The submitted Manuscript presents an approach for Aurora real-time detection based on hemispheric RGB camera images. In contrast to other approaches using deep learning, the introduced method uses spectra indices calculated from RGB image data.

Unfortunately, the authors assume exhaustive preknowledge on the detection / measurement of aurora that impedes an easy and exciting entry into the topic. 

The method is very complex and hard to understand due to missing figures that would definetely help to grasp the matter. Furthermore, it sounds like the approach is only applicable to one study area using one specific camera setup where the authors developed the method. It was not stated that the approch was tested / validated at some else location or using some other camera configuration. 

The evaluation is based on manual observations that AFAIK could be very subjective. Unfortunately it is not stated if the reference data is based on observations of several operateurs to have some quality control on the Groud Truth data. 

Due to the mentioned points I recommend to resubmit the paper after extensive rework as the method itself sounds interesting. 

Some more comments are given directly in the attaced PDF.

I thank the authors for the interesting manuscript. The manuscripts details a simple method to classify images in an ASC aurora image collection according to the presented ASC index. The method is computationally lightweight, which is important for the goal of nowcasting local arc breaking of auroras in all-sky camera images. The single ASC index indicator makes the classification outcome easily interpretable by experts and novices in the field.

That said, next to the line-by-line review attached, there are some general comments that need addressing in a potential revision.

1. The manuscript makes the data appear as very complex, multi-dimensional and challenging. Sadly, this only appears so due to ambiguous writing. In the end, after careful review, it appears the images are rather small (150,00 pixel) 3-band images, which is hidden behind confusing writing and misleading illustrations (fig. 1). This needs fixing.
2. The manuscript makes the developed method appear as intricate-yet-powerful, which is related to how the data is presented. In the end, the presented method is a multi-level, double-bound thresholding with 5 different thresholding levels (i.e. categroies). This method is indeed simple, but introduces a considerable amount of fine-tuned parameters due to the lack of in-depth prior image processing. This also makes the formulation ambiguous and the method very difficult to replicate for other observatories.
3. The manuscript requires more context information to make it accessible and comprehensible to researchers outside the expert aurora observation community. Special terms and phenomena such as the N2 red line or local arc breaking are neitehr explained in-text or referenced in literature. It is very hard to understand the message and research objectives of the manuscript for an common EGU audience. There is still more than sufficient space available for some added references to give the reader information on where to find further details on the assumption baseline of the manuscript.
4. The manuscript lacks references to make some context information understandable and to to make certain design decisions of the method easier to reason and comprehend.
5. The manuscript lacks a details discussion of the imaging parameters (camera resolution, quantisation, etc.). The authors use JPEG images with small resolutions, dynamic exposure times from the camera, hence automatic white balancing, and an ingrained 3-band 8-bit pixel quantisation. The paper lacks any in-depth discussion of the actual influence of those instrumentation parameters. The paper lacks any comparison and impact assessment of the image compression error from JPEG. Overall, the manuscript treats the actual imaging influences superficially.
6. The manuscript lacks a comparison with alternative, more up-to-date methods of image pattern recognition.
7. The manuscript needs considerable language revision, as minor and intermediate grammar mistakes are frequent.
8. The actual presentation of the ASC images needs revision as the displayed metrics within the image are not indicative and certain features in the images require expert explanation. Furthermore, fewer-but-larger images would support reader comprehension.

I would appreciate a careful revision of the mentioned points, as well as the marked points in-text.

The paper presents a fast approach for real-time Aura detection by using RGB images acquired by an All-Sky-Camera (ASC) (i.e., full frame camera with a fish-eye lens, pointing towards the sky), located in an a heated-dome at the Kiruna Observatory. The aim of the authors is to identify intensification of auroral arc with expanding motion, named Local-Arc-Breaking (LAB), and send email alerts when the “Level 6” criterion for LAB aurora detection is satisfied.

