Improved automatic estimation of winds at the cloud top of Venus using superposition of cross-correlation surfaces
Improved automatic estimation of winds at the cloud top of Venus using superposition of cross-correlation surfaces
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DOI：
10.1016/j.icarus.2016.01.018
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发表时间：
2016-06-01
期刊：
影响因子：
3.2
通讯作者：
Horinouchi, Takeshi
中科院分区：
文献类型：
--
作者：
Ikegawa, Shinichi;Horinouchi, Takeshi
准确的风场观测是研究大气动力学的关键。提出了一种新的金星向阳面云自动跟踪方法，并利用金星快车轨道器上的金星监视相机获得的紫外图像进行了评估。它使用在几个小时内连续获得的多个图像。根据图像的成对组合计算互相关，并将其叠加以识别云平流。结果表明，叠加提高了速度估计的准确性，并显着减少虚假的模式匹配，导致大的错误。提出了两种方法来评估每个获得的云运动矢量的精度。一个依赖于考虑各向异性云形态的互相关的置信界限。另一个依赖于通过将连续图像分成两组而获得的两个独立估计的比较。这两种评估可以结合起来筛选结果。结果表明，筛选矢量的精度在向赤道30度附近很高，而在高纬度地区精度相对较低。对它们的分析支持了先前报道的金星云层存在日复一日的大尺度变化，并进一步表明了较小尺度的特征。这项研究的成果有望推动金星大气动力学的研究。(C)2016爱思唯尔公司All rights reserved.
Accurate wind observation is a key to study atmospheric dynamics. A new automated cloud tracking method for the dayside of Venus is proposed and evaluated by using the ultraviolet images obtained by the Venus Monitoring Camera onboard the Venus Express orbiter. It uses multiple images obtained successively over a few hours. Cross-correlations are computed from the pair combinations of the images and are superposed to identify cloud advection. It is shown that the superposition improves the accuracy of velocity estimation and significantly reduces false pattern matches that cause large errors. Two methods to evaluate the accuracy of each of the obtained cloud motion vectors are proposed. One relies on the confidence bounds of cross-correlation with consideration of anisotropic cloud morphology. The other relies on the comparison of two independent estimations obtained by separating the successive images into two groups. The two evaluations can be combined to screen the results. It is shown that the accuracy of the screened vectors are very high to the equatorward of 30 degree, while it is relatively low at higher latitudes. Analysis of them supports the previously reported existence of day-to-day large-scale variability at the cloud deck of Venus, and it further suggests smaller-scale features. The product of this study is expected to advance the dynamics of venusian atmosphere. (C) 2016 Elsevier Inc. All rights reserved.