the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 03 Sep 2026
Simultaneous Temperature and Wind Profiling in the Troposphere and Lower Stratosphere Using a Rayleigh Doppler Lidar with a Fizeau-Interferometer-Based High-Spectral-Resolution Receiver
Abstract. Simultaneous profiling of atmospheric wind and temperature are essential for investigating atmospheric dynamics and wave–mean flow interactions. However, conventional molecular Rayleigh lidar techniques, such as hydrostatic integration for temperature retrieval and double-edge discrimination for wind measurement, perform best in aerosol-poor regions and can suffer substantial biases in the troposphere because narrowband Mie scattering contaminates the broadband molecular signal. To overcome this limitation, we developed a high-spectral-resolution lidar (HSRL) receiver based on a Fizeau interferometer and a 32-channel linear photomultiplier-tube array, which spatially resolves the Rayleigh–Brillouin (RB) spectrum across the detector array, allowing Doppler shift and thermally induced spectral broadening to be determined simultaneously and thereby enabling joint wind and temperature retrieval without frequency scanning. Numerical simulations were first performed to optimize the spectral sampling characteristics of the receiver, identifying the 32-channel configuration as a favorable compromise between spectral resolution and signal-to-noise ratio (SNR) over the 2–20 km altitude range. Field measurements were conducted in Urumqi, Xinjiang, China (43.8° N, 87.6° E). Wind profiles were retrieved from 2 to 20 km with a temporal resolution of 10 min, while temperature profiles were independently retrieved from 5.3 to 20 km with a temporal resolution of 5 min. The vertical resolutions were 60 m below 10 km and 300 m above 10 km. Compared with radiosonde measurements, the retrieved temperatures showed mean absolute temperature differences of 1.19 K at 5.3–10 km and 2.10 K above 10 km. Below 5.3 km, wind retrieval used model temperature as an auxiliary input, yielding mean absolute differences of 2.34 and 2.50 m s−1 for the zonal and meridional wind components, respectively. In the fully lidar-based joint-retrieval region above 5.3 km, the corresponding wind differences were 1.84 and 2.97 m s−1 at 5.3–10 km and 3.02 and 3.08 m s−1 above 10 km. These results demonstrate that the proposed scan-free multichannel Fizeau-interferometer HSRL enables reliable temperature profiling from the middle troposphere to the lower stratosphere, while extending wind measurements into the lower troposphere. The proposed receiver provides a compact spectral architecture with potential for future airborne and spaceborne Doppler lidar applications.
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Status: open (until 20 Oct 2026)
- RC1: 'Comment on egusphere-2026-5254', Anonymous Referee #1, 04 Sep 2026 reply
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RC2: 'Comment on egusphere-2026-5254', Anonymous Referee #2, 15 Oct 2026
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Reviewer Comments
Manuscript Title: Simultaneous Temperature and Wind Profiling in the Troposphere and Lower Stratosphere Using a Rayleigh Doppler Lidar with a Fizeau-Interferometer-Based High-Spectral-Resolution Receiver
Author(s): Liangyu Pu et al.
MS No.: egusphere-2026-5254General Comments
This work demonstrates the simultaneous retrieval of temperature and wind vertical profiles using a Rayleigh Doppler Lidar with a Fizeau-Interferometer-Based High-Spectral-Resolution Receiver. This is an interesting work that expands the capability of the existing USTC Doppler lidar by applying modifications to the instrument. The manuscript describes the methodology and demonstrates the measured temperature and wind profiles. In my opinion, it is essential to convince the reader in the feasibility of these measurements with the presented hardware setup. However, the manuscript lacks details on assessment of the hardware performance. The authors do not discuss the difficulties in correcting instrumentation errors, calibration aspects, nor evaluate the measurement errors limited by the actual hardware performance. Below I provide specific comments followed by few more general comments and technical corrections.
Specific Comments
- Lines 13–16: The authors statement “To overcome this limitation” is not completely valid because the described approach only partially addresses the signal contamination problem by Mie scattering. In this manuscript the temperature retrievals are limited to 5.3 km and above. Please rephrase the statement to indicate that the technique partially addresses the contamination problem.
- Line 45: An alternative approach for atmospheric temperature profiling that is immune to contamination by Mie scattering is based on iodine-vapor-filter HSRL. Please consider referring to this alternative approach and cite the relevant work by Hair et al., “High-Spectral-Resolution Lidar with Iodine-Vapor Filters: Measurement of Atmospheric-State and Aerosol Profiles,” Applied Optics, 40, 5280–5294 (2001).
- Line 176 and Fig. 2(a): The authors state that the instrument has a coaxial transmitter–receiver configuration. However, the conceptual image shows a biaxial arrangement of the instrument. The biaxial configuration would explain the lower 2km blind zone that is not discussed in the manuscript. Please clarify the instrument configuration.
