The manuscript presents divergence and vorticity data sets from radar wind profilers in the Bejing metropolitan region separated in four triangles spanned by the radar wind profilers mesonet in this region. The authors show the derivation of the vorticity and divergence from the horizontal wind data. They present a comparison to ERA5 and propose the method for short time forecasts of rainfall events.

Data quality:

The data sets are accessible via Zenodo. The data is of good quality and interesting. However, the metadata is not documented very well, especially in the netCDF files. This needs to be improved.

I consider the manuscript suitable for being published in ESSD after the following points have been addressed:

Major comment:

• Data sets on Zenodo: Please use the possibility to include metadata (attributes) to the netCDF files. It should be possible to interpret and use them even without the readme file. The global attributes should include at least title, authors, institutions, version, contact, and date, furthermore the definitions of the triangles with the coordinates of the corners. The variable attributes should include the long name of the variable and the units. The short name according to CF standard names (https://cfconventions.org/Data/cf-standard-names/current/build/cf-standard-name-table.html) would be nice as well. The definition of the time axis should be clear within the files, i.e. at least the start time and the interval length must be mentioned. The same would be nice for the level coordinate.

Minor comments:

• Data sets on Zenodo: I would suggest to add two additional coordinates for time and altitude with UTC timestamps and heights above sea level.
• I noticed that the minimum and maximum values of divergence and vorticity are a factor of 2 larger for triangle 2 compared to the other triangles. Why is this? I suggest to add a paragraph to the manuscript where you analyse the differences between the four triangles and also give some interpretation. 
• Suggestion: Add a rain flag to the data sets on Zenodo.
• Section 2.1: I am missing more specific information about the RWPs. What type are they, what’s their accuracy for the wind measurements, give a brief introduction to the measurement principle.
• Section 2.2, l. 137 ff.: This is just a suggestion. If I got this correctly, i = 1,2,3 counts the triangle corners which are called A, B, C. So essentially, the index 1, 2, 3 is the same as A, B, C, respectively. You could get rid of this „double naming“ by saying i = A,B,C which would clarify that it means the same thing and adapt the following equations accordingly.
• Section 3: I found the comparison between the RWP data and ERA5 not very convincing, especially since the correlations shown in figure 3 are so small that it is hard to conclude that they are correlated at all. Is there any other data set you can use to verify your data? 
• Please add references to l. 190.
• I did not get the definition of z_i. Is it the PBL height or something else. Please rephrase the definition in l. 193.
• Please also rephrase the following lines 195-197. As far as I understand, you are not setting z/z_i to 2 or 1 but looking at these specific altitudes, right? In the current formulation it sounds as if z/z_i is a parameter which can be freely chosen, but it is a coordinate.
• You discuss in section 3 why ERA5 is less reliable than the RWP observations and why the pdfs are steeper. However, there are probably also some uncertainties in the RWP measurements and approximations needed. Please comment on this.
• l. 228: The method of the precipitation measurements should be mentioned.
• Fig. 4: For comparison it would be good to show the same plots for times without rain events. Otherwise it is hard to interpret how the shown behaviour is specific for rain events. This should be also further discussed in section 4 l. 240 ff.
• Section 4 and 5: You mention that the RWP mesonet can be used for rain forecasting. I am missing a more detailed explanation on how this works. Please add more discussion.
• I do not understand l. 250 f. Please rephrase this sentence.
• l. 258: Please introduce the abbreviation CI.
• l. 287 ff.: Are the data of the wind fields also published somewhere? If yes, please cite.

Technical comments:

• Please check the use of singular and plural of nouns, e.g. l. 66 stations, l. 111 "datasets" or "is" and „has“, l. 163 positions.
• Please also check if there are articles before nouns when necessary, e.g. l. 197 the two dynamic parameters (or specify the parameters), l. 511 the grey layer , l. 317 the surface. 
• l. 83: typo in calculate
• l. 278: typo twice "by the“
• L. 301: typo "which" is too much
• Equations in section 2.2: The primes (A’ etc.) are not visible. Please improve their visibility.
• Punctuation characters are missing for all equations.
• The ESSD guidelines suggest to use a sans-serif font for the figures.

This manuscript presents a high-resolution dataset of horizontal divergence and vertical vorticity profiles derived from a radar wind profiler mesonet in Beijing. Utilizing the triangle method on continuous wind measurements throughout 2023, the study provides important datasets into mesoscale convective processes and the precursors to convective rainfall events. The authors demonstrate that these radar-derived parameters differ from ERA5 reanalysis, highlighting potential shortcomings of the latter at capturing mesoscale systems. In general, the scientific merits of the dataset are clear. I would recommend the publication of this paper after minor revisions. 

Minor comments:

1. The manuscript would benefit from a clearer discussion regarding the uncertainties inherent in RWP measurements. It is recommended to acknowledge potential biases caused by vertical velocity retrieval errors, radar signal attenuation under varying meteorological conditions, and sampling inaccuracies associated with spatial-temporal resolutions. 

2. The representativeness of divergence and vorticity profiles calculated using the triangle method strongly depends on the geometry and spacing of the profiler network. It may be helpful to discuss the potential effects of the heterogeneous urban landscape and complex terrain of the Beijing region.

3. Does the triangle method inherently assume linear variations of wind fields across measurement sites? If so, could nonlinear mesoscale dynamics during rapidly evolving convective conditions significantly impact the accuracy or sensitivity of divergence and vorticity retrievals?

4. The authors highlight the potential application of this dataset for rainfall nowcasting. It may be helpful to further discuss how the derived divergence and vorticity profiles could practically benefit short-term forecasting of convective rainfall events compared to existing methods. Providing clearer context or practical examples could strengthen the manuscript’s discussion of its predictive capability and lead-time advantages.