the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 16 Oct 2024
The ATMONSYS water vapor DIAL: Advanced measurements of short-term variability in the planetary boundary layer
Abstract. High-resolution measurements of water vapor concentrations and their transport throughout the turbulent planetary boundary layer (PBL) and beyond are key for an enhanced understanding of atmospheric processes. Therefore, data from the mobile atmospheric monitoring system (ATMONSYS) differential absorption lidar (DIAL) is presented for the first time. The ATMONSYS DIAL has been developed with the goal of resolving turbulence throughout the PBL at a sampling frequency of 10 s and vertical resolutions of less than 200 m. General measuring capabilities during high-noon, clear-sky, summer conditions with a maximum vertical measurement range of >3 km and statistical uncertainties of <5 % are demonstrated. The analysis of turbulence spectra shows an overall good agreement with Kolmogorov's law, demonstrating its general capability of resolving turbulence, although deviations to the Kolmogorov behaviour can be observed at certain frequency ranges. By the combination of the ATMONSYS DIAL with an adjacent high-quality Doppler wind lidar, some of those deviations are evaded in the co-spectra due to independent noise of both instruments. However, the intermediate deviations from the expected Kolmogorov behavior in the co-spectra persist. Under consideration of the surrounding landscape, an impact of present surface heterogeneities on those intermediate frequency deviations seems plausible. Agreement of the co-spectra with Kolmogorov's law at the highest frequencies reveals that the ATMONSYS DIAL is capable to resolve turbulent latent energy fluxes down to the measurement's Nyquist frequency of 5 · 10-2 Hz. A system cross-intercomparison of the ATMONSYS DIAL with two adjacent water vapor Raman lidars and radiosondes shows good agreement between all sensors, despite minor DIAL deficiencies under certain conditions with shreded clouds surpassing the lidar. Observed profile-to-profile DIAL fluctuations and sensor-to-sensor deviations, in combination with low statistical uncertainty, show the advantage of humidity lidars, such as the ATMONSYS DIAL, to capture both short-term and small-scale dynamics of the lowermost atmosphere.
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Status: open (until 06 Dec 2024)
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RC1: 'Comment on amt-2024-168', Anonymous Referee #1, 25 Oct 2024
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Summary
The paper describes an instrument designed to continuously profile water vapor in the lower atmosphere at short time scales (10s averaging for Nyquist frequency of 0.05Hz). The instrument design and relevant specifications are described. An hour of high resolution data in the PBL and above are shown as examples. The goal is to resolve turbulence and an analysis of turbulence spectra are highlighted. The instrument is compared to radiosondes and state-of-the-art Raman lidar systems. Overall the paper achieves its objectives but there are some concerns and issues that need to be addressed.
Specific comments: major issues and concerns
- Calibration of the DIAL needs to be discussed further. The thinking and explanation are not very scientific
- (line 87) "The DIAL technique is advantageous for measuring water vapor for several reasons, most important because it is inherently self-calibrating by its working principle". Then on line 205: The Water vapor DIAL is found to have a bias at low ranges. Then Line 210, the biases are calibrated away using radiosonde data. This removes one of the most useful benefits of DIAL, so Please discuss the magnitude of the problem, and calibration procedure further.
- Line 206 "However, due to the DIAL principle and the instrumental setup, this cannot be a classic overlap issue." Why not?
- Line 207 "Therefore, we assume that there has been an issue with a detector overload which leads to this artifact." Shouldn't you be able to tell if the detector is saturating? It later is indicated that the feature that would allow the authors to tell if there are issues with clouds was turned off, is that correct?
- The calibration makes many of the comparisons not very compelling, such as at Line 604. “4 systems show good agreement in the lowest 2 km above ground”. Have not all of these systems been calibrated to the radiosonde below 1.1 km?
- Rayleigh-Doppler errors in DIAL
- In the simplified version of the DIAL equation (Eq 1) it seems the outgoing and return absorption coefficients being additive is not correct. [See Bösenberg 1998 Eq. 10 and 11] Furthermore it is suggested to have the G term in Eq 1 written out or referenced.
- The authors discuss that DIAL is subject to RD errors under two regimes, (molecular backscatter higher than aerosol, and at strong aerosol gradients), then proceed to not apply a correction due to its difficulty (and/or its introducing more uncertainty). At this point, it seems relevant to note several papers in the recent literature that solve the Rayleigh Doppler problem in DIAL by simultaneously measuring the molecular to aerosol scattering ratio (backscatter ratio). This was done with a high spectral resolution lidar (HSRL) channel. This seems particularly applicable to the ATMONSYS instrument as it is well positioned to measure the backscatter ratio at 355 nm from the Raman lidar, or at 532 nm with an I2 HSRL channel (likely better as closer in wavelength). Suggest that the authors acknowledge this method as a possible means to have a reliable RD correction. This would also allow ATMONSYS to offer robustly calibrated aerosols measurements and remove reliance on Klett inversions (one of the many drawbacks of this technique shows up in Figures 6 and 7 - and noted in the caption of Figure 7)
- The above is relevant as discrepancies with the radiosondes and Raman lidars may be due to gradients (RD error) as mentioned in line 630. And the proper implementation of the correction term calls for further research (line 634). The “shreds of clouds” mentioned in line 635 as the most probable cause of discrepancy seems also to be gradient problems (RD errors) but this could be addressed with an HSRL channel.
