Testing a polarimetric cloud imager aboard research vessel Polarstern: comparison of color-based and polarimetric cloud detection algorithms
Testing a polarimetric cloud imager aboard research vessel Polarstern: comparison of color-based and polarimetric cloud detection algorithms
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DOI：
10.1364/ao.54.001065
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发表时间：
2015-02-10
期刊：
影响因子：
1.9
通讯作者：
Macke, Andreas
中科院分区：
文献类型：
--
作者：
Barta, Andras;Horvath, Gabor;Macke, Andreas
云量估算是日常气象观测的重要组成部分。云量测量可用于气候模式评估、近预报太阳辐射、参数化海面日照波动以及建立大气能量传输模式。目前，最广泛的地面测量云量的方法是基于分析由数码相机获得的天空的非偏振强度和颜色分布。作为一种新的方法，我们提出可以通过额外使用180度视场成像偏振仪测量的天窗偏振来辅助云检测。2010年秋天，我们在ANT-XXVII/1号科考船上对这种新型极化云探测器进行了测试。我们的目标之一是测试测量硬件在跨大西洋巡航的极端条件下的耐久性。在这里，我们描述了仪器，并比较了几种不同的云检测算法的结果，一些是传统的，一些是新开发的。我们还讨论了我们设计的弱点和可能的改进。与传统的非偏振全天空成像仪开发的云检测算法的比较使我们能够评估偏振量的附加价值。我们发现：(1)基于神经网络的算法在所研究的方案中表现最好；(2)全局信息（强度的均值和方差）、非光学信息（如太阳视图几何）和偏振信息（如极化程度）提高了云检测的准确性，尽管略有提高。(C) 2015美国光学学会
Cloud cover estimation is an important part of routine meteorological observations. Cloudiness measurements are used in climate model evaluation, nowcasting solar radiation, parameterizing the fluctuations of sea surface insolation, and building energy transfer models of the atmosphere. Currently, the most widespread ground-based method to measure cloudiness is based on analyzing the unpolarized intensity and color distribution of the sky obtained by digital cameras. As a new approach, we propose that cloud detection can be aided by the additional use of skylight polarization measured by 180 degrees field-of-view imaging polarimetry. In the fall of 2010, we tested such a novel polarimetric cloud detector aboard the research vessel Polarstern during expedition ANT-XXVII/1. One of our goals was to test the durability of the measurement hardware under the extreme conditions of a trans-Atlantic cruise. Here, we describe the instrument and compare the results of several different cloud detection algorithms, some conventional and some newly developed. We also discuss the weaknesses of our design and its possible improvements. The comparison with cloud detection algorithms developed for traditional nonpolarimetric full-sky imagers allowed us to evaluate the added value of polarimetric quantities. We found that (1) neural-network-based algorithms perform the best among the investigated schemes and (2) global information (the mean and variance of intensity), nonoptical information (e.g., sun-view geometry), and polarimetric information (e.g., the degree of polarization) improve the accuracy of cloud detection, albeit slightly. (C) 2015 Optical Society of America