The proposed approach is composed of two steps: 1) perform a pixel-wise classification of all the image pixels into different categories (including three aurora categories), based on the color information of the pixel itself; 2) compute a series of indexes based on the percentage of pixels detected for each category and the average luminosity of the most intense aurora pixels. Based on the computed indexes, the alert system can detect most relevant aurora events and trigger an alert.

The topic is interesting and the proposed solution for a real time alert is relevant, especially because it is a fast and not computationally expansive approach (which is crucial for real-time applications). However, there are some critical aspects that should be solved (or at least discussed):

• Computation of the thresholds and transferability of the method.

The pixel-wise classification is basically based on thresholding on RGB data, and it relies on a large number of thresholds. This makes the classification fast and computationally easy. However, it requires a proper fine tuning of the threshold levels, that is crucial for an accurate classification. I believe that threshold calibration is mostly based on the authors’ experience, as very little information about this is provided in the paper. The clear consequence of this, is that the procedure may not be transferable to another site, with different environmental conditions and different instruments (as the authors stated in the paper). With this idea in mind, it would be useful if the authors can discuss deeper their choices and provide some more considerations that have led to the derived threshold values, rather than just providing the values themselves.

• Camera geometry

Is the camera geometry considered in your study? I understand that you are not trying to reconstruct the location of the aurora (which may be an interesting extension of your study for the future), but the “occupancy” of the pixel classified as aurora (within the different classes), among the total amount of pixels. However, you are using a fish-eye lens (Nikon Nikkor 8 mm), with strong geometrical distortions, that require a proper camera model for correcting them (please, see e.g., https://docs.opencv.org/4.x/db/d58/group__calib3d__fisheye.html). I think that this aspect should be taken into account (e.g., by undistorting the images before any image processing) or at least discussed to see if geometrical distortions have a relevant impact on the accuracy of the classification.

• Comparison with different methods

It would be useful to have some comparison with different state-of-the-art approaches (or also widely established methods, if any) to fully grasp the potential of the proposed solution.

• Deep Learning

In the paper, the authors state that Neural Networks (NN) are black boxes, difficult to debug, and strongly dependent on the training data. I partially agree with the authors on these statements. However, I believe that Deep Learning (DL) is a powerful tool for identifying the presence of aural events and to classify them, according to a-priori defined classes (as it is done in this paper) and ground truth data (manually classified images, or images classified with the algorithm proposed in this paper). Moreover, if the NN layers are trained with datasets from different locations and cameras, and with proper data augmentation, this may result in a more transferable and generalized approach, that can be applied in different observatories. Additionally, NN is basically based on sequences of filter convolutions. Therefore, it may overcome the limitation of considering each pixel independently from its neighborhood for the classification (of course DL is not the only possible solution for this: spatial filters, Markov Random Fields are few other examples). Eventually, DL may be combined with the step 2 of the proposed solution to build a real time alert framework.

All this thing considered, I understand that the use of DL is out of the scope of this paper (I suggest considering it for future works, though), but I believe that some more references and discussions on recent works that involve DL for aurora image classification may improve the quality and completeness of this work.

• Figure and Tables

I believe that figures and tables can be improved. In my opinion, Figure 1 is not clear, and it should be revised (please, see comments in the pdf supplement). Additionally, figures with ASC images are often placed far from the text in which they are cited (even 3 or 4 pages afterwards), and this prevent a smooth read of the paper. I suggest revising figure placement in the text, reducing the number of ASC images to the really informative ones and making them bigger.

Moreover, I believe that the tables containing the thresholding conditions are too verbose and don’t add relevant information in the text. Moreover, they are splitted in different pages, making even harder to read the tables. I suggest summarizing the main concepts of the tables (e.g., the different classes and the main classification criteria) in just one or two tables to be included in the main text of the paper. On the other hand, all the thresholds can be condensed in one table in the appendix.

• Background knowledge

As commented by other reviewers, I also believe that some background knowledge should be included in the text to allow a wider community to understand your work.

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Please, refer to the comments included in the pdf supplement for other minor comments.