Automated compact mobile Raman lidar for water vapor measurement: instrument description and validation by comparison with radiosonde, GNSS, and high-resolution objective analysis

Sakai, Tetsu; Nagai, Tomohiro; Izumi, Toshiharu; Yoshida, Satoru; Shoji, Yoshinori

doi:https://doi.org/10.5194/amt-12-313-2019

Articles | Volume 12, issue 1

https://doi.org/10.5194/amt-12-313-2019

© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/amt-12-313-2019

© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 12, issue 1

Research article

|

17 Jan 2019

Research article |

| 17 Jan 2019

Automated compact mobile Raman lidar for water vapor measurement: instrument description and validation by comparison with radiosonde, GNSS, and high-resolution objective analysis

Tetsu Sakai, Tomohiro Nagai, Toshiharu Izumi, Satoru Yoshida, and Yoshinori Shoji

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Final revised paper (published on 17 Jan 2019)
Preprint (discussion started on 22 May 2018)

Interactive discussion

Status: closed

AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment

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RC1: 'Review for paper amt-2018-107', Anonymous Referee #1, 19 Jun 2018
- AC1: 'Response to Reviewer #1', Tetsu Sakai, 29 Aug 2018
RC2: 'Comment by the Anonymous Referee #2', Anonymous Referee #2, 23 Jul 2018
- AC2: 'Responses to Reviewer #2', Tetsu Sakai, 29 Aug 2018

Peer-review completion

AR: Author's response | RR: Referee report | ED: Editor decision

AR by Tetsu Sakai on behalf of the Authors (22 Sep 2018) Author's response Manuscript

ED: Referee Nomination & Report Request started (01 Oct 2018) by Ulla Wandinger

RR by Anonymous Referee #2 (13 Oct 2018)

Suggestions for revision or reasons for rejection

I reviewed the updated version of the manuscript by et Sakai et al. To accommodate the reviewer’s requests, the authors spent mainly their effort in slightly changing the tone of the manuscript and to demonstrate to some extent that the system they are describing is related to the rain forecasting system, though smoothing the final goal which is clearer now but to my opinion not fully in line with the objective of AMT journal.

This is stated in home page of the journal and it is:

Atmospheric Measurement Techniques (AMT) is an international scientific journal dedicated to the publication and discussion of advances in remote sensing, as well as in situ and laboratory measurement techniques for the constituents and properties of the Earth's atmosphere.
The main subject areas comprise the development, intercomparison, and validation of measurement instruments and techniques of data processing and information retrieval for gases, aerosols, and clouds. Papers submitted to AMT must contain atmospheric measurements, laboratory measurements relevant for atmospheric science, and/or theoretical calculations of measurements simulations with detailed error analysis including instrument simulations.

So not only a description of a measurement systems or intercomparison are required but advances in measurement field must be reported
The authors state that to their knowledge, such a small mobile Raman lidar has only been reported by Chazette et al. (2015).

Reviewing the literature, the authors may easily find, for example, the following paper in addition to the one by Chazette et al.

Engelmann et al., “The automated multiwavelength Raman polarization and water-vapor lidar PollyXT: the neXT generation”, Atmos. Meas. Tech., 9, 1767-1784, https://doi.org/10.5194/amt-9-1767-2016, 2016.

This paper describes the most recent version of a portable multi-wavelength Raman and polarization lidar PollyXT equipped with a water-vapor Raman channel.

In additional we may also consult the following link:

https://infoscience.epfl.ch/record/140620

https://www.raymetrics.com/product/temperature-and-humidity-lidar

Therefore, the system described by the authors is definitely not one of the first attempt to deploy a compact Raman lidar system able to operate on a continuous basis. About the Polly XT system’s performances, they look similar to those described in the manuscript by Sakai et al., with a better full overlap height.

