资源环境科技发展态势分析平台

A methodology, aimed to be fully operational, for automatic cloud classification based on the synergetic use of a sky camera and a ceilometer is presented. The random forest machine learning algorithm was used to train the classifier with 19 input features: 12 extracted from the sky camera images and 7 from the ceilometer. The method was developed and tested based on a set of 717 images collected at the radiometric stations of the Univ. of Jaen (Spain). Up to nine different types of clouds (plus clear sky) were considered (clear sky, cumulus, stratocumulus, nimbostratus, altocumulus, altostratus, stratus, cirrocumulus, cirrostratus, and cirrus) plus an additional category multicloud, aiming to account for the frequent cases in which the sky is covered by several cloud types. A total of eight experiments was conducted by (1) excluding/including the ceilometer information, (2) including/excluding the multicloud category, and (3) using six or nine different cloud types, aside from the clear-sky and multicloud category. The method provided accuracies ranging from 45% to 78%, being highly dependent on the use of the ceilometer information. This information showed to be particularly relevant for accurately classifying "cumuliform" clouds and to account for the multicloud category. In this regard, the camera information alone was found to be not suitable to deal with this category. Finally, while the use of the ceilometer provided an overall superior performance, some limitations were found, mainly related to the classification of clouds with similar cloud base height and geometric thickness.

Plain Language Summary The different cloud types are the results of different atmospheric processes. In addition, cloud types have a varied interaction with the solar radiation. Therefore, cloud monitoring, have interest in a varied of fields, ranging from the study of the atmospheric thermodynamic processes to solar energy. So far, cloud monitoring is conducted based on human observation, making cloud type databases scarce and, in general, low reliable. A procedure for automatic cloud classification is conducted here using information from a sky camera and a ceilometer. The information derived from these two instrument is showed provide an enhanced performance.