- Line 178: It is likely that the instrument’s hardware components such as wavelength meter as well as the laser frequency locking scheme and frequency long term stability were previously described in earlier papers. However, it would be valuable to either provide more detailed hardware description in this manuscript or to refer to the previously published paper that includes more details.
- Line 185: Please consider specifying the manufacturer and model number of the linear PMT array.
- Equation (12): Equation 12 estimates the number of the photons detected by each channel. Assuming the convolution with the transmission function of the receiver accounts for the fraction of the N total photons in each channel, dividing N(z) received photons by N number of channels can be a duplicate in normalization. Please clarify the normalization in the Equation 12.
- Line 230: Please revise the terminology regarding returns and detector counts: “the total number of returned photons”. The dark counts do not contribute to the “returned photons” since they do not originate from the returned photons, but rather contribute to the total number of counts. Please distinguish consistently between “returned photons” and total number of counts.
- Lines 240–250: The optimization problem is a trade-off due practical constraints and often does not have a single optimal solution. According to the presented metric shown in Figure 3, the optimum detector channel number appears to be 16. However, the performance is not significantly different between cases for 8 and 128 channels provided the proposed metric has an offset of only 1km. Intuitively, with increasing number of detectors the dark counts start to dominate the returns and degrade SNR. The opposite is true for decreasing number of detectors. What is the minimum number of spectral bands are required for the accurate measurements? The presented color map shown in the Figure 3 has a vertical resolution of 1 km. Later in the manuscript the profile retrievals use coarse 300 m vertical resolution above 10km. It is unclear whether the calculations demonstrated in Figure 3 are accounting for larger vertical integration above 10 km or authors use 1km vertical resolution in the modeling. Please clarify.
- Section 4, Fig. 5: The authors show a rapid convergence of their retrieval algorithms demonstrating a mean error across the altitudes 2-4 km and 5-20 km. Please explain how the algorithm is converging within single cycle independent of the amplitude of the original perturbation and whether it demonstrates the same convergence rate when multiple parameters are perturbed. Convergence of the mean error of the retrieval do not represent the convergence of retrievals at individual altitudes. Assuming Figure 6 represents the converged result of the same simulation, it then demonstrates the resulting biases of the retrievals at individual altitude rather than the mean error of the profile. It would be more indicative of the successful convergence if the authors can demonstrate the convergence for individual retrievals.
- Fig 6, lines 285–289: The authors state “As shown in Fig. 6, the retrieved temperature exhibits a noticeable negative bias between 2 and 4 km. This underestimation is mainly caused by the increasing contribution of Mie scattering in the lower troposphere.” However, authors do not explain an introduced contamination by Mie signal for the presented modeling result. In my opinion, the temperature retrieval bias profile shown in Figure 6(a) experience a positive linear trend below 5km. This is hard to attribute it to an existence of such aerosol gradient in the lower atmosphere up to 5km. Please, provide an explanation on the mechanism that causes this bias or explain the modeling process if aerosol profile was used in the model to intentionally introduce the bias.
- Fig 8(a–c): The authors report the measured and the fitted spectra at 4.5, 4.6, and 4.7 km with changing contribution of Mie scattering. Based on the width of the Mie peak in the modeled spectra, the fitted curve represents a model of RB + Mie spectra not convoluted with the instrument function (laser spectra and receiver’s transmission function). However, the authors comparing the calculated RB spectra with the measured one. Please explain the purpose of such comparison. In my opinion, the shape of the measured curves is asymmetric and visually would not be even closely matching the fitted curve once it is convoluted with the measured transmission functions and laser spectrum from Figure 7. The fitted curves have narrow peak from Mie scattering that does not appear in the measured signals even at 4.5 km. I believe, the asymmetry in the measured spectra is indicative of the asymmetry in the receiver’s transmission function. For reference, Figure 7 shows a measured receiver’s transmission functions in the laboratory setting with a much smaller skewness. I assume that field measured spectra are affected by the difference in illumination of the Fizeau vs in the laboratory setting, or less likely by possible etalon/Fizeau drift. Please explain such large discrepancy in the two spectra and describe the correction procedure of the transmission function that allows to achieve such a good accuracy for the temperature profile above 5.3 km. Also, the lidar temperature profiles presented in the Figure 8 do not appear to have a native resolution of 60 m below 10 km and 300 m above 10 km. Please specify what type smoothing was applied to the profile and present an unsmoothed version of the lidar temperature profile.
