We present a new model of the geomagnetic field spanning the last 20 years and called Kalmag. Deriving from the assimilation of CHAMP and Swarm vector field measurements, it separates the different contributions to the observable field through parameterized prior covariance matrices. To make the inverse problem numerically feasible, it has been sequentialized in time through the combination of a Kalman filter and a smoothing algorithm. The model provides reliable estimates of past, present and future mean fields and associated uncertainties. The version presented here is an update of our IGRF candidates; the amount of assimilated data has been doubled and the considered time window has been extended from [2000.5, 2019.74] to [2000.5, 2020.33].

中文翻译：

---

Kalmag 模型作为 IGRF-13 的候选模型

我们提出了一个跨越过去 20 年的地磁场新模型，称为 Kalmag。源自 CHAMP 和 Swarm 矢量场测量的同化，它通过参数化的先验协方差矩阵分离对可观察场的不同贡献。为了使逆问题在数值上可行，它通过卡尔曼滤波器和平滑算法的组合在时间上进行了排序。该模型提供对过去、现在和未来平均场和相关不确定性的可靠估计。此处提供的版本是我们 IGRF 候选者的更新；同化数据量翻倍，考虑的时间窗口从[2000.5, 2019.74]延长至[2000.5, 2020.33]。