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，以建立一个地球磁场的“综合”模型为长期目标。在第一步中，我们希望利用全球M-I-T模型在理解电离层方面取得的进展，更好地分离卫星数据中的核心和电离层信号。电离层产生的磁信号尤其难以处理，因为卫星数据每次只能提供一个非常狭窄的当地时间窗口的信息。为了克服这一困难，我们希望应用一种源自同化方法的技术，这种技术已经成功地应用于核外流研究。该技术依赖于电离层的理论模型，如高层大气模型（UAM），在该模型中估计了与简单背景模型偏差的统计数据。以协方差矩阵格式提供的导出统计量可直接用于磁数据反演过程，以获得期望的地核和电离层模型。我们计划将该技术应用于德国CHAMP卫星的磁安静时间数据。作为一个输出，我们应该得到一个为所选数据量身定制的电离层磁场变化模型和一个核岩石圈磁场模型，在这个模型中，电离层信号可能的泄漏可以避免或至少减少。理论上，该技术可以很容易地扩展到处理磁层中产生的大规模磁场。

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.