- Table 1: Please include in the table 1 more technical parameters of the hardware: the laser manufacturer and model, frequency-locking accuracy, and long-term frequency drift as these are important parameters for reliable operation. Table 1 contains the duplicated “Transceiver” headings. Please replace them with “Transmitter” and “Receiver” where appropriate. Later in the manuscript authors report the laser linewidth of 0.67 GHz, while in the table the specified value is “<1GHz”. Consider replacing it with more accurate value of 0.67GHz. Please also clarify what optical components “optical efficiency” parameter accounts for.
- Table 2: In Table 2 authors specify parameters for the Fabry-Perot etalon. However, it is unclear if this parameters are for the original etalon, or when it is setup to operate in Fizeau interferometer mode. The plates misalignment will affect the peak transmission and the spectral bandwidth. I believe, the “spacer thickness” is more commonly used terminology than “spacer length”.
Other General Comments
- Please demonstrate the measured spectra and the fitted RB function convoluted with laser spectrum and receiver transmission spectra for the aerosol free altitude above 5.3 km with robust temperature measurement as a good example of the fit.
- Please provide an example with a thin cirrus cloud to demonstrate that Mie scattering signal contamination is entirely confined within the cloud. That will demonstrate how robust the temperature measurements are. The attenuated backscatter profile would be useful for this assessment.
- Please explain if the measured instrument receiver’s transmission spectrum measured in the laboratory setting is representative of the receiver transmission spectrum for field operation. Include any adjustments you apply to calibrations to account for the offsets ,for example, in detector gain, laser pulse characterization, and indicate if you apply any range dependent corrections to the receiver’s transmission function.
- Systematic Error budget: Please, provide an error budget estimate due to instrument hardware limitations: long term laser frequency locking accuracy and frequency drift, laser spectrum stability, interferometer drift, detector gain drift/afterpulsing if relevant.
- Indicate if the measurements can be performed during day time or are limited only to the night operation.
Technical Correction
- Fig 2(a): The presented conceptual image lacks clear labeling, “faint” fiber connection. Please consider replacing this conceptual 3D image with a 2D optical schematics. Also, consider to use a presentation style similar to Xia et al., “Stratospheric temperature measurement with scanning Fabry-Perot interferometer for wind retrieval from mobile Rayleigh Doppler lidar,” Optics Express, 22, 21775–21789 (2014). I believe this paper describes the same instrument.
- Fig 2(b): The photograph appears to be not informative. I suggest replacing it with 2D schematics of the Fizeau optical layout or use a semitransparent 3D CAD rendering. Please use font colors with higher contrast for labels.
- Fig 2(c): The photograph is not informative and appears to show a black shipping container. Please consider replacing it with an image of the actual instrument or semitransparent 3D CAD rendering.
- Line 193: Please replace “fineness” with “finesse” throughout the manuscript, including the tables.
- Line 320: Please replace “Mie scattering energy” with “Mie scattering signal” for consistency with the terminology.
- Throughout the manuscript: The authors report simulated temperature and wind retrieval error with two decimal places. This may be confused with the measurement accuracy of the instrument. Please reconsider how you report the results of these simulations to make it consistent with the error estimates.
Citation: https://doi.org/10.5194/egusphere-2026-5254-RC2
Data sets
Observation data of a High-Spectral-Resolution Lidar (HSRL) Liangyu Pu et al. https://doi.org/10.57760/sciencedb.00zrn
Model code and software
Fizeau-HSRL Forward Model (1.0.0) Liangyu Pu https://doi.org/10.5281/zenodo.22196460
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Liangyu Pu
Tingdi Chen
Yuli Han
Zhaowang Su
Yiming Song
Zhu Meng
Yuxin Wen
Chong Chen
Dongsong Sun
Xianghui Xue
Xiankang Dou
Atmospheric wind and temperature are essential for understanding weather and atmospheric dynamics. We developed a new lidar receiver that measures both quantities from the same atmospheric backscatter signal without frequency scanning. Field experiments demonstrated wind measurements from 2 to 20 km and simultaneous wind and temperature measurements from 5.3 to 20 km, with good agreement with radiosondes.
Atmospheric wind and temperature are essential for understanding weather and atmospheric...
The paper manuscript “Simultaneous Temperature and Wind Profiling in the Troposphere and Lower Stratosphere Using a Rayleigh Doppler Lidar with a Fizeau-Interferometer-Based High-Spectral-Resolution Receiver” by Pu et al. presents a novel lidar system based on an imaging Fizeau interferometer and a multi-channel PMT array that is used to resolve the Rayleigh-Brillouin spectrum for measuring temperature and wind profiles. Although the underlying technology is not new, this is the first time that it has been used to measure both temperature and wind simultaneously and up to 20 km, which is indeed very interesting for atmospheric science applications. The paper is clearly written and well structured. Regarding the scientific presentation, however, I have identified a few major issues, as can be found in the attached pdf. In my opinion, it is important to address these points accordingly before the paper can be considered for publication in AMT.