| 16 Jun 2026
A New NO2 Profile Retrieval from a Combination of Scanning Long-Path DOAS and Point Measurements Using Bayesian Inference-Based Spatiotemporal Reconstructions
Abstract. This study presents a fully Bayesian approach for retrieving reliable vertical profiles of nitrogen dioxide (NO2) from a combination of path and in-situ measurements. Instead of focusing on complete two-dimensional reconstructions, we employ a three-dimensional correlated field model (2D in space + 1D in time) as a physically consistent intermediary step to extract profile information. The Bayesian inference framework, implemented using the Numerical Information Field Theory for Python library (NIFTy), allows for a rigorous propagation of measurement uncertainties and explicitly includes spatial and temporal correlations. Data from the CINDI-III campaign, including two in-situ ICAD devices and a new DOAS system, were used as input. The new DOAS system consists of a fast scanning active and passive DOAS instrument that can be used for Long-Path as well as MAX-DOAS measurements, called HyDOAS (Hybrid Differential Optical Absorption Spectroscopy). The resulting profiles were compared against independent MAX-DOAS retrievals as a consistency check. Our results demonstrate that the Bayesian spatiotemporal framework yields physically consistent and temporally coherent NO2 profiles that agree well with established methods within their uncertainties. The approach offers a promising pathway to derive high-quality vertical information even from geometrically limited measurement configurations.
Competing interests: S.S., J.L., and D.P. are employees of Airyx GmbH, which manufactures the HyDOAS and ICAD instruments used in this study. The remaining authors declare no competing interests.
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General remarks
Henning et al. present a profile retrieval scheme based on Long-Path DOAS and in-situ measurements made during the CINDI-3 campaign at the Cabauw measurement tower. The paper matches the scope of AMT, and the method appears innovative and sound.
However, the benefit of this new profile retrieval remains limited, as the setup requires a high measurement tower. In addition, horizontal information is very limited, unless viewing geometry and location of in-situ measurements would be modified (which then would make the setup more complex and would probably require two towers / tall buildings).
The authors compared their results to MAX-DOAS profiles based on the M3 algorithm. Here, additional comparisons to other MAX-DOAS profile retrievals would be interesting.
I recommend publication on AMT after dealing with the issues listed below. In particular, the authors might consider how far the 2D retrieval is really providing added value, or directly use a 1D retrieval scheme instead. Further MAX-DOAS comparisons should be added.
(a) Partial profiles
Surface concentrations are measured by in-situ networks and are essential for air quality / health issues.
Tropospheric profiles are important for satellite validation (where AMFs require profile assumptions).
But what is the added value of partial profiles within the lower boundary layer? This should be motivated in more detail in the introduction.
The proposed retrieval scheme yields partial profiles of NO2 from ground to an altitude of ~200 m.
The upper limit is defined by the height of the highest retro-reflector, which is somehow trivial, but still an important information for the reader to be added in the instrument setup description. In addition, this should be also discussed in section 7, as this limits future applications of the proposed method to measurement sites next to tall towers or tall buildings (that allow for the installation of retro-reflectors), which are probably quite challenging to find.
(b) 2D retrieval
The authors present 2D (x, z)+1D (t) fields of NO2. However, in section 7, they point out that
"All light paths lie nearly in one vertical plane connecting the HyDOAS emitter and the KNMI tower, creating a largely symmetric setup."
causing
"the reconstructions to be more reliable in the vertical than in the horizontal direction."
The authors conclude that
"The observed smooth horizontal gradients in the posterior mean fields thus reflect prior-driven regularization rather than measurement-driven features."
With these conclusions, some earlier statements are not actually solid:
Line 36 ff:
This 2+1D formulation provides the physical consistency required to derive meaningful vertical
gradients and boundary-layer structure
Line 324 ff:
This anomaly suggests that significant horizontal variability in NO2 concentration occurred at that
time, challenging the underlying model assumptions and potentially leading to the overestimation in the upper layer. The 2D
reconstructions in Fig. 2 supports this hypothesis.
Please revise.
The information content on horizontal gradients is poor due to the measurement setup. In addition, there is no other data available which would allow for investigating the retrieved horizontal gradients, as both MAX-DOAS profile inversion and boxlayers assume horizontal homogeneity.
Thus, why should 2D fields be investigated at all? Instead, a 1D profile inversion could be set up directly, instead of starting with 2D and averaging horizontally later.
(c) MAX-DOAS comparison
The retrieved vertical profiles have been compared to profiles based on MAX-DOAS inversions with the M3 algorithm.
To my knowledge, within CINDI-3, vertical profiles are also routinely inverted by the FRM4DOAS processor, which uses two different algorithms, MMF and MAPA. Have the authors also compared their results to the FRM4DOAS products? This would provide additional information on the consistency of different MAX-DOAS inversion schemes.
The profile inversion from MAX-DOAS is in principle similar to the profile retrieval from the LP-DOAS measurements presented here.
In both cases, profile information is derived from a limited set of slant column measurements for different viewing geometries.
Thus, it would be interesting if the authors could comment on how far the Bayesian Inference-Based Spatiotemporal Reconstructions approach might also be used for MAX-DOAS profile retrievals.
(d) Sonde measurements
To my knowledge, there have been direct profile measurements with sondes performed during CINDI-3. Could these measurements be used for comparisons as well?
(e) Temporal coverage
The CINDI-3 campaign lasted several days. Please comment on the availability of profile retrievals for other days and add them to the appendix if available.
Detailed comments