I find that the author make sufficiently clear that their method of signal processing improves the results from multi-pulse-length LIDAR measurements, in that it enables to merge data obtained from longer and shorter laser pulses and extract a better picture of water vapor distribution in the atmosphere. Therefore I recommend this paper for publication. I have only minor issues that I hope the authors might clarify. 

1) Abstract: It is not clear to me why longer pulses should blank the detector. If the energy is distributed over a longer time, the dynamic range of the detector should not be the limit. Please explain it better. Is this only due to the fact that no data can be recorded while the pulse is still on its way out of the laser? Or there are other processes involved (see following point)?

2) Page 7: "In addition to masking due to potential errors in the noise model, the long pulse channels tend to experience a bias in the lower altitudes associated with the pulse length and recovery time of the detector (the exact causes of this effect are still not fully understood and may be related to stray light, detector recovery time, afterpulsing or a combination of all three)."

How is the dependency of this effect on the pulse length. Can you give a quantitative answer?

3) Figure 5, bottom right panel. The authors discuss the noisy data (blue line), but the average value of the blue line seems to be much shifted toward higher humidity as well. Can they explain it?

4) What happens if instead of short and long pulses one used only short pulses with more or less energy? I understand that this might not be possible with the present setup, but what if?

This paper discusses the signal processing methods for combining short and long pulse signals from ground based micropulse water vapor DIAL system to reap the benefits of both in different signal regimes with a goal of enabling water vapor retrievals closer to the surface which is of interest the broader atmospheric science community.  The paper is well written, and methods are adequately discussed.  I recommend the paper to be published after revisions that land between major to minor.

Minor Comments:

