I would like to extend my congratulations to Bellinghausen et al. for their meticulously researched, comprehensive, and well-crafted manuscript. Their work is both concise and impactful. By leveraging machine learning to improve the early prediction of storm surges, their research holds significant potential for benefiting society and the risk community. 

If the model's performance indeed surpasses that of currently feasible hydrodynamic and empirical models, as suggested in the manuscript, it could serve as an excellent and computationally cheap tool for early prediction of storm surges in the Baltic Sea after which more expensive models can be implemented. Additionally, this manuscript serves as a motivation for further exploring machine learning models in the field of disaster risk management.

I only have few suggestions for further improvements:

    1.    The manuscript can be bolstered by including more comparisons with other hydrodynamic and empirical models, especially if any of them are currently used for early prediction in the Baltic Sea.

    2.    Fig 1 - As a minor suggestion, changing the white color of continental mass to gray shade would help with readability. Since white is often used to represent water on similar maps, it took me some time to understand it.

    3.    Line 186 - The acronym PF (prefilling, I assume) is used for the first time without defining it.

    4.    Line 192 - Could you please clarify whether the linear de-trending and 95th%ile selection as positive labels were done prior to or after splitting the data into M_T and M_V? If these were done prior to the split, data leakage would have occurred in training by having included information from the validation set. This could raise questions about the validity of the model performance. This would be less of a concern if it can be demonstrated that the linear trend of the mean is parallel to the x-axis (i.e., not varying by time), and the frequency of occurrence of 95th%ile events remains consistent over the two datasets.

    5.    Line 194 - DJF is defined on page 510 while first used here. Suggest moving the definition to first usage.

    6.    Line 232 - u10 -> U10

    7.    Line 239 - interpretable *models*

    8.    Line 253 - It would be helpful here to include whether the 75-25 split was done temporally similar to M_T and M_V, and the reasoning behind that decision.

    9.    Line 327- The statement is unclear about why TTPR is more sensible than VTPR. 

    10.    Section 5.2 - Suggest adding the time lag range used in this experiment, in addition to in Table A5.

    11.    Figure 13, 15, 16 - Similar as comment 9. Using VTPR and TTPR selectively for different stations gives the impression that only the best TPR is shown. To remain consistent with the text, I would suggest only providing VTPR.

    12.    Table A2 - The predictors are represented in uppercase in text, but lowercase in the table. Suggest changing all to same case.

This paper presents a machine-learning approach to predict the occurrence of extreme sea levels (storm surges) at the Baltic Sea coast using a Random Forest Classifier. The approach uses atmospheric variables (sea level pressure, wind speed components and participation rates) and the pre-filling of the Baltic Sea as predictors.  The predictand is the occurrence of storm surges, defined as water levels exceeding the 95th percentile, with observations taken from the GESLA database. Results show that the model can predict the occurrence of storm surges with some degree of accuracy, achieving true positive rates in the validation dataset of approximately 70-75% at most stations for lead times of up to 3 days. The authors thoroughly analyze the results, exploring feature importance and generating Predictor Maps to connect the physical processes driving storm surges with the performance of the model.

The paper is well-written, and the authors provide a comprehensive explanation of their model development, which is commendable for reproducibility, and they provide a thorough analysis of the results.

My main concerns with this study are:

- The authors chose to implement a Random Forest Classifier to predict whether or not a storm surge occurs (i.e., binary classification). While this is useful, it is not as practical as predicting the actual storm surge levels, which would provide much more actionable information for decision-makers. While I am not suggesting a rework of the analysis, I wonder if the authors considered using a Random Forest Regressor to predict the actual storm surge values instead of just the occurrence. This could offer a more valuable forecast for real-world applications.

- My second concern is related to the accuracy of their model. The authors compare their model’s performance to the global ocean reanalysis by Muis et al. (2016) and show that their machine learning model outperforms it. While this comparison is very promising, it is important to note that the Muis reanalysis was carried out more than 8 years ago and was not validated specifically for the Baltic Sea. More critically, the VTPR reported by the authors are around 75% at best. Does this mean that 1 in 4 storm surges could go unpredicted? And the VTPR is even lower for other stations. This raises the question of whether this level of accuracy is sufficient for practical purposes. Is there any comparable work in storm surge prediction that achieves a similar or better performance? In other words, is a VTPR of 75% a good benchmark?

Other minor considerations:

- The authors may want to consider incorporating sea-level pressure gradients as another predictor for storm surges (see for example, https://doi.org/10.1016/j.apor.2023.103496). 

- The authors use VTPR and TTPR depending on which one is better for a given station. I suggest using only VTPR for consistency.

- The title, specifically "at weekly timescales" can be misleading. The model is shown to be accurate only for lead times of up to 3 days, with accuracy dropping significantly for longer lead times.

Review comment on “Using Random Forests to Predict Extreme Sea-Levels at the Baltic Coast at Weekly Timescales” by Bellinghausen et al.

Summary

The manuscript explores a machine learning method to predict daily extreme sea levels (>95^th percentile) along Baltic coast using a Random Forest Classifier and seven tide gauge stations. Predictor variables, chosen for their physical relevance, are sea level pressure, surface wind, precipitation and prefilling. The manuscript has the advantage of simplicity, practicality and low computational requirements, which are attractive features for implementation in early warning systems. Interestingly, the proposed method captures the regional variability in the predictors of extreme sea level but requires further discussion and comparison with similar predictive methods in the same region or under similar conditions. More clarity on the methodology is needed for the reproducibility of the model.

