| 12 Feb 2026
Improved NO2 spectral fits for TROPOMI and OMI by removing wavelengths around 430 nm
Abstract. The Fraunhofer absorption feature at 430 nm influences the retrieval of nitrogen dioxide (NO2) from measurements by satellite-based instruments such as the Tropospheric Monitoring Instrument (TROPOMI) and Ozone Monitoring Instrument (OMI). The width and depth of the feature in the measured spectrum are affected by rotational Raman scattering (RRS) throughout the atmosphere and by vibrational Raman scattering (VRS) in open water bodies. RRS, or the Ring-effect, is accounted for in the Differential Optical Absorption Spectroscopy (DOAS) retrieval of the NO2 slant column density (SCD) by means of a scalable reference spectrum, which will not fully pick up the variation of the depth of the 430 nm feature with the solar activity cycle. It is not possible to account for VRS with a scalable reference spectrum, since VRS characteristics depend on several aspects, including the viewing geometry and the material dissolved in the water, such as chlorophyll. From detailed inspection of DOAS fit residuals, the difference between the measured and modelled spectra, it is clear that the 430 nm feature disturbs the NO2 SCD retrieval.
In this paper we investigate the benefits of removing the wavelength range 428–433 nm from the DOAS retrieval. This "NO2-gap approach" reduces the SCD error and the RMS error of the fit over water bodies by 10–20 % and the fit residual for the remaining parts of the window improves. Over some land scenes, where the residual outside the 428–433 nm range looks very good, the SCD error and RMS error are reduced by 5–10 %. For other areas the fit residual does not deteriorate by the NO2-gap approach. Over ocean waters the SCD is seen to decrease by a few percent, which leads to a decrease of the stratospheric NO2 column of on average up to -2 μmol m-2 in the tropics. Over land the change in SCD may be positive or negative by a few percent, which in combination with the change in the stratospheric column leads to changes in the tropospheric NO2 column of on average ±2 μmol m-2. These changes are too small to alter the general conclusions of the routine validation of TROPOMI data. Because of the improvement of the SCD error and systematic improvements over open water it has been decided to implement the NO2-gap approach in the forthcoming processor versions of TROPOMI and OMI.
The paper “Improved NO2 spectral fits for TROPOMI and OMI by removing wavelengths around 430 nm” by van Geffen et al. describes the improvement of the DOAS retrieval algorithms for the TROPOMI and OMI instruments by disabling a part of the fit-window that reduces the impact of vibrational Raman scattering on the spectral fit quality. The study updates the TROPOMI NO2 retrieval algorithm described in van Geffen et al., 2020 and 2022. The “NO2-gap approach” is analysed for land- and water scenes and the impact on the stratospheric and tropospheric NO2 columns is discussed.
The topic of the manuscript is within the scope of AMT and it is of interest to the scientific community. It can be recommended for publication, if the authors make an effort to address the comments listed below, and improve the manuscript accordingly.
Specific comments:
Section 1
The authors explain that the remaining structures in the NO2 fit residual around 430 nm for retrievals over clear-sky dry land indicate that the accounting for RRS effects (by including a Ring spectrum in the DOAS fit) may not be fully accurate. The possible effects of the RRS (besides the effects of VRS over water) are shortly discussed in Section 3. Could these RRS effects be further investigated by applying the TROPOMI DOAS algorithm on simulated reflectances calculated with a radiative transfer model with RRS (e.g. for some specific scenarios)?
Section 2.1.1
Are there any other important algorithm improvements in the upcoming TROPOMI NO2 processor v2.9.1 (besides the “NO2-gap approach” described in this manuscript) that are of interest to the reader and could be shortly mentioned here?
Section 2.2
P4 Please include the definition of the geometric AMF used in this study.
Section 2.2.2
The statistical DOAS uncertainty is derived from the standard deviation on the slant columns in 2°x2° grid cells. Since the SCD also depends on the viewing geometry, it might be better to use geometrical corrected slant columns (GCD). The question is if there are significant differences between the statistical uncertainties based on the SCD or GCD.
Section 3
P9 Please include a global map of RD and RL
Section 3.1
A possible dependence of the RMS_430 ratio on the viewing/scattering geometry is not really discussed in the manuscript. It would be useful to include scatter plots of the RMS_430 ratio as a function of solar-, viewing or scattering angles for clear-sky pixels over the Atlantic Ocean.
Section 3.4
Fig.6 Why are the RMS_430 ratios only plotted in black (>2) and white (<2)? A color map would provide more information, e.g. about the RMS_430 ratios over different water scenes. Measurements with cloud radiance fractions > 0.5 could be marked as well.
Section 4.1
Is there also a significant impact of the NO2-gap fit on the statistical SCD uncertainty? This parameter could be included in Tables 2 and 3 as well.
Section 4.2
Fig.10 Please include a global map of the relative change in the NO2 SCD as well. The scatter plot on Fig. 11 show small changes in the SCD for most pixels but there seems to be a fairly large scatter in the SCD change as well.
Section 6
The authors discuss the unexpected large TROPOMI trop. NO2 columns over the Tibetian lakes that are likely due to unreliable DOAS retrievals and they also propose an (experimental) approach to improve the spectral fit over small areas like the lakes. I agree that such an approach to construct the missing reference spectrum might not be suitable for global retrievals and operational use. However, a case study for the Tibetian lakes would fit in the manuscript and enhance the scientific significance of the paper.