the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Airborne performance assessment of the DLR MIRO MGA3 quantum cascade laser spectrometer for fast N2O measurements
Abstract. Nitrous oxide (N2O) is the most dominant precursor of ozone-depleting substances and the third most important anthropogenic greenhouse gas, with agriculture contributing the largest share of emissions (56 %). Over the past two decades, airborne in-situ measurements have become increasingly important for studying emission and transport of N2O in the atmosphere, also driven by the development of more precise and simultaneously faster Quantum Cascade Laser (QCL)-based spectrometers. However, many QCL-based spectrometers exhibit sensitivities to environmental and flight-related parameters that can vary rapidly (≤ 1 s), such as static or cabin pressure and aircraft roll and pitch angles. The impact of changing ambient water vapor is particularly critical due to both dilution and quantum-mechanical effects. Because the variability of N2O in the lower troposphere is often very small (<1 ppb) relative to its high background (∼ 338 ppb), even minor variations in these parameters can significantly affect data quality and must be corrected. Although many instruments can resolve such small concentration changes, their typical temporal resolution of 1 Hz may limit the application of advanced measurement techniques such as eddy covariance on fast-moving aircraft. Here, we present and evaluate a new instrument setup for precise (< 0.2 ppb) and high-frequency (10 Hz) airborne in-situ N2O measurements in altitudes up to 4500 m based on a MIRO MGA3 QCL spectrometer (MIRO Analytical AG). The instrument was successfully deployed during two airborne science missions, namely onboard the unpressurized DLR Cessna during the Greenhouse Gas Monitoring (GHGMon) campaign in 2023 in the Netherlands, as well as onboard the NASA DC8 during the Satellite Investigation of the Asian Air Quality (ASIA-AQ) campaign in 2024. Specifically, we evaluate and compare different water vapor correction approaches using ASIA-AQ data sampled within the tropical boundary layer over South Korea, the Philippines, Thailand and Taiwan, which partly were characterized by specifically high ambient humidity (up to 30000 ppm H2O). The water vapor correction methods include an empirical approach that relies on both native and corrected MIRO in-flight water vapor measurements, as well as an approach by an updated version of the MIRO specific fitting software. Comparison with N2O measurements from a well-established instrument onboard the NASA DC8 shows agreement within combined measurement uncertainties for all tested water vapor correction approaches, albeit special caution is needed for humidities larger than 15000 ppm. The new water vapor corrected data shows a 42 % better precision than with original default settings of the instrument. We further show that the instrument setup is insensitive to flight parameter changes such as roll and pitch angle of the aircraft, and allows for stable measurements even under challenging conditions such as in the turbulent boundary layer. This instrument setup enables improved characterization of N2O emissions and sources from the agricultural sector which is, in particular, relevant for tropical regions with strong agricultural activity and high humidity, where observational data remain scarce.
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Status: final response (author comments only)
- RC1: 'Comment on egusphere-2026-1686', Anonymous Referee #1, 19 May 2026
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RC2: 'Comment on egusphere-2026-1686', Anonymous Referee #2, 25 Jun 2026
This paper describes a quite comprehensive assessment of the performance of a new laser-based system for airborne measurements of N2O. The paper is well-written, and fits well within the scope of AMT. However, a few issues listed below should be addressed before the paper can be recommended for publication.
General comments:
I suggest early in the description of the wet-dry correction problem to cite the early papers on the approach of using simultaneously measured water vapor and long-lived gases for accurate correction of wet to dry air mole fractions (Chen et al., 2010; Rella et al., 2013):
Chen, H., Winderlich, J., Gerbig, C., Hoefer, A., Rella, C. W., Crosson, E. R., Van Pelt, A. D., Steinbach, J., Kolle, O., Beck, V., Daube, B. C., Gottlieb, E. W., Chow, V. Y., Santoni, G. W., and Wofsy, S. C.: High-accuracy continuous airborne measurements of greenhouse gases (CO2 and CH4) using the cavity ring-down spectroscopy (CRDS) technique, Atmos. Meas. Tech., 3, 375–386, doi:10.5194/amt-3-375-2010, 2010.