- Line 39 Numerical Weather Prediction. There are confusing science drivers for this instrument application.
- This instrument is well-suited for short time scales – the science driver mentioned in the opening sentences of the abstract. And again in line 27 understanding the humidity transport process (process studies) is a very reasonable science objective.
- But the science driver given on line 41 needs clarification. The WMO OSCAR requirements have uncertainty, temporal, vertical and horizontal requirements (note these are listed as ‘goal’, ‘breakthrough’,and ‘threshold’). So is this uncertainty requirement for process studies, or for regular observations? This instrument is not well suited to improve the numerical weather prediction for routine monitoring as it would be impractical to meet the horizontal requirements. In the same sense that it is not economically feasible to make the radiosonde observations at sufficient horizontal spatial scales to improve weather forecasts. Line 391 again references the WMO criteria <5%. And finally, line 660 references this criteria again, and alludes to monitoring and data assimilation. This does not appear to be a realistic science driver for this instrument.
- Line 197 Spectroscopic T and P dependency
- Line 197. Please explain the rationale behind using radiosondes to inform the water vapor spectroscopic line parameters. The data is short, presented from 1 hr of a single day. Yet the radiosonde becomes uncorrelated in time (the radiosonde apparently was at 10.75 UTC and used to evaluate the period 11.6 to 12.6 UTC, correct?). Would not surface measurement of the T and P (assuming a lapse rate and hydrostatic equation to get profiles) provide better results? Reanalysis data would yield even higher quality data, if that was required.
- Planetary Boundary Layer heights
- Throughout the measurement section (lines 272 - 354) the planetary boundary layer heights (PBLH) are discussed. Moisture and aerosol gradients are used synonymously with PBLH. But, as is well known, these methods are proxies for the PBLH and can fail for a variety of reasons. The aerosol lidar community often overlooks this issue. The authors have the means to measure the PBL height directly using thermodynamic buoyancy (virtual potential temperature from radiosondes) or kinematics (Doppler lidar). For example, the vertical wind velocity in Figure 7 provides evidence that the PBL is around 1 km above ground level at 12 UTC (automated methods to derive the top of the PBLH from this data based on the bulk Richardson number exist). At minimum, why not use these direct methods as proof that the proxy gradient methods are correct for the time shown? This is important to provide more confidence for claims as in Line 396 “This is again an indication for the position of the PBL top” and the analysis that follows.
- In cases where this is not possible (perhaps the overview section around Figure 5), state that PBL heights were assumed using gradient methods and, as such, might not be the actual PBL.
- Line 370. The rationale for the negative water vapor sounds reasonable but likely incomplete. Would not RD error be expected at the steep gradient? How about the effect of cloud heterogeneity? Furthermore, quality controlling the data by masking out negative water vapor might introduce problems from binning/smoothing. Why not use a gradient method to remove clouds before retrieval of the DIAL to avoid any smoothing issues?
- Section 4.3 Turbulence spectra
- As a suggestion, since the frequency response doesn’t have much overlap with the expected trend, perhaps plotting this data as an Allan Variance (two sample variance vs integration time) would be easier to interpret. In this case the Kolmogorov constant is +⅔.
- Of course it is possible the frequency rolloff at longer integration times may be due to instrument instability beyond 1 minute or longer. But the most compelling rationale for the DIAL accuracy is the similar lack of low frequencies seen in Doppler winds. The rest of the discussion regarding the reasons for the non-Kolmogorov atmosphere is tangential.
- Line 465 Do you mean altitude 973 m AGL and not 500 m? It is hard to tell which altitude is deviating from which. But it is clearly different at all altitudes from the Doppler wind spectrum.
- Line 662. “ A spectrum analysis of the DIAL showed good agreement with Kolmogorovs…” This conclusion is unjustified. What was shown was good agreement with the Doppler lidar frequency spectrum. And that the Kolmogorov inertial subrange rolled off at low frequencies for some reason or other (which is not really necessary to explain)
Technical corrections: minor grammar, misspellings, or strange word choices
Line 10. ‘Evaded’ perhaps 'explained' would be better?
Line 16. Shreded is misspelled, but suggest changing to ‘broken clouds’
Line 46. Deeply requested. Suggest changing to ‘often requested’
Line 140. ‘Begin of the lidar range’. Suggest ‘start of the lidar range’
Line 166. ‘Renowned DIAL equation’. Suggest changing to ’well-known DIAL equation’
Line 281. ‘Surpassing the lidar’. Suggest “passing over the lidar”
Line 336 and 633. Straight forward should be one word, straightforward
Line 370 ‘Supersaturated’. Not a good word choice in English for this condition. Is it something in the electronic gain saturated or perhaps a non-linear response of the detector (perhaps some combination of both?). Suggest ‘saturated non-linear response’
Line 379 ‘Spread is weaker’. Suggest ‘spread is reduced’
Line 621. 40 min hour. It seems the word hour is unintended
Line 636 and line 675. ‘Shreds of clouds’. Suggest ‘wisps of clouds’
Line 639 ‘Such flags have not been set in the corresponding time’, Unclear what meaning is desired here.
Line 644 ‘Proof’ is the wrong word. Use ’prove’
Line 663 ‘Interludes’: Suggest ‘portions’. Actually the spectrum doesn’t agree well beyond at time scales longer than approximately 1 minute
Citation: https://doi.org/10.5194/amt-2024-168-RC1
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