1. Title: Consider adding ground-based to the title to better capture the focus of the manuscript (…from multi-pulse-length ground-based lidar data)
2. Line 2. Add ‘in duration’ after (low pulse energy)
3. There are many references to longer pulses saturating the detector and causing ‘ringing’ or non-linearities several microseconds after the 1 us pulse shuts off that limit the utility of the longer pulses for quantitative DIAL retrievals near the surface.   The amount of light on the detector from the laser flash is proportional to the pulse width. If the dominant effect limiting the utility of the 1 us pulse is the after pulsing, you should be able to show the effects of after pulsing between the long and short pulses by blocking the transceiver and directly plotting the afterpulsing between the two configurations. This would be a useful figure to convince the reader of the root cause that resulted in this work.  The effect of ‘smearing’, absent any non-linear detector effect, that limits the near field water vapor retrieval seems like a stretch and should only limit the retrieval down to ~150 m above the surface.  Having a figure demonstrating this root cause would be a good addition to the narrative.
4. Lines 45-50. Please elaborate how a direct DIAL retrieval is over constrained and to what four observations you are referring.  If this is a reference to PTV, please make clear that the constraint comes about from the use of the PTV method.
5. Lines 48-55 – The authors have made a clear case of the utility of PTV for overcome limitations of low flux lidar systems, however, it’s unclear why PTV is needed to merge data from long and short pulses for the near field water vapor retrievals. Why not use a weighted average with altitude to simple merge the two retrievals near the surface? Would this not be computationally less expensive?  Lines 69-72 indicate that a 100 m resolution is used for the near surface retrievals…is PTV needed if a single vertical resolution is employed for the near field retrieval?
6. Line 97-98. This sentence is a little confusing as you reference a detector channel being common across all four channels. Please clarify what channels.
7. Lines 148-150. This is generic statement that should be further clarified or removed without references. DIAL profiling within liquid cloud is clearly a challenge as the extinction of most liquid clouds are sufficiently high such that signals attenuate to the measurement limit well below the resolution of typical DIAL retrievals (~100-300 m).  That said, so long as the assumptions of the DIAL equation are not broken (the humidity and cross sections stays relatively constant within a range bin, correcting for Rayleigh-Doppler effects, and signals stay linear and constant between on and off signals), profiling within and near clouds is feasible.  This in practice is easier with ice cloud as the extinction is much lower.  The DLR WALES airborne DIAL has demonstrated this repeatedly and preliminary cases with the NASA HALO airborne DIAL have also been published.    The comment about integrating over heterogneous backscatter is noted.  There should be clarifying text to clarify the difference between deficiencies in lidar sampling that would result in errors, vs adequate sampling strategy (fast and high resolution) but averaging over multiple shots to get adequate SNR for a good DIAL or cloud property retrieval.  An average value (and variance if available) is still a very scientifically useful value.
   1. Wendisch, M., Crewell, S., Ehrlich, A., Herber, A., Kirbus, B., Lüpkes, C., Mech, M., Abel, S. J., Akansu, E. F., Ament, F., Aubry, C., Becker, S., Borrmann, S., Bozem, H., Brückner, M., Clemen, H.-C., Dahlke, S., Dekoutsidis, G., Delanoë, J., De La Torre Castro, E., Dorff, H., Dupuy, R., Eppers, O., Ewald, F., George, G., Gorodetskaya, I. V., Grawe, S., Groß, S., Hartmann, J., Henning, S., Hirsch, L., Jäkel, E., Joppe, P., Jourdan, O., Jurányi, Z., Karalis, M., Kellermann, M., Klingebiel, M., Lonardi, M., Lucke, J., Luebke, A. E., Maahn, M., Maherndl, N., Maturilli, M., Mayer, B., Mayer, J., Mertes, S., Michaelis, J., Michalkov, M., Mioche, G., Moser, M., Müller, H., Neggers, R., Ori, D., Paul, D., Paulus, F. M., Pilz, C., Pithan, F., Pöhlker, M., Pörtge, V., Ringel, M., Risse, N., Roberts, G. C., Rosenburg, S., Röttenbacher, J., Rückert, J., Schäfer, M., Schaefer, J., Schemann, V., Schirmacher, I., Schmidt, J., Schmidt, S., Schneider, J., Schnitt, S., Schwarz, A., Siebert, H., Sodemann, H., Sperzel, T., Spreen, G., Stevens, B., Stratmann, F., Svensson, G., Tatzelt, C., Tuch, T., Vihma, T., Voigt, C., Volkmer, L., Walbröl, A., Weber, A., Wehner, B., Wetzel, B., Wirth, M., and Zinner, T.: Overview: quasi-Lagrangian observations of Arctic air mass transformations – introduction and initial results of the HALO–(𝒜 𝒞)3 aircraft campaign, Atmos. Chem. Phys., 24, 8865–8892, https://doi.org/10.5194/acp-24-8865-2024, 2024.
   2. Groß, S., Wirth, M., Schäfler, A., Fix, A., Kaufmann, S., and Voigt, C.: Potential of airborne lidar measurements for cirrus cloud studies, Atmos. Meas. Tech., 7, 2745–2755, https://doi.org/10.5194/amt-7-2745-2014, 2014.
   3. Dekoutsidis, G., Wirth, M., and Groß, S.: The effects of warm-air intrusions in the high Arctic on cirrus clouds, Atmos. Chem. Phys., 24, 5971–5987, https://doi.org/10.5194/acp-24-5971-2024, 2024.
   4. Nowottnick, E. P., and Coauthors, 2024: Dust, Convection, Winds, and Waves: The 2022 NASA CPEX-CV Campaign. Bull. Amer. Meteor. Soc., 105, E2097–E2125, https://doi.org/10.1175/BAMS-D-23-0201.1.
8. Line 215. What decrease the histogram time to 2Hz or 4 Hz to capture variability then average further in post-processing?
9. Figures 1 and 3. Is it possible to show where the contributions are from each ‘pulse’?  It would be informative to the reader to explicitly see how these get stitched together rather than trying to squint between the panels. 
10. Line 310 – remove ‘where’ between 500 m and due.
11. Figure 5. Given the focus here is on improving the near surface retrievals and PTV has been demonstrated in previous publications to improve performance aloft, it would be ideal to zoom in on the retrievals down near the surface (below 2 or even 1 km) to better elucidate the improvements. As they stand some of the plots are busy and discerning the different lines down near the surface is difficult.  It would be good to show a vertical resolution curve for each combined PTV retrieval (on the right y axis?) to give the reader a sense of how DIAL resolution stacks up against the short and long pulse duration.
12. Table 1. It would be good to compile the statistics for low altitudes (below 1km?) and all altitudes. This way the reader gains a better understanding of the improvements near the surface relative to the overall improvement. 
13. Paragraph starting at line 349 – Great discussion on the need for future trades.