跟踪太阳表面的颗粒并重建速度场。II。误差分析
Tracking granules at the Sun's surface and reconstructing velocity fields. II. Error analysis
摘要：
太阳表面水平速度场的确定对于理解太阳对流区的动力学和磁学性质至关重要。这些测量可以通过跟踪颗粒来完成。然而，跟踪来自地面观测的颗粒会受到地球大气湍流的影响，这会导致图像失真。本文的重点是评估这种噪声对速度场图的影响。我们使用的相干结构跟踪算法最近开发的，并将其应用到两个独立的系列图像，包含相同的太阳信号。我们首先表明，强烈建议对图像的时间序列进行k-ω滤波作为预处理以减少噪声，而相反，应避免使用解拉伸。我们还表明，颗粒的寿命有很强的影响的速度误差条和寿命上的阈值应施加，以尽量减少错误。最后，虽然太阳流模式很容易识别，图像质量也很好，但事实证明，每21秒对两张图像进行一次时间采样是不够频繁的，因为图像失真仍然会污染2500 km尺度上30%水平的速度场，即颗粒开始表现得像被动标量的尺度。相干结构跟踪算法是一个有用的工具，当至少有两个独立的序列是可用的表面水平太阳速度场测量的噪声控制。
英文摘要：
The determination of horizontal velocity fields at the solar surface is crucial to understanding the dynamics and magnetism of the convection zone of the sun. These measurements can be done by tracking granules. Tracking granules from ground-based observations, however, suffers from the Earth's atmospheric turbulence, which induces image distortion. The focus of this paper is to evaluate the influence of this noise on the maps of velocity fields. We use the coherent structure tracking algorithm developed recently and apply it to two independent series of images that contain the same solar signal. We first show that a k-ωfiltering of the times series of images is highly recommended as a pre-processing to decrease the noise, while, in contrast, using destretching should be avoided. We also demonstrate that the lifetime of granules has a strong influence on the error bars of velocities and that a threshold on the lifetime should be imposed to minimize errors. Finally, although solar flow patterns are easily recognizable and image quality is very good, it turns out that a time sampling of two images every 21 s is not frequent enough, since image distortion still pollutes velocity fields at a 30% level on the 2500 km scale, i.e. the scale on which granules start to behave like passive scalars. The coherent structure tracking algorithm is a useful tool for noise control on the measurement of surface horizontal solar velocity fields when at least two independent series are available.
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