Satellite-atmosphere interactions cause large uncertainties in low-Earth orbit determination and prediction. Thus, knowledge of and the ability to predict the space environment, most notably thermospheric mass density, are essential for operating satellites in this domain. Recent progress has been made toward supplanting the existing empirical, operational methods with physics-based data-assimilative models by accounting for the complex relationship between external drivers such as solar irradiance, Joule, and particle heating, and their response in the upper atmosphere. Simultaneously, a new era of CubeSat constellations is set to provide data with which to calibrate our upper-atmosphere models at higher spatial resolution and temporal cadence. With this in mind, we provide an initial method for converting precision orbit determination solutions from global navigation satellite system enabled CubeSats into timeseries of thermospheric mass density. This information is then fused with a physics-based, data-assimilative technique to provide calibrated model densities.

中文翻译：

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使用支持 GNSS 的小型卫星实现基于物理的精确中性密度规范

卫星-大气相互作用导致近地轨道确定和预测存在很大的不确定性。因此，空间环境的知识和预测能力，尤其是热层质量密度，对于在该领域运行卫星至关重要。通过解释太阳辐照度、焦耳和粒子加热等外部驱动因素及其在高层大气中的响应之间的复杂关系，在用基于物理的数据同化模型取代现有的经验、操作方法方面取得了进展。同时，CubeSat 星座的新时代将提供数据，用于以更高的空间分辨率和时间节奏校准我们的高层大气模型。考虑到这一点，我们提供了一种初始方法，用于将全球导航卫星系统启用的立方体卫星的精确轨道确定解决方案转换为热层质量密度的时间序列。然后将此信息与基于物理的数据同化技术融合，以提供校准的模型密度。