This paper proposes a novel method to estimate ground-level cloud cover using images from a ground-based sky imager and convolutional neural network, as an alternative to human observations. The topic is of great value and the methodology is generally sound. The paper is well structured and well written. The study provides a feasible technical approach for automated monitoring of surface cloud cover. In summary, the following issues need further improvement:

Thank you for reviewing this manuscript. Based on the reviewer's comments, we have added some content to the revised manuscript, as detailed below. We believe that the quality of the manuscript has been improved and the clarity of the manuscript has been enhanced after addressing the reviewer's comments.

（1）The input data of photos were sampled at 1 hour intervals, corresponding to the manual observations. It is unclear whether the sampling time is exactly the same as the time of manual observation. Clouds can change rapidly in a short period of time, which may lead to inaccurate sample data if there is a time discrepancy between the photos and observations.

We agree with the reviewer's comments. The KMA ground weather observation guidelines stipulate that observations should be conducted for every on the hour. However, the timing of observation may vary depending on the observer, which can lead to inconsistencies and potential inaccuracies in the collected data. Unfortunately, such information has not been documented, preventing us from determining the precise observation time.

（2）This study utilizes CNN to establish the model, with images as input and manual observations as true value labels. The method is relatively effective. However, there are two aspects of problems:

1) Manual observations also have errors, which are determined by human subjective judgments. Therefore, the methods developed based on such samples and labels inevitably still contain errors, which cannot be further improved through model tuning and data augmentation. It is recommended to try comparison with other conventional methods, and develop research methods to improve the accuracy of sample data;

2) The manual observations quantify the results into 0-10 tenths. From the observation perspective, the resolution of such cloud cover observations is slightly low. Using 1 tenth to 1.9 tenths may have a difference of 9 percentage points, which needs further improvement in the observation accuracy of cloud cover for refined observations and forecasts.

We agree with the reviewer's comments on 1) and 2). The KMA-observed cloud cover was recorded as 0-8 oktas or 0-10 tenths according to the WMO synoptic observation guidelines. The employed observation method, as also noted by the reviewer, has a low resolution of quantitative values; hence, the margin of error is a few % in the same cloud cover class. Therefore, we believe that it would be possible to estimate the cloud cover with high accuracy if the pixel information (with or without clouds) in the image is extracted and learned in terms of the percent cloud cover as the label. In this study, the data resolution was rather low because the OBS cloud cover was used as a label. Therefore, we have added the following sentence:

L301 “Depending on the characteristics of the data to be learned, it is possible to estimate the cloud cover in % instead of oktas and tenths; accordingly, the time resolution can also be estimated in minutes rather than hourly intervals.”

（3）Based on traditional image processing methods like NBR threshold, the accuracy is usually lower during sunrise and sunset due to the influence of scattering near the sun. This study also found larger errors during this period. What are the reasons? Is it because there are fewer manual observation samples?

We used the DL method to compensate for the shortcomings of the threshold method and the ML method. The estimation accuracy of the cloud cover in this study was better than that of the previously employed threshold method and ML method. However, the accuracy was still relatively low at sunrise/sunset time. This is because, as noted by the reviewer, the image was polluted due to the scattering of light near the sun, making it difficult to distinguish clouds from the sky. Therefore, we added the following sentence to the main text on possible methods to improve the accuracy of cloud cover estimation at sunrise/sunset time:

L223 “Moreover, in this study, images acquired at sunrise/sunset time accounted for 16.23% of all learning datasets. In other words, the images acquired at sunrise/sunset time were learned 2 to 3 times lower for each cloud cover class than images acquire at daytime and nighttime. Therefore, it is expected that the cloud cover estimation accuracy can be improved if more images are acquired and learned during sunrise/sunset time.”