A cloud screening algorithm for ground-based sun photometry using all-sky images and deep transfer learning
Abstract. Aerosol optical depth (AOD) is used to characterize aerosol loadings within Earth’s atmosphere. Sun photometers measure AOD from the Earth’s surface based on direct-sunlight intensity readings by spectrally narrow light detectors. However, when the solar disk is partially obscured by cloud cover, sun photometer measurements can be biased due to the interaction of sunlight with cloud constituents. We present a novel deep transfer learning model on all-sky images to support more accurate AOD retrievals. We used three independent image datasets for training and testing: the novel Northern Colorado All-Sky Image (NCASI), the Whole Sky Image SEGmentation (WSISEG), and the METCRAX-II datasets from the National Center for Atmospheric Research (NCAR). We visually partitioned all-sky images into three categories: 1) clear sky around the solar disk, 2) thin cirrus obstructing the solar disk, and 3) thick, non-cirrus clouds obstructing the solar disk. Two-thirds of the images were allocated for training and one-third were allocated for testing. We trained models based on all possible combinations of the training sets. The best-performing model successfully classified 95.5 %, 96.9 %, and 89.1 % of testing images from NCASI, METCRAX-II and WSISEG datasets, respectively. Our results demonstrate that all-sky imaging with deep transfer learning can be applied toward cloud screening, which would aid ground-based AOD measurements.
Eric A. Wendt et al.
Eric A. Wendt et al.
Eric A. Wendt et al.
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The manuscript by Wendt et al. used a deep transfer learning model to develop a cloud screening algorithm for ground-based sun photometry applications. Sun photometer images from three different sites are used for both data training and testing, and the images are classified as clear, cirrus and thin clouds. The algorithm can achieve hitting rates about 90% for the three datasets. The manuscript is overall reasonably presented. However, as a model based on deep learning, there are still some major problems with the model development and validation. The following lists my detailed comments.
1. It is noticed that a total of approximately 1500 images are used for both data training and testing, which is very small for deep learning model development. With such a small number of samples, there would be possible problems on overfitting, which can be demonstrated by the significant drop of accuracy for different datasets in Table 2. There are other problems related to small sample size as well. Thus, I don’t think such a small sample size would result any solid algorithm based on deep learning.
2. Images from three different sites are used. What’s the essential differences among them during data training and testing. It is noticed that the fractions of images of different classes (clear, cirrus or cloud) are quite different. Again, this further demonstrates the insufficiency of datasets for deep learning.
3. How are the prepared images labeled before the learning? As noticed from Session 2.2, the procedure is automated. If this is true, the authors have developed a physical model for the classification, and it is not necessary to develop a deep learning model any more. Because if we understood the physical model mentioned in Session 2.2 is the truth, the deep-learning based one will never beat it. Thus, the preparation for the image classification has to be better discussed.
4. Neither the model development nor the results were discussed in details. As an AMT articles, readers expect really detailed techniques and results to fully understand and to fully repeat the methods. The current manuscript is really concise, which makes it difficult to evaluate the method.
5. The manuscript mostly discussed their own algorithm. How is the current algorithm compared with traditional ones? Is the deep learning algorithm showing any advantages compared with conventional ones? In other words, the current model should be compared with similar ones.