Forecasting secular variation using core flows
Forecasting secular variation using core flows
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使用核心流量预测长期变化
DOI：
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发表时间：
2010
期刊：
影响因子：
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通讯作者：
K. Whaler
中科院分区：
文献类型：
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作者：
C. Beggan;K. Whaler
在过去的十年里，卫星测量与地面观测站的数据相结合，已经可以构建非常详细的地球磁场长期变化（SV）模型。然而，预测主要领域的变化仍然是一个挑战，主要是因为控制SV的核心过程没有得到充分的理解。因此，大多数预测不诉诸任何物理模型的限制，但使用，例如，从以前的测量多项式外推。我们尝试应用物理模型预测的平均SV在2010-2015年期间，通过开发一个核心流模型。这个稳定流模型，来自SV数据在2004年5月至2009年5月，产生一组高斯SV系数，用于平流大尺度磁场向前的时间。虽然这个模型还没有提交作为候选IGRF-11，我们提出了我们的SV预测模型，并将其与其他候选IGRF-11 SV模型进行比较。此外，我们研究了使用Ensemble卡尔曼滤波器来优化同化来自（1）预测方法和（2）噪声数据测量的场模型。如果在相当长的一段时间内无法获得覆盖全球的高质量卫星数据，就有可能出现这种情况。我们发现，同化模型的整体失配的实际字段可以低于个人的输入模型的失配，每个模型的不确定性是合理的众所周知的。
Over the past ten years satellite measurements in combination with data from ground-based observatories have allowed very detailed models of the secular variation (SV) of the Earth’s magnetic field to be constructed. However, forecasting the change of the main field still remains a challenge, primarily because the core processes controlling SV are not sufficiently well understood. Hence, most forecasts do not appeal to any physical modelling constraints but use, for example, polynomial extrapolation from previous measurements. We attempt to apply a physical model to forecast the average SV during 2010–2015 by developing a core flow model. This steady flow model, derived from SV data during 2004.5 to 2009.5, generates a set of Gauss SV coefficients which are used to advect the large scale magnetic field forwards in time. Although this model has not been submitted as a candidate for IGRF-11, we present our SV prediction model and compare it to other candidate IGRF-11 SV models. In addition, we examine the use of the Ensemble Kalman filter to optimally assimilate field models derived from (1) forecast methods and (2) noisy data measurements. Such a scenario might conceivably arise if high quality satellite data with global coverage are not available for a significant period of time. We show that the overall misfit of the assimilated model to the actual field can be lower than the individual misfits of the input models, provided the uncertainties of each model are reasonably well known.