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The first comparison between Swarm-C accelerometer-derived thermospheric densities and physical and empirical model estimates

机译:swarm-C加速度计衍生的热力学之间的第一次比较   密度和物理和经验模型估计

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摘要

First systematic comparison between Swarm-C accelerometer-derivedthermospheric density against both empirical and physics-based model resultsusing multiple model performance metrics is presented. This comparison isperformed at the satellite's high temporal resolution, which provides ameaningful evaluation of the models' fidelity for orbit prediction and otherspace weather forecasting applications. The comparison against the physicalmodel is influenced by the specification of the lower atmospheric forcing, thehigh-latitude ionospheric plasma convection and solar activity. Amachine-learning exercise is also used to isolate the impact ofthermosphere-driving mechanisms. The results show that the short-timescalevariations observed by Swarm-C during periods of high solar and geomagneticactivity were better captured by the physics-based model as compared to theempirical model in this analysis. It is concluded that Swarm-C data agree wellwith the climatologies inherent within the models, and is, therefore, a usefuldataset for further model validation and scientific research.
机译:提出了使用多个模型性能指标将Swarm-C加速度计衍生的热层密度与基于经验和基于物理的模型结果进行系统比较的方法。该比较是在卫星的高时间分辨率下进行的,该分辨率为轨道预测和其他空间天气预报应用提供了模型保真度的有意义评估。与物理模型的比较受较低的大气强迫,高纬度电离层等离子体对流和太阳活动规格的影响。机器学习练习也用于隔离热球驱动机制的影响。结果表明,与基于经验的模型相比,基于物理学的模型可以更好地捕获Swarm-C在高太阳和地磁活动期间观测到的短时尺度变化。结论是,Swarm-C数据与模型内在的气候条件非常吻合,因此,它是用于进一步模型验证和科学研究的有用数据集。

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