Major comments

I would suggest revising the title as it lacks accuracy in two ways. First, the method proposed can predict extreme sea-level occurrences but not the magnitude. Second, as stated by the authors, “for lead times longer than 3 days, the predictive skill degrades” so I wouldn’t go as far as stating that the prediction is at weekly timescales.

The sentence in line 12 states that the predictive skill of the proposed method is higher than that derived from storm-surge reanalysis of dynamical models. This statement needs to be proved in the discussion providing comparisons with previous studies and direct references.

How would the presented method complement or improve the regional models in operation in the Baltic Sea (e.g. the BSHcmod mentioned in the Introduction)? Could the authors compare the percentage of true or false warnings or the computational time required?

I understand the complexity of comparing the results with operational forecast systems in the Baltic sea due to the lack of available forecasts at lead time and therefore appreciate the comparison with extreme storm surge data from GTSR. However, I do not understand the choice of comparing in Table 1 the GTSR results with the best RF TPR as well as taking the testset or validation set depending on the station. A fair comparison between performances should show the same predictors and dataset for the RF.

As stated in line 2022 the authors decided to only test combinations of predictors that could theoretically explain the storm surge. However, given the moderate sensitivity obtained (70%), could the authors justify the missing processes/variables that would have been relevant to include or make any assumptions on how would the result change by letting the model derive the most important features?

A major concern is the sometimes arbitrary use of the results based on the test (Mt) or validation (Mv) dataset (see Line 324-325 “We selected promising results based on a combination of the TPR of the test dataset from Mt and validation datasets Mv”). This practice can inflate the perceived success rather than providing a fair assessment of its performance. Can the authors specify the criteria used to select the results or choose a single dataset for all the results to allow better comparison and have the other as a supplementary information?

Minor points

Line 2: Use “machine-learning” or “machine learning” consistently along the mauscript

Line 7: European Centre for Medium-Range Weather Forecasts

Line 13: Use “tide-gauge” or “tide gauge” consistently along the manuscript

Line 56: Remove “Also” for better English phrasing

Line 58 to 72: This is a descriptive synthesis of previous studies (Tadesse et al., 2020; Bruneau et al., 2020; Tiggleloven et al., 2021) that breaks the flow of the introduction for the reader switching from past and present verbal tenses. I suggest rewriting for better clarity and coherence with the scope of the manuscript.

Line 65: I suggest adding “e.g.,” before “Bruneau et al., (2020)” given the wider literature available (see “A Review of Application of Machine Learning in Storm Surge Problems” by Qin et al., 2023).

Line 61: Change across manuscript “sea level” with “sea-level” for coherence

Line 80: The acronym GESLA has already been expressed in the abstract and used in line 61

Line 83 and Line 94 unnecessarily repeat the formula “for the reader unfamiliar with…”

Line 165: the phrase "should be representative" implies an assumption rather than a statement backed by evidence or verification. What was the rationale behind the selection of the stations?

Figure 2: Along the text the stations are referred with their name (Line 344), with their number (Line 23) or with number and name in brackets (Line 426). I suggest a simpler notation, e.g. always referring to them with their name given the fact that they’re all different. Otherwise, I suggest keeping the numbering next to the crosses in the following figures (e.g., Figure 18) so that the reader knows which station is which without having to scroll back up to check Figure 2.

Line 182: This might be a personal preference, but I would remove Table A2 given the open-accessibility of ERA5 documentation with a full description of the parameters available to download.

Line 186: Specify that PF stands for prefilling

Line 193: correct time-series to timeseries for coherence with the rest of the manuscript

Line 214: Why if the predictand timeseries starts in 2005 does the Mt period start in 2009?

Line 228 and Figure 5: DT acronym already specified above

Line 232: It would be relevant and interesting for reproducibility to specify what questions and thresholds were applied for the predictors and how were they decided

Line 253: Were the authors using a random split or a chronological split that accounts for the fact that the sea-level predictions are time dependent?

Line 257: Remove brackets for Breiman 2001

Line 298: (Müller 2017)

Figure 7. The grey squares pointed out by the text box “Top 1% feature importance (grey squares)” could be referred to in the figure caption for better visibility of the plots. There is also a “b)” above the text box that I believe doesn’t belong to the figure. Also, replace “difference” with “different” in the caption.

Line 347: Not specified that AoI stands for area of interest

Line 370: The Baltic Sea is sometimes referred to as BS, sometimes as Baltic Sea. Choose one and maintain along the manuscript.

Figure 11: “The percentage indicates the corresponding VTPR or TTPR” and Figure 13: “Depending on the station VTPR or TTPR is shown”. Without a justification selecting the best case scenario between validation or test true positive rates potentially obscures the model’s performance across different datatests giving the impression that the model performs better than it actually does. I suggest showing the results from both periods also for better evaluating the model’s sensitivity to different time periods.

Fig. 12 and Fig. 14 are not mentioned in the text.

Line 505: Replace “We will” with a present or past tense.

Line 511: There’s already many acronyms and this is only used once, I suggest specifying September, October, November.

Line 514-517: If the authors intend to validate their machine learning model’s performance why are they not taking the GTSR as the baseline?

Table 1: Why is the testset used instead of the validation set for stations 3 and 4?

Line 560: timeseries

Line 569: remove brackets in “by Tyralis et al., 2019”.

Line 616: The reference paper of ERA5 by Hersbach et al., 2018 should be fully cited in the references.

Tables A4 to A9 can be reduced into a single Table as the legend to interpret them would be the same and there’s no need to have the experiments separated.