The manuscript presents an interesting study on learning from observational datasets, and the authors propose a novel approach, building on their previous work on the HIDRA family, to generate dense grid predictions. The text is generally well written; however, the model architecture is not explained as clearly as expected, and my major concerns are regarding the evaluation, explainability, and generalization aspects. How can one transfer the model to other regions, and what are the limitations?

Suggesting sections 2.3 and 3.1 to a dataset section, along with the training dataset, to improve clarity and flow. Adding a problem definition subsection before the model architecture would be beneficial. The number of figures and tables is considerably higher than in a typical paper. I suggest reducing them in the main text and relocating additional information to an appendix to avoid distracting from the main flow of the research.

L36: This statement requires an appropriate reference. Also, it is appreciated to mention the limitations and challenges of data-driven models, such as stability and physical fidelity?

L41: There are several successful models for ocean emulation and forecasting. It is unclear why the authors primarily cite their own work to demonstrate the capabilities of data-driven models in ocean applications. Also, Rus et al. is cited repeatedly in some parts of the text, which is somewhat distracting.

L57: The terminologies of sea level are mixed here. SLA and ADT are distinct terms. Some studies use these terms imprecisely; however, when SLA, ADT, MDT, and SSH are discussed together, their distinct definitions should be respected. SSH represents sea level relative to the reference ellipsoid. ADT corresponds to sea level relative to the geoid (i.e., SSH - geoidHeight) and is fundamentally different from SLA, which measures sea level relative to a given mean sea surface (i.e., SSH - MSS); and MDT = MSS - geoidHeight. Hence, it is recommended to use the appropriate terms consistently throughout the paper.

L115: By adding MDT to SLA, we will have ADT? If instantaneous satellite data are required for this task, why are OTC and DAC applied?

L126: It's not clear what adjustments were applied, and is it only for visualization? Weren't they applied for model training and evaluation?

L154-155: This sentence is not clear. Is this HIDRA models' challenge or a general challenge?

Fig.5: GT was not defined. L(s) and their arrows are confusing, perhaps require an explanation in the caption. Is block HIDRA3 frozen, fine-tuned, or jointly trained during the training of model HIDRA-D?

Fig.6: How can we intuitively explain the 2D Fourier domain? The reshape block and how the data are transformed from physical space to Fourier space are not clear.

L231: If b is due to the difference between the vertical reference surface, it should be constant or change due to vertical land movements at the location of tide gauges. Isn't it?

Table 1: Could you also add the performance of Nemo and HIDRA-D against the tide gauge over the testing period?

Fig. 7 & 8: It would be helpful to indicate the RMSE value for each panel in the bottom row.

Sec. 3.2: I suggest adding (or replacing with Fig. 7 or 8) the RMSE contour of HIDRA-D against Nemo for the lead times over the test dataset.

L323: according to Fig. 7, visually, one can observe that the difference between NEMO and HIDRA-D contains processes greater than \lambda_R=150km. Suggest comparing the radially averaged power spectrum of NEMO and HIDRA-D to discuss the spatial scales that the model can capture.

Table 3: For what lead time? As a question, is RMSE simply averaged over all tide gages, or RMSE=sqrt(mean(MSE_i)). The second form should be presented as RMSE total. I suggest adding the performance of a naïve baseline model for comparison with Nemo and HIDRA-D, in which the forecast for the next time step is simply the last observed value (i.e., yp_{t+1} = y_t).

Fig. 10,13,14: I suggest including or replacing the plot with RMSE as a better index for performance assessment. I realised that black and gray colors in the legend refer to dark and light colors, but it's not a good way to show. Please show, e.g., dark and light orange for Nemo (and similarly for HiDRA-D) in the legend or simply remove the black and gray and mention it in the caption.

Fig. 11: Are the time series hourly? Has any smoothing been applied to the tide gauge data? Have you calculated the correlation between tide gauges? I can see the neighbor tide gauges have similar behavior, so we can expect that excluding one tide gauge is unlikely to have a significant impact on model training.

L402: The variants are not clear. Has the grid size changed after reshaping, or was the feature vector dimension modified and then reshaped to 4by4 instead of 5by5?

In the model definition, it would be worth mentioning that the tensor of Fourier components is padded to produce the desired output grid (?).

L418: This is a strong claim, as the Nemo model is evaluated in an autoregressive (AR) mode, whereas HIDRA-D is assessed for single-step forecasting. I think these two evaluation settings are not directly comparable.

-- Some Minors:

Check the first line of the abstract. It's not aligned with the title regarding using satellite altimetry data.

L30: "when modeling using a numerical model", what about data-driven models? Is it only for numerical models?

L31-33: This statement is somewhat overstated. Numerical models do not always rely on ensemble modeling, as this depends on the task. Also, ensembles vary not only in parameters but also in initial conditions, boundary conditions, and forcing fields (?).

L38-41: It is expected that forecasting at a single point would be substantially faster than performing spatiotemporal Nemo forecasting?

L61: "As a rule, "?

L64-66: Please revisit these lines to ensure clarity.

The term “swath” is not commonly used for conventional satellite altimetry data, except for the SWOT dataset. Standard altimeters provide along-track measurements.

L222: "SLA represents the level relative to a reference geoid."?

Review of “HIDRA-D: deep-learning model for dense sea level forecasting using sparse altimetry and tide gauge data”

OVERVIEW

This paper presents an improvement to the existing HIDRA sea level forecasting model. The paper is interesting and rigorous, and the writing is clear. Aside from a few points and methodological steps that require further elaboration, the manuscript is well suited for publication. I suggest a minor revision.

HIGH SSH PERFORMANCE

From Table 3, it is apparent that HIDRA-D^N performs markedly worse than NEMO for high SSH levels, which are usually the ones we care about most from an impact perspective. More discussion of this point is warranted than the existing note on line 354. This suggests that HIDRA-D is not “better” than NEMO at least for some applications, particularly coastal flood warning, a point which should be highlighted in the conclusions. Do you have an understanding or theory as to why the model performs worse on high SSH? Might there be a way to correct it that would not harm the model’s overall skill?

MINOR POINTS

Line 218: “The resulting field is then adjusted using the land-sea mask for the Adriatic Sea”. Can you be more specific what this adjustment is?

Line 275: Describe in more detail how you arrived at these weights. Was there an objective hyperparameter tuning? Or hand-tuned? If so, what metric(s) were you trying to maximize during this tuning?

Line 281: “To progressively decrease the learning rate by a factor of 100, we utilize a cosine annealing schedule”. Unclear. Does this mean that the starting and ending LR during training differ by a factor of 100? Or something else?

Sec 2.5: More details required. Was a validation set used? Any early stopping conditions? How did you ensure overfitting did not occur?

Sec 3.1: This model description subsection seems better suited for the methods than the results.