Rella, C. W., Chen, H., Andrews, A. E., Filges, A., Gerbig, C., Hatakka, J., Karion, A., Miles, N. L., Richardson, S. J., Steinbacher, M., Sweeney, C., Wastine, B., and Zellweger, C.: High accuracy measurements of dry mole fractions of carbon dioxide and methane in humid air, Atmos. Meas. Tech., 6, 837–860, doi:10.5194/amt-6-837-2013, 2013.
It might be easier for the reader if the setup of the experiments (section 3.1) are presented first, and then the different approaches (lines 250 – 280).
On the calibration of the H2O measurements: I was a bit confused about the term “one-point calibration”, and then see that dry air cylinders where used for this. I would not consider this a calibration, but rather a zero measurement. A one-point calibration would be providing a known amount of water vapor with (single) value to the instrument to determine the sensitivity. In addition, the influence of self-broadening of H2O on the linearity of the H2O Measurement should be discussed (see e.g. Chen et al., 2010, Rella et al, 2013).
Given the overall length of the manuscript, I suggest removing the section from Line 550 to Line 575 on interpretation of N2O measurements in the context of simultaneous NH3 measurements. This would be better included in the mentioned upcoming study on source attribution.
Specific comments
L28: “agricultural sector which is, in particular, relevant” > “agricultural sector, which is particularly relevant”
L184: This is a bit unclear. If the calibration gases are dry and should not contain any water, why is there a non-zero “known actual value”, and were does this come from?
L191: add a comma after “calibration cycles”, unless you meant to say that you excluded values less than 1 ppb
Eq. 3-6: it would be helpful to use different symbols for N2O mole fraction and water vapor
L236 “an already” -> “and already”
Eq. 7: please clarify if C_H2O is the dry or wet air mole fraction for water vapor.
L 286: “is provided only” may be change to “is provided with only”, not clear otherwise
L342: note the same equation was also used earlier in Chen et al., 2010. I suggest citing this (as well or instead).
L359-360: is it the mean of all curves, the curve using the mean of the coefficients, or the result of a fit to all data? The mean of the many curves would behave quite different given the different ranges of H2O in each curve.
L366: “correlation … diverge” please reformulate. The values diverge, correlation is not discussed for each line.
L369: “unless … not” I guess you need to remove the “not”
L380: “to assure, that all” remove “,”
L401: is the precision error aggregated to the 0.01% H2O bins? This should be done, given there are many independent measurements at 5Hz within each of the bins.
L402: “and the uncertainty of our own cylinders” why is this needed, given they were cross-calibrated against the cylinders of DACOM?
L404-419: is the uncertainty meant as a one-sigma or two-sigma uncertainty? This is important when interpreting the significance of the differences.
L413: “water dependencies found in the pressure regulator” can you elaborate on this? Does it mean the pressure depends on the amount of water vapor?
L420-424: Does any of the three options mentioned potentially indicate that the uncertainty estimate is wrong for one or both instruments?
L425: Was a cross-correlation performed to check for misalignment in sample time, to rule out errors due to different airmasses being sampled?
L441: “lowest determined in-flight precision” does this mean the worst or the best precision?
L506: Is it possible that the vibrations or accelerations on the Cessna compared to the much heavier DC8 are causing the larger variability, rather than the lack of cabin pressure control? This should at least be discussed in this context.
Caption Fig 10: „The change in water vapor mole fraction while entering does not show any effect on the water vapor corrected v1-β-off-DLH data“ This statement should be formulated somewhat weaker. This cannot be concluded from ambient air measurements as it cannot be ruled out that compensating effects occur.
Eq. A1: there is something wrong here: the right side has indices i,j while the left side does not. So what is α? Note in Gordon et al. (2022) the left side does have the indices.
Citation: https://doi.org/10.5194/egusphere-2026-1686-RC2
Data sets
Airborne and Satellite Investigation of Asian Air Quality Crawford et al. https://doi.org/10.5067/SUBORBITAL/ASIA-AQ/DATA001
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Knez et al. provide a valuable assessment of the airborne performance of a new N2O analyzer. The performance of the MIRO MGA3, measuring N2O, CO, CH4, and H2O, is characterized during two flight campaigns. Given the sampling locations in humid environments, the authors examine the role of water vapor on the reported N2O values and evaluate different correction strategies. The manuscript is well-written and likely to be of interest to the greenhouse gas measurement community. I have a few comments that I believe should be addressed prior to publication.