This paper presents a dataset of manual and automated in situ measurements of snow water equivalent (SWE) over the Northern Hemisphere, which is called NorSWE. To the best of my knowledge, there is no in situ global SWE dataset publicly available to date. Currently, researchers need to compile such a dataset from each individual source every time they require it for their applications, and apply their own filters and quality checks, which is time and labour intensive. The scientific community will therefore highly benefit from this dataset, and it fits very well within the scope of the journal. The dataset is excellent, offers a wide range of applications (as the authors very well describe and demonstrate in the paper) and is especially timely as many global products and applications rely on actual SWE measurements and are being increasingly used by the community. The authors did an impressive data compilation and data curation work. The paper is also well written and clear and I hope to see it published soon. However, I have a few minor comments/suggestions and technical corrections that should be addressed before the paper is published.

I only have one rather major suggestion, but I call it major just because it may require a bit more time than the rest of minor comments. It regards the spatial coverage of the dataset. The dataset covers a large part of the Northern Hemisphere, but there are some gaps, which the authors recognise in Section 8 (Lines 373-380). It is true that there is a lack of observations in certain areas (e.g. high mountain Asia), and that many other SWE data are just not publicly available. I also acknowledge that it is not possible to find and include every single available dataset and that a line must be drawn somewhere.  However, there is some data available over Europe which the authors did not include and which I strongly encourage they do. These include the Global Climate Observing System (GCOS), the Norwegian Water Resources and Energy Directorate, and a few other individual sites over the Alps. The sources are well listed in Table 1 in Fontrodona-Bach et al. (2023), and they are also used and listed by Seo et al. (2025) in Table 1 https://doi.org/10.5194/essd-2024-349 (preprint). This would give some coverage (despite being still limited) to the European Alps and to Scandinavia outside of Finland, and I strongly encourage that these datasets are included. If the authors wish, they can get in touch with me and I will send them some notes on how I downloaded these data.

I do not know if the authors requested permission to each individual agency to include their data in NorSWE (if they did, maybe they should explain this in the paper). In any case, in my opinion it is necessary to include clear statements that when using NorSWE data, all the original data sources (so all source datasets) must be appropriately cited as well as the citation of this paper and the NorSWE dataset itself (which is on Zenodo). This provides clear and proper acknowledgement to previous data collections that form this compilations dataset.

I think this dataset will be very useful and I look forward to seeing this paper published and the dataset in use. The rest of comments are listed in the attached pdf. 

I appreciate the authors' effort in creating a centralized data repository for all snow data. I have a few suggestions to further enhance the paper:

1) Maybe not in this paper, I suggest that the authors consider including data from the California Department of Water Resources (CADWR) snow data and ASO Lidar SWE data when they update their data repository in the future.

2) Different data sources have distinct measurement protocols, which can make uniform quality control difficult. It may be helpful for the authors to discuss the limitations of different datasets, such as the temperature bias and the precipitation underestimation issue in SNOTEL, and reference other works that have attempted to address these challenges.

3) Other snow-related data sources, such as UA-SWE data for 4-km resolution over CONUS and satellite snow cover data, could also be discussed in the paper.

This paper presents a novel dataset of in situ snow water equivalent (SWE) measurements from North America, Finland, and Russia over the period 1979-2021. The authors clean and harmonize data from 9 distinct sources to provide these measurements in a single file. This represents, to the best of my knowledge, the first publicly-available quasi-hemispheric in situ SWE dataset. As such, I believe it will be of tremendous value to the scientific community and the methods the authors apply seem to have produced a very high-quality dataset. Overall, I would not hesitate to recommend this paper for publication soon. I do, however, have a few minor suggestions that the authors could consider to strengthen the paper even further.

First, while I recognize the tremendous amount of work that goes into wrangling all of this SWE data, there are 2 additional sources that are fairly accessible that could increase the spatial coverage over Europe, particularly in mountainous regions. One is the GCOS dataset from Switzerland, available at https://envidat.ch/#/metadata/gcos-swe-data (citation below) The other is from the Norwegian Water Resources and Energy Directorate: https://www.nve.no/vann-og-vassdrag/vannets-kretsloep/snoe/automatiske-snostasjoner/?ref=mainmenu

Second, the authors do an excellent job of outlining possible uses for their dataset, and there is one more they can consider adding. For applications where spatiotemporally continuous data is necessary, the interpolation of in situ SWE and snow depth data has created some of the highest-quality estimates, such as the 4km University of Arizona SWE product (Broxton et al., 2019). The NorSWE dataset the authors present could facilitate the creation of a high-quality gridded hemispheric products that would be of tremendous value to the research community, and I would encourage them to highlight that as a potential use case.

Overall, I think this is a high-quality dataset that will be very useful, and look forward to the publication of the paper and the use of the data.

Broxton, P., Zeng, X. & Dawson, N. (2019). Daily 4 km Gridded SWE and Snow Depth from Assimilated In-Situ and Modeled Data over the Conterminous US. (NSIDC-0719, Version 1). [Data Set]. Boulder, Colorado USA. NASA National Snow and Ice Data Center Distributed Active Archive Center. https://doi.org/10.5067/0GGPB220EX6A.

Marty, C. (2020). GCOS SWE data from 11 stations in Switzerland.  EnviDat.  https://www.doi.org/10.16904/15.