosphere interaction. Our main goal is to explore the possibility of inferring ionospheric electron density from thermospheric neutral mass density measurements and vice versa. Crucial data from in-situ multi-instrument sampling across satellite constellations is required to advance our knowledge and manage the repercussions of space weather on the ionosphere-thermosphere system. We employ the TIE-GCM-DMD to evaluate a thermosphere-ionosphere data assimilation scheme through observing system simulation experiments. We present experiments utilizing synthetic electron and neutral measurements derived from an operational physics-based model. We validate the functionality and quantify the enhancement in thermosphere-ionosphere modeling by incorporating satellite in-situ observations into the assimilation framework.