基于数据的实时热层密度估计框架
Data-driven framework for real-time thermospheric density estimation
AI总结：
本文提出一种基于数据的实时中性密度估计框架，通过准物理电离层热层模型中的模型-数据融合实现。框架包含准物理动态降阶模型的开发和动态校准，用于实时更新高层大气状态。
摘要：
本文展示了一种新的基于数据的实时中性密度估计框架，通过准物理电离层热层模型中的模型-数据融合实现。该框架有两个主要组成部分：(i) 开发一个准物理动态降阶模型（ROM），该模型使用底层动力学的线性近似和驱动因素的影响；(ii) 通过估计代表模型参数的ROM系数对ROM进行动态校准。我们之前已利用物理模型的模拟输出和非操作性密度估计（来自单轨道上的加速度计测量）展示了准物理ROM的开发。在本文中，我们展示了该框架在使用操作性测量时的潜力。我们使用具有5分钟分辨率的模拟GPS导出轨道历元作为测量数据。该框架是首个此类简单而稳健且准确的方法，具有为利用具有内在预测/预报能力的准物理模型提供实时操作更新的高潜力。
英文摘要：
In this paper, we demonstrate a new data-driven framework for real-time neutral density estimation via model-data fusion in quasi-physical ionosphere-thermosphere 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) dynamic calibration of the ROM through estimation of the ROM coefficients that represent the model parameters. We have previously demonstrated the development of a quasi-physical ROM using simulation output from a physical model and assimilation of non-operational density estimates derived from accelerometer measurements along a single orbit. In this paper, we demonstrate the potential of the framework for use with operational measurements. We use simulated GPS-derived orbit ephemerides with 5 minute resolution as measurements. 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 using quasi-physical models with inherent forecasting/predictive capabilities.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