The authors also continue to state that the system description could be very beneficial for investigating measurement locations that are effective for the heavy rain forecasting. In literature there are other paper demonstrating the value of assimilating water vapour Raman lidar measurements in forecast models. Here, the authors refers to a paper by Yoshida et al. 2018, “Feasibility study of data assimilation using a mobile water vapor” which is a proceeding to conference of 4-pages, quite limited to fully demonstrate a big thesis like the impact of Raman lidar measurement on a forecasting system.

In the paper by Yoshida et al., the results collected over about two months-experiment are condensed in one example where there is a a certain impact due to the Raman lidar data assimilation though this is not striking when the heavier rain fall comes. Nevertheless, it s not my role to judge the other paper but I think that for sure the paper does not show a large benefit in assimilating Raman lidar water vapor measurements up to 1.0-1.5 km during daytime. Though this may be theoretically useful, it must be demonstrated that the measurements of their system, close and within the incomplete overlap region are really useful and cannot impact negatively the forecasts.

To this purpose, the authors provide an estimation of the total uncertainty for the overlap correction of at most 23%, which is quite high in a region where the atmospheric variability requires a higher accuracy for the the data to have an impact on the forecast model.

My opinion is that there is still limited interest in having another manuscript which mainly does not provide any special advance in the usage of the Raman lidar technique for measuring water vapour. The interest in the readers can only come from the fact that a new compact Raman lidar system for monitoring water vapour is available (also on the market?) and can be operated in a measurement network or at a remote station. The manuscript does not need to necessarily be linked to the scope of improving rainfall forecasting but to many other application, like for any other Raman lidar system. The manuscript does not reveal any special feature of this lidar system which can significantly improve the weather forecasts with respect to other systems previously presented in literature.
Therefore, the manuscript must be necessarily reshaped to consider the real scope of this paper: to introduce another automated compact Raman lidar for water vapour measurements, which has an affordable cost (<250K US dollars) and apparently easy to be operated. I do not think this requires a strong effort but the manuscript will benefit from this work and may be of some interest to the community.
Under these premises, if the editor feels that this is line with the scope of the journal, I recommend major revisions and I am happy to further support the Editor in any following step of this review procedure.

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RR by Anonymous Referee #1 (25 Oct 2018)

RR by Anonymous Referee #3 (21 Nov 2018)

Suggestions for revision or reasons for rejection

The paper by Sakai et al. describes the setup of a trailer with a UV Raman lidar for water vapor measurements. This paper is a useful reference for future research with this system's data and data assimilation. The authors want to use the system for heavy rain forecasting. However, I still agree with reviewer two. This paper is not about heavy rain forecasting with a Raman lidar, as the title still suggests. And therefore I would still recommend to drop this part from the title. Maybe you can find an acronym for the lidar system name, that includes "..for heavy rain forecasting". And that acronym/name can be mentioned in the title. Overall I would recommend the paper for publication, but I have some minor comments:

The general problem, when the data shall be used for short-time forecasts of heavy rain events, is probably the occurrence of clouds. Whenever the water-vapor content is critically large, there are also low clouds, which in turn prohibit optimal lidar measurements. Therefore many people use synergistic approaches with different measurement systems (such as lidar, microwave radiometer, radar, GPS, ...) for such a task. This fact should be mentioned. Lidar is an important tool but not the only required instrument here.

Table 1: This table could be skipped and the few requirements written in one sentence. There are also no proper citations for these requirements, where do they come from?

Table 2: The given beam divergence is before or after beam expansion? Please specify.

Figure 1: The two lines from Laser and PIN to the electronics, should be drawn below the receiver box. Right now one line is on top of the beam path which is a bit confusing.

P4L2: "The temperature is maintained to 22-32 degree C." That sounds not very stable. Can you find a few words on the actual stability. It seems that with a field of view of 0.3 mrad temperature stability, and thus laser beam alignment, is very crucial. Do you have any details on that, how the temperature affects the overlap alignment? Do you have a camera to check the alignment?

