This manuscript prevents a novel method to apply optimal estimation techniques to retrieve the wind profile continuously up to 3 km using Doppler lidar measurements.  This overcomes a main limitation in current Doppler lidar wind measurements that are typically limited by the presence of aerosols and clouds in the lower atmosphere.  By providing continuous measurements up to 3 km with this method, error covariances are also created facilitating assimilation of the wind profiles into NWP models.  The benefits of this new technique over current approaches will be of high interest to readers of AMT, especially those in the remote sensing and NWP communities. However, there are a number of issues that need to be addressed, detailed below, prior to this being fully acceptable to AMT.  Most notably, additional analysis (which should be quickly and easily performed) will be necessary to support important claims that VADoe wind retrieval errors meet WMO standards for use in NWP. 

Specific Comments

1. Line 23: In addition to providing wind speed uncertainty in the abstract, it would be best to provide the vector RMSE to account for wind direction uncertainty for applications where the wind direction is important (e.g., storm mode forecasting, aviation).
2. Line 44: Suggest changing ‘stares’ to ‘measures’ or ‘points’, as stares imply the lidar may be pointed in that given position for a prolonged period of time which may not always be true.
3. Line 56: In addition to the proposed VADoe method here, there are other novel methods (e.g., Stephan et al 2019) that can be used to extend the range of lidar wind measurements that should also be referenced and discussed at least in the introduction, perhaps elsewhere. They provide enhanced range compared to VAD and likely lower error statistics than VADoe, but will not provide continuous measurements to 3 km.
4. Section 2.2: While this section is a nice detailed explanation of the VADoe technique, there are some questions that remain. Specifically, how is the VADoe technique applied when there are few or no valid measurements made from the lidar, such as when low-clouds and fog completely attenuate the signal within the lowest tens of meters?  Is a retrieval still made?  If so, should one be made?  If not, how much ‘valid’ data (measurements above -23 dB) is needed to make a retrieval?
5. Line 207: Were any other simple quality control measures applied to ensure there were not significant changes in the wind between the radiosonde launch and the lidar measurements (e.g., front passages, convective outflows, etc)? While I’m sure out of the large dataset, there’s only small fraction of instances when that may have happened given the lidar profiles are generally <8 min from the radiosonde launch, but these cases may have an outsized effect on later statistics presented. A simple filter looking for large differences in wind speed and direction throughout the sonde and lidar wind profiles could detect these cases.
6. Figure 3: Personally, I find the representation of the 1-sigma confusing, particularly when trying to compare what is shown here with what is discussed in the next (DOF ranges from 4.9 to 25.3). It would be better to show the 1-sigma as error bars around the solid line showing the mean.  An alternative option would be to add multiple dashed lines each representing the mean +/- 1 sigma.
7. Figure 4: It would be helpful to add an additional panel to show the errors associated with the OE retrieval of v. This would help the reader understand the accuracy of the wind estimate, particularly above the PBL where I assume the magnitude of the vertical striping is within the larger uncertainty of the retrieval at those higher altitudes.
8. Line 245: Additional clarification is needed here, likely requiring rewording. Are the radiosonde observations (every 3/6 hr) interpolated to a 15-min resolution for comparison of wind?  If that’s the case, this is not a good approach as there can be significant errors in interpolating over 3/6 hr gaps, and the comparison should be done by bilinearly interpolating the lidar observations around a radiosonde launch to the launch time (at the VADtrad measurement heights).
9. Line 248: While the OE retrieved profiles are inherently smooth, it’s not fair to smooth the radiosonde profiles for the comparison with the OE retrievals but not for the VADtrad measurements. By smoothing the radiosonde profiles for the OE-retrievals, the error statistics are likely going to biased low (showing better performance than the OE retrievals actually perform when compared with observations, given inherent limitations of the OE method), misleading readers. The radiosondes should not be smoothed for either comparison
10. Line 251: Wind precision estimates are available for the VAD profiles (as stated at line 77). Why are they not used here, with a similar criterion of rejecting data wherein the uncertainty exceeded 5 m/s?
11. Lines 328: The bias of the wind speed measurements of VADoe in the low SNR band is considerably worse than the referenced TAMDAR paper (bias of 0.90 m/s, vs -2.52 m/s for VADoe). I do not agree they are comparable as stated.
12. Line 330: Insufficient results are presented to support the claim here that the VADoe measurements at the low SNR band meet the WMO threshold requirement for horizonal wind in the free troposphere for global and high-resolution NWP. The WMO requirements referenced are given as the vector error in m/s.  The authors do not present the vector error, but instead only present results for the wind speed and wind direction separately.  The vector error will be a combination of these, and will be considerably worse than the presented wind speed error alone.  In order to ensure that the VADoe wind retrievals at the lower SNR are acceptable for assimilation into NWP following WMO standards, the authors must also present result showing the performance of VADoe vector RMSE. This will require the additional analysis and another set of figures, similar to Figures 7/8, with a supporting discussion.
13. Line 363-365: Similar to the two above comments, this statement must be removed or further supported with additional data analysis. The VADoe estimates at the additional effective range appear to be significantly worse than TAMDAR and may not meet WMO threshold requirements for NWP.

Reference

Stephan, A., Wildmann, N. and Smalikho, I.N., 2019. Effectiveness of the MFAS method for determining the wind velocity vector from windcube 200s lidar measurements. Atmospheric and Oceanic Optics, 32, pp.555-563.

Publisher’s note: a supplement was added to this comment on 2 May 2023.

see attached doc. needs major revisions.