DATA ASSIMILATION APPROACH FOR FORECAST OF SOLAR ACTIVITY CYCLES
DATA ASSIMILATION APPROACH FOR FORECAST OF SOLAR ACTIVITY CYCLES
复制标题
太阳活动周期预测的数据同化方法
DOI：
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发表时间：
2016
期刊：
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通讯作者：
I. Kitiashvili
中科院分区：
文献类型：
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作者：
I. Kitiashvili
许多预测未来太阳活动周期的尝试大多是基于对以前太阳活动周期的观测得出的经验关系，它们产生了广泛的预测强度和周期持续时间。目前发电机模型得到的结果也严重偏离彼此，从而提出了有关标准的问题，以量化这种预测的可靠性。模拟未来太阳活动的主要困难是发电机模型和观测的缺点，这些缺点使我们无法确定全球太阳磁结构及其动力学的当前和过去状态。数据同化是一种相对较新的方法，用于在系统的物理特性不为人所知的情况下，发展基于物理的预测并估计其不确定性。本文提出了一种应用集合卡尔曼滤波方法，通过使用低阶非线性发电机模型，包括基本的物理和太阳黑子周期的一般性质，可以描述太阳黑子周期的建模和预测。尽管这个模型很简单，但数据同化方法为未来太阳活动周期的强度提供了合理的估计。特别是，2008年计算和发布的第24周期预测到目前为止保持得相当好。在本文中，我将首次尝试使用数据同化方法预测第25周期，并讨论该预测的不确定性。
Numerous attempts to predict future solar cycles are mostly based on empirical relations derived from observations of previous cycles, and they yield a wide range of predicted strengths and durations of the cycles. Results obtained with current dynamo models also deviate strongly from each other, thus raising questions about criteria to quantify the reliability of such predictions. The primary difficulties in modeling future solar activity are shortcomings of both the dynamo models and observations that do not allow us to determine the current and past states of the global solar magnetic structure and its dynamics. Data assimilation is a relatively new approach to develop physics-based predictions and estimate their uncertainties in situations where the physical properties of a system are not well-known. This paper presents an application of the ensemble Kalman filter method for modeling and prediction of solar cycles through use of a low-order nonlinear dynamo model that includes the essential physics and can describe general properties of the sunspot cycles. Despite the simplicity of this model, the data assimilation approach provides reasonable estimates for the strengths of future solar cycles. In particular, the prediction of Cycle 24 calculated and published in 2008 is so far holding up quite well. In this paper, I will present my first attempt to predict Cycle 25 using the data assimilation approach, and discuss the uncertainties of that prediction.