Automatic Cloud‐Type Classification Based On the Combined Use of a Sky Camera and a Ceilometer

Abstract

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 Jaén (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.

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“…Estimation of the daily irradiance from site latitude, elevation and day of the year, plus haze index, or clear‐sky with a correction factor, does not account properly for the effects of the clouds. A better statistic of clouds, such as for example the one of Apollo, or even better, the use of high frequency measured direct normal irradiances, may permit to accurately assess the effect of clouds. The measured DNI of reference days for every month, for example, Reference is not enough to permit a proper assessment.…”

Final response to RC2

“…Since clouds are highly variable in space and time, measurements at high spatial and temporal resolution with small uncertainties are needed (WMO, 2012). Recent research has therefore been conducted to find an automated cloud detection instrument (or a combination of instruments) to replace human observers (Boers et al, 2010;Tapakis and Charalambides, 2013;Huertas-Tato et al, 2017;Smith et al, 2017).…”

“…However, the laser pulse is not only scattered back by cloud hydrometeors, but also by aerosols (Liu et al, 2015). Examples of active remote sensing instruments are cloud radar (Kato et al, 2001;Illingworth et al, 2007;Feister et al, 2010), lidar (Campbell et al, 2002;Zhao et al, 2014) and ceilometers (Martucci et al, 2010). Due to the narrow beam, a disadvantage of these measurement techniques is the lack of instantaneous cloud information of the whole of the upper hemisphere.…”

Cross-Domain Ground-Based Cloud Classification Based on Transfer of Local Features and Discriminative Metric Learning

“…We first sample local patches in each region using a dense sampling strategy, and then extract local features. Within each patch, we extract histograms of each local feature with three scales , i.e., (P, R) = (8, 1), (16,2) and (24,3). After each patch is represented as a histogram, we apply max pooling strategy on all local histograms for each region, i.e., reserving the maximum response of each histogram bin among all histograms.…”

“…Specifically, we extracted LBP feature with (P, R) equal to (8,1), (16,2) and (24,3), and then concatenated histograms of the three scales to form a feature vector for each cloud image. So the final feature vector of each cloud image has 10 + 18 + 26 = 54 dimensions.…”