The current state and future directions of modeling thermosphere density enhancements during extreme magnetic storms

TL;DR

This paper reviews the current understanding of thermosphere density enhancements during extreme magnetic storms, emphasizing the importance of accurate modeling for satellite safety and proposing future research directions involving data assimilation and historical datasets.

Contribution

It summarizes recent achievements in modeling thermosphere response to magnetic storms and discusses how data assimilation and historical data can improve future density predictions.

Findings

Empirical models with data assimilation outperform those without during storm phases.

Forecasting models can be improved by incorporating historical extreme storm datasets.

Large-scale thermosphere data assimilation can enhance future density predictions.

Abstract

Satellites, crewed spacecraft and stations in low-Earth orbit (LEO) are very sensitive to atmospheric drag. A satellite's lifetime and orbital tracking become increasingly inaccurate or uncertain during magnetic storms. Given the planned increase of government and private satellite presence in LEO, the need for accurate density predictions for collision avoidance and lifetime optimization, particularly during extreme events, has become an urgent matter and requires comprehensive international collaboration. Additionally, long-term solar activity models and historical data suggest that solar activity will significantly increase in the following years and decades. In this article, we briefly summarize the main achievements in the research of thermosphere response to extreme magnetic storms occurring particularly after the launching of many satellites with state-of-the-art accelerometers from which high-accuracy density can be determined. We find that the performance of an empirical model with data assimilation is higher than its performance without data assimilation during all extreme storm phases. We discuss how forecasting models can be improved by looking into two directions: first, to the past, by adapting historical extreme storm datasets for density predictions, and second, to the future, by facilitating the assimilation of large-scale thermosphere data sets that will be collected in future events. Therefore, this topic is relevant to the scientific community, government agencies that operate satellites, and the private sector with assets operating in LEO.