Stochastic forecasting of the geomagnetic field from the COV-OBS.x1 geomagnetic field model, and candidate models for IGRF-12
Stochastic forecasting of the geomagnetic field from the COV-OBS.x1 geomagnetic field model, and candidate models for IGRF-12
复制标题
DOI：
10.1186/s40623-015-0225-z
复制
发表时间：
2015-05-14
影响因子：
3
通讯作者：
Finlay, Christopher C.
中科院分区：
文献类型：
--
作者：
Gillet, Nicolas;Barrois, Olivier;Finlay, Christopher C.
我们提出了覆盖1840年至2020年的地磁场模型COV-OBS.x1，从中导出了IGRF-12的候选模型。对于最近的时代，它主要是由观测站的年度平均值和测量的奥斯特，冠军，和群卫星任务的第一个差异的限制。利用地磁序列时间谱的随机信息构造先验模型协方差矩阵，弥补了数据约束的不足。这种方法使得有可能使用的后验模型误差，例如，来衡量的“观测”的不确定性，在数据同化方案的研究外核dynamics.We还提出并说明了一个随机算法，旨在预测地磁场。外核表面处的径向场由核运动平流，核运动由1阶自回归过程控制。这种特殊的选择是由地磁序列的功率谱密度所观察到的斜率所激发的。由于使用了增广状态集合卡尔曼滤波算法，可以考虑时间相关的模型误差（与未解决的磁场相关的子网格过程）。我们表明，包络线的预测包括观测到的长期变化的地磁场超过5年的时间间隔，即使在快速变化的情况下。在测试的核心动力学的假设的目的，这种原型方法可以实现建立预测地磁场的能力的“状态零”，通过测量什么可以预测时，没有确定性的物理纳入动态模型。
We present the geomagnetic field model COV-OBS.x1, covering 1840 to 2020, from which have been derived candidate models for the IGRF-12. Towards the most recent epochs, it is primarily constrained by first differences of observatory annual means and measurements from the Oersted, Champ, and Swarm satellite missions. Stochastic information derived from the temporal spectra of geomagnetic series is used to construct the a priori model covariance matrix that complements the constraint brought by the data. This approach makes it possible the use of a posteriori model errors, for instance, to measure the 'observations' uncertainties in data assimilation schemes for the study of the outer core dynamics.We also present and illustrate a stochastic algorithm designed to forecast the geomagnetic field. The radial field at the outer core surface is advected by core motions governed by an auto-regressive process of order 1. This particular choice is motivated by the slope observed for the power spectral density of geomagnetic series. Accounting for time-correlated model errors (subgrid processes associated with the unresolved magnetic field) is made possible thanks to the use of an augmented state ensemble Kalman filter algorithm. We show that the envelope of forecasts includes the observed secular variation of the geomagnetic field over 5-year intervals, even in the case of rapid changes. In a purpose of testing hypotheses about the core dynamics, this prototype method could be implemented to build the 'state zero' of the ability to forecast the geomagnetic field, by measuring what can be predicted when no deterministic physics is incorporated into the dynamical model.