Prediction of geomagnetic field with data assimilation: a candidate secular variation model for IGRF-11
Prediction of geomagnetic field with data assimilation: a candidate secular variation model for IGRF-11
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DOI：
10.5047/eps.2010.07.008
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发表时间：
2010-01-01
影响因子：
3
通讯作者：
Tangborn, Andrew
中科院分区：
文献类型：
--
作者：
Kuang, Weijia;Wei, Zigang;Tangborn, Andrew
数据同化在气象学和海洋学中被用来结合动力模式和观测来预测状态变量的变化。沿着类似的发展路线，我们创建了一个地磁数据同化系统--MOSST-DAS，其中包括一个数值地球发电机模型、一套可追溯到公元前5000年的地磁和古地磁场模型，以及使用顺序同化算法的数据同化组成部分。为了减少地球发电机模型产生的系统误差，采用了预测-校正迭代算法以获得更准确的预报。用7年地磁预报对该系统和新算法进行了检验。将计算结果与混沌模型和IGRF场模型进行了比较，结果吻合较好。利用到2009年的地磁场模式，我们给出了2010年至2015年五年平均长期变化(SV)的预测，直到L=8度。我们的预测作为候选的SV模式提交给IGRF-11。
Data assimilation has been used in meteorology and oceanography to combine dynamical models and observations to predict changes in state variables. Along similar lines of development, we have created a geomagnetic data assimilation system, MoSST-DAS, which includes a numerical geodynamo model, a suite of geomagnetic and paleomagnetic field models dating back to 5000 BCE, and a data assimilation component using a sequential assimilation algorithm. To reduce systematic errors arising from the geodynamo model, a prediction-correction iterative algorithm is applied for more accurate forecasts. This system and the new algorithm are tested with 7-year geomagnetic forecasts. The results are compared independently with CHAOS and IGRF field models, and they agree very well. Utilizing the geomagnetic field models up to 2009, we provide our prediction of 5-year mean secular variation (SV) for the period 2010-2015 up to degree L = 8. Our prediction is submitted to IGRF-11 as a candidate SV model.