Co-estimation of the Earth main magnetic field and the ionospheric variation field

该项目的目的是共同估计地核和电离层磁场的模型，从长远来看是建立一个地球磁场的“综合”模型。在这第一步中，我们希望利用全球M-I-T建模在了解电离层方面取得的进展，以便更好地区分卫星数据中的核心信号和电离层信号。电离层中产生的磁信号特别难以处理，因为卫星数据每次仅提供非常窄的本地时间窗口的信息。为了解决这个困难，我们想应用一种从同化方法中衍生出来的技术，这种技术已经成功地应用于外核流研究。该技术依赖于电离层的理论模型，如高层大气模型（UAM），其中估计了与简单背景模型的偏差统计数据。然后，以协方差矩阵格式提供的导出的统计数据可以直接用于磁数据反演过程，以获得预期的核心和电离层模型。我们计划应用该技术的德国CHAMP卫星数据选择磁安静的时间。作为一个输出，我们应该得到一个电离层磁变化场的模型为选定的数据和核心岩石圈场模型，其中可能的泄漏电离层信号被避免或至少减少。该技术在理论上可以很容易地扩展到处理磁层中产生的大规模场。

The aim of this project is to co-estimate models of the core and ionosphere magnetic fields, with the longer-term view of building a “comprehensive” model of the Earth’s magnetic field. In this first step we would like to take advantage of the progresses made in the understanding of the ionosphere by global M-I-T modelling to better separate the core and ionospheric signals in satellite data. The magnetic signal generated in the ionosphere is particularly difficult to handle because satellite data provide only information on a very narrow local time window at a time. To get around this difficulty, we would like to apply a technique derived from assimilation methods and that has been already successfully applied in outer-core flow studies. The technique relies on a theoretical model of the ionosphere such as the Upper Atmosphere Model (UAM), where statistics on the deviations from a simple background model are estimated. The derived statistics provided in a covariance matrix format can then be use directly in the magnetic data inversion process to obtain the expected core and ionospheric models. We plan to apply the technique on the German CHAMP satellite data selected for magnetically quiet times. As an output we should obtain a model of the ionospheric magnetic variation field tailored for the selected data and a core-lithosphere field model where possible leakage from ionospheric signals are avoided or at least reduced. The technique can in theory be easily extended to handle the large-scale field generated in the magnetosphere.