How is the fused silica window constructed? Do you transmit and receive through the same window? Are there problems (especially if the window is dirty) with detector overloading from the scattering of the laser beam?

It is mentioned that the data acquisition is analog and photon counting. Do you have any comments on the gluing procedure, or do you only use p.c. data, do you want to present any figure that shows the raw signals?

Fig 10, P8L39. A very narrow interference filter is used. Did you consider a temperature correction of the 407 nm signal with help of the filters transmission curve? This might be the reason for the discrepancies above 5 km height. At least mention that the issue might be relevant here.

Fig 11/13/14/ Table 3: When there is the calculation of the slope and intercept, I suggest to also give the statistical error of these values from the fit. Then you can determine if the slope difference from 1/unity is indeed significant or not.

P14L17. The first sentence misses a dot.

P17L3-5: This is a single sentence paragraph. I suggest connecting the sentence to the previous paragraph.

I suggest harmonizing the figures for equal font size, line width, and resolution.

I realized that there are not many references within this manuscript, although many have been published on the topic of water vapor Raman lidars, calibration, and assimilation. Here are just a few that might be useful:

Dai, G., Althausen, D., Hofer, J., Engelmann, R., Seifert, P., Bühl, J., Mamouri, R.-E., Wu, S., and Ansmann, A.: Calibration of Raman lidar water vapor profiles by means of AERONET photometer observations and GDAS meteorological data, Atmos. Meas. Tech., 11, 2735-2748, https://doi.org/10.5194/amt-11-2735-2018, 2018.

Foth, A. and Pospichal, B.: Optimal estimation of water vapour profiles using a combination of Raman lidar and microwave radiometer, Atmos. Meas. Tech., 10, 3325-3344, https://doi.org/10.5194/amt-10-3325-2017, 2017.

Foth, A., Baars, H., Di Girolamo, P., and Pospichal, B.: Water vapour profiles from Raman lidar automatically calibrated by microwave radiometer data during HOPE, Atmos. Chem. Phys., 15, 7753-7763, https://doi.org/10.5194/acp-15-7753-2015, 2015.

Bhawar et. all 2011, The water vapour intercomparison effort in the framework of the Convective and Orographically induced Precipitation Study: airborne to ground based and airborne to airborne lidar systems
https://doi.org/10.1002/qj.697

Herold, C., D. Althausen, D. Müller, M. Tesche, P. Seifert, R. Engelmann, C. Flamant, R. Bhawar, and P. Di Girolamo, 2011: Comparison of Raman Lidar Observations of Water Vapor with COSMO-DE Forecasts during COPS 2007. Wea. Forecasting, 26, 1056–1066, https://doi.org/10.1175/2011WAF2222448.1

Grzeschik, Matthias & Bauer, Hans-Stefan & Wulfmeyer, Volker & Engelbart, Dirk & Wandinger, Ulla & Mattis, Ina & Althausen, Dietrich & Engelmann, Ronny & Tesche, Matthias & Riede, Andrea. (2008). Four-Dimensional Variational Data Analysis of Water Vapor Raman Lidar Data and Their Impact on Mesoscale Forecasts. Journal of Atmospheric and Oceanic Technology - J ATMOS OCEAN TECHNOL. 25. 10.1175/2007JTECHA974.1.

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ED: Reconsider after major revisions (29 Nov 2018) by Ulla Wandinger

AR by Tetsu Sakai on behalf of the Authors (14 Dec 2018) Author's response Manuscript

ED: Publish as is (27 Dec 2018) by Ulla Wandinger

Short summary

We developed an automated compact mobile Raman lidar (MRL) system for measuring the vertical distribution of the water vapor mixing ratio in the lower troposphere, which has an affordable cost and is easy to operate. The MRL was installed in a small trailer for easy deployment and can start measurement in a few hours, and it is capable of unattended operation for several months. We describe the MRL system and present validation results obtained by comparing with the other humidity sensors.