Observing system simulation experiments in geomagnetic data assimilation
Observing system simulation experiments in geomagnetic data assimilation
复制标题
地磁资料同化观测系统模拟实验
DOI：
10.1029/2006jb004691
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发表时间：
2007
影响因子：
--
通讯作者：
W. Kuang
中科院分区：
文献类型：
--
作者：
Don Liu;A. Tangborn;W. Kuang
对地磁数据同化系统进行了观测系统模拟实验（OSSEs）。这些实验的目的是证明如何评估将地磁观测同化到地球动力学模型中的新算法。进行“自然”运行，以建立地球核心状态的“真实”演变，由此创建合成的地表地磁场观测。然后使用最优插值（OI）方案将这些观测结果同化到地球动力学模型中。利用不同的瑞利数模拟了自然界和模型运行中的模型误差。由于真正的核心状态演化是完全已知的，同化结果可以在计算域中的任何状态变量和任何点进行评估。在这项工作中，我们重点研究了整个外核磁场的极向（可观察到的）和环向（不可观察到的）分量。利用不同程度的观测和不同的预报误差相关长度尺度进行了实验。我们还研究了模式误差对同化和地磁预报精度的影响。持续约90%的磁自由衰减时间的同化运行对外核深处磁场的两个分量都有积极的影响。对地表磁场的预报显示出较低的误差增长，这表明用于预报的初始条件通过同化得到了很大的改善。
[1] Observing System Simulation Experiments (OSSEs) have been carried out for a geomagnetic data assimilation system. The purpose of these experiments is to demonstrate how new algorithms for assimilating geomagnetic observations into a geodynamo model can be evaluated. A “nature” run is carried out to establish a “true” evolution of the Earth's core state, from which synthetic surface geomagnetic field observations are created. These observations are then assimilated into the geodynamo model using an optimal interpolation (OI) scheme. Model error is simulated by using a different Rayleigh number in the nature and model runs. Because the true core state evolution is known completely, the assimilation results can be evaluated in terms of any state variable and at any point in the computational domain. In this work we focus on the poloidal (observed) and toroidal (unobserved) components of the magnetic field throughout the outer core. Experiments are carried out using observations of different degrees and varying forecast error correlation length scales. We also investigate the impact of model error on the assimilation and on the accuracy of geomagnetic forecasts. Assimilation runs lasting about 90% of the magnetic free decay time show a positive impact on both components of magnetic field deep within the outer core. Forecasts of the surface magnetic field show much lower error growth, indicating that the initial condition used for the forecasts has been substantially improved through assimilation.