Variability of solar surface irradiances in the 1‑minute range is of interest especially for solar energy applications. Eight variability classes were previously defined for the 1 min resolved direct normal irradiance (DNI) variability inside an hour. In this study spatial structural parameters derived fromsatellite-based cloud observations are used as classifiers in order to detect the associated direct normal irradiance (DNI) variability class in a supervised classification scheme. A neighbourhood of 3×3 to 29×29 satellite pixels is evaluated to derive classifiers describing the actual cloud field better than just using a single satellite pixel at the location of the irradiance observation. These classifiers include cloud fraction in a window around the location of interest, number of cloud/cloud free changes in a binary cloud mask in this window, number of clouds, and a fractal box dimension of the cloud mask within the window. Furthermore, cloud physical parameters as cloud phase, cloud optical depth, and cloud top temperature are used as pixel-wise classifiers. A classification scheme is set up to search for the DNI variability class with a best agreement between these classifiers and the pre-existing knowledge on the characteristics of the cloud field within each variability class from the reference data base. Up to 55 % of all DNI variability class members are identified in the same class as in the reference data base. And up to 92 % cases are identified correctly if the neighbouring class is counted as success as well – the latter is a common approach in classifying natural structures showing no clear distinction between classes as in our case of temporal variability. Such a DNI variability classification method allows comparisons of different project sites in a statistical and automatic manner e.g. to quantify short-term variability impacts on solar power production. This approach is based on satellite-based cloud observations only and does not require any ground observations of the location of interest.

中文翻译：

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根据对地静止卫星云观测的空间特征，对直接法向辐照度1分钟的时间变异性进行分类

1分钟范围内太阳表面辐照度的变化尤其令人关注，特别是对于太阳能应用。先前为一小时内的1分钟分辨直接法向辐照度（DNI）变异性定义了八个变异性类。在这项研究中，从基于卫星的云观测得到的空间结构参数用作分类器，以便在监督分类方案中检测相关的直接法向辐照度（DNI）变异性类。与仅在辐照度观测位置使用单个卫星像素相比，对3×3到29×29卫星像素的邻域进行评估可以更好地得出描述实际云场的分类器。这些分类器包括在感兴趣位置周围的窗口中的云分数，此窗口中二进制云掩码中的无云/无云更改数量，云数以及该窗口内云掩码的分形框维。此外，将云物理参数（如云相位，云光学深度和云顶温度）用作按像素分类器。建立分类方案以搜索DNI变异性类别，并在这些分类器与参考数据库中每个变异性类别中有关云场特征的现有知识之间取得最佳一致性。与参考数据库相同的类别中，最多可识别出所有DNI变异性类别成员的55％。如果将相邻的类别也算作成功，那么多达92％的案例也可以正确识别-后者是对自然结构进行分类的常用方法，在我们的时间变异性情况下，类别之间没有明显的区别。这种DNI可变性分类方法允许以统计和自动方式比较不同项目站点，例如量化对太阳能发电的短期可变性影响。这种方法仅基于基于卫星的云观测，不需要对感兴趣位置进行任何地面观测。