Overview:

The manuscript presents the Integrated Multi-source Polar Meso-Cyclone Tracks (IMPMCT) dataset based on both ERA5 reanalysis and remote sensing data during winter in the Nordic Sea, demonstrates clearly the workflow of this method, and compares the results with existing manually identified and reanalysis-based track datasets. There remains a clear need for establishing a more comprehensive tracking dataset capable of capturing PMCs throughout their lifecycle due to their impacts on human activities and regional climate change. The manuscript is generally well-organized, and the figures effectively communicate the results while being concise. However, there are a few aspects where the presentation could be improved. The detailed comments are listed below, and I encourage the authors to make the necessary adjustments to improve the study.

Major comments:

1. The present study utilized a series of datasets, including ERA5 reanalysis, AVHRR data andQuikSCAT/ASCAT data, which have different spatial and temporal resolutions, and these data are stored with different projections/grids. How are these multi-source datasets treated in the cyclone tracking algorithm to maintain consistency? Please clarify.
2. Line 164: ERA5 data. How accurate are the ERA-5 fields used in the analysis of the Nordic Sea? What are the known biases? As the authors did not repeat their method with other reanalysis datasets to test the robustness of their results, I would suggest declaring the known biases of ERA5 in this part.
3. Line 262: To maximize the inclusion of potential PMCs, we implement more lenient vortex detection criteria compared to Stoll et al. (2021). The selected criteria seem to be very subjective. Importantly, how sensitive are the results to subjective criteria such as the “vorticity peak threshold”, “isolated vortex threshold”? Have the authors conducted sensitivity tests, and what metrics were used to evaluate the robustness of the results? Please include this.
4. It seems a YOLO (You Only Look Once) object detection algorithm is employed to detect and extract cyclonic cloud characteristics. This description of this procedure could be improved in my opinion. The authors start by generally describing the structure of the YOLOv8-obb model on line 377, with so many acronyms. However, the specific process by which this algorithm works to detect cloud features was oversimplified in the following paragraph.
5. When comparing the results from the IMPMCT to existing identified PL lists from previous studies, the authors give the difference in parameters and plot them. It is more appropriate to conduct a significance test between two samples in order to statistically validate the accuracy.
6. Figure issues:

• Specify what is plotted in Figure 1 in the name of the colorbar, same comments for Figure 3b, and Figure 7.
• The green star symbols denoting the local vorticity maxima are hard to read when overlaid on the AVHRR infrared imagery. Please change the color or enlarge the symbols. Same comments for stars in Figure 10b and wind vectors in Figure 11.
• The unit of the colobar in Figure 7a should be 1e^-4s^-1

Minor comments:

Lines 41-42: Add references about this statement.

Lines 59-61: Add references about this statement or remove it as it seems irrelevant to the core points of this paragraph.

Lines 129-131: Moreover, fundamental questions persist regarding the differences in formation mechanisms between PMCs and PLs, and whether PMCs can transition into PLs under specific meteorological conditions. This question seems not to be addressed.

Line 138: Winter should be defined here rather than in the Data part.

Line 140: “multi-dimensional” to “multiple”

Line 161: “sourced” to “obtained”

Line 169: delete “for atmospheric, land, and ocean variables”

Lines 191- 192: Notably, QuikSCAT data spans only 1999–2009, while ASCAT has remained operational since 2010. Rephrase to: QuikSCAT operated from 1999 to 2009, whereas ASCAT has continued operations since 2010.

Lines 281-284: “Specifically, for a vortex at a given time step, its ideal point after experiencing a time step under the steering wind influence is first calculated A search radius of 180 km is then applied around this estimated location to facilitate vortex tracking in subsequent time steps..” Should be two separate sentences.

Lines 293-294: Rephrase to: If no spatially connectable vortices are identified in adjacent time steps, the vortex is classified as being terminated.

Lines 316-319: Rephrase to: Building upon the lenient vorticity identification criteria established in prior analysis, a substantial population of vortex tracks has been identified within the reanalysis dataset. This collection encompasses not only cyclonic systems but also terrain-induced shear flows, low-pressure troughs, and small-scale atmospheric disturbances.

Line 373: Delete “deliberately”

Lines 391-393: Rephrase to: To ensure prediction stability, particular emphasis is placed on maintaining consistent oriented bounding box annotations and center point positions across similar evolutionary phases of cyclonic cloud morphologies.

Linee 409-413: Rephrase to: To remove duplicate records, we implement a selection criterion: for any cluster of detections from the same AVHRR infrared scan (with cyclone centers <50 km apart), only the detection whose center is nearest to the VCI image center is retained.

Line 436: Delete “frequently”

Lines 453-455: Rephrase to: To reduce the influence of strong winds in the cyclone core, we use the 75th percentile of wind speeds within the extended search radius as the environmental advection speed (reference value).

Lines 484-485: Rephrase to: All reference datasets are spatially and temporally co-located with our derived tracks, retaining only those persisting for ≥3 hours.

Line 526: “extraneous” to “irrelevant”

Line 545: Rephrase to: Additionally, since the dataset includes remote sensing images of cyclones, users can easily verify the accuracy of cyclone properties and make necessary adjustments based on their specific use cases.

Line 568: “these categories” to “them”

See attached file.

Review of ESSD-2025-186 by Fang et al.

This manuscript describes a polar mesoscale cyclone dataset assembled by the authors for the study of polar meteorology. In general, it is well-written, and the steps in dataset construction are well-described. The dataset compiled here definitely is useful for the community. I have some minor suggestions and comments. Once these issues are addressed, I recommend publishing this manuscript. 

1.    Title: I don’t see a reason to use “Meso-Cyclone” as mesocyclone is a well-defined term in meteorology. It is just one word. Also, it is a database only for the Nordic Seas, not the entire polar oceans. It would be better to reflect this regional context in the title, or in the acronym defined for the dataset.

2. Line 200, “For each vortex with available AVHRR data, …” What percentage of such vortices identified from ERA5 have AVHRR data available? This info is useful for readers. As AVHRR is on a sun-synchronous satellite, it does not have full synoptic coverage for the polar region. So the percentage of actual coverage in this context needs to be described.

3.    It is not clear to me what exact cloud properties are included in the dataset beyond the cloud morphology. Any usual cloud properties such as cloud top pressure, cloud optical depth, etc., are included? If so, it’s better to specify them up front.

4.    Table 2: Why is the matched fraction with Rojo PL tracks so low compared to the matches with the other two PL track datasets? This needs to be explained. 

5.    Line 598 “IMPMCT could serves as a critical benchmark for evaluating high-latitude climate model performance.” It would be beneficial to elaborate on how a track-based dataset can be utilized for climate model evaluation. Are the tracks compiled here enough for robust statistics (related to comment #2 above)? What standard model output can be used directly for such comparison, or do climate models need to output high-resolution data to be used by track algorithms to generate similar datasets for comparison? 

6.    There are occasional English typos, e.g., “could serve” not “could serves” at Line 598. A careful proofread would be helpful. I assume ESSD might have a technical editor in a later stage for such proofreading.