the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 11 Nov 2024
Global retrieval of TROPOMI tropospheric HCHO and NO2 columns with improved consistency based on updated Peking University OMI NO2 algorithm
Abstract. The TROPOspheric Monitoring Instrument (TROPOMI), onboard the Sentinel-5 Precursor (S5P) satellite launched in October 2017, is dedicated to monitoring the atmospheric composition associated with air quality and climate change. This paper presents the global retrieval of TROPOMI tropospheric formaldehyde (HCHO) and nitrogen dioxide (NO2) vertical columns using an updated version of the Peking University OMI NO2 (POMINO) algorithm, which focuses on improving the calculation of air mass factors (AMFs). The algorithm features explicit corrections for the surface reflectance anisotropy and aerosol optical effects, and uses daily high-resolution (0.25°×0.25°) a priori HCHO and NO2 profiles from the Global Earth Observing System Composition Forecast (GEOS-CF) dataset. For cloud correction, a consistent approach is used for both HCHO and NO2 retrievals, where (1) the cloud fraction is re-calculated at 440 nm using the same ancillary parameters as those used in the NO2 AMF calculation, and (2) the cloud top pressure is taken from the operational FRESCO-S cloud product.
The comparison between POMINO and reprocessed (RPRO) operational products in April, July, October 2021 and January 2022 exhibits high spatial agreement, but RPRO tropospheric HCHO and NO2 columns are lower by 10 % to 20 % over polluted regions. Sensitivity tests with POMINO show that the HCHO retrieval differences are mainly caused by different aerosol correction methods (implicit versus explicit), prior information of vertical profile shapes and background corrections; while the NO2 retrieval discrepancies result from different aerosol corrections, surface reflectances and a priori vertical profile shapes as well as their non-linear interactions. With explicit aerosol corrections, the HCHO structural uncertainty due to the cloud correction using different cloud parameters is within ± 20 %, mainly caused by cloud height differences. Validation against ground-based measurements from global Multi-Axis Differential Optical Absorption Spectroscopy (MAX-DOAS) observations and the Pandonia Global Network (PGN) shows that in April, July, October 2021 and January 2022, POMINO retrievals present a comparable day-to-day correlation but a reduced bias compared to the RPRO products (HCHO: R = 0.62, NMB = −30.8 % versus R = 0.68, NMB = −35.0 %; NO2: R = 0.84, NMB = −9.5 % versus R = 0.85, NMB = −19.4 %). An improved agreement of HCHO/NO2 ratio (FNR) with PGN measurements based on POMINO retrievals is also found (R = 0.83, NMB = −18.4 % versus R = 0.82, NMB = −24.1 %). Our POMINO retrieval provides a useful source of information particularly for studies combining HCHO and NO2.
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RC1: 'Comment on amt-2024-182', Anonymous Referee #1, 11 Dec 2024
The paper “Global retrieval of TROPOMI tropospheric HCHO and NO2 columns with improved consistency based on updated Peking University OMI NO2 algorithm”, by Zhang and co-authors, presents an updated version of the POMINO algorithm for HCHO and NO2 and its structural uncertainty in the AMF calculation. The paper is well structured. The topic fits in the scope of AMT.
One concern is the application of aerosol correction on FRESCO parameters. The FRESCO CF has been recalculated and corrected using aerosol information in UV and VIS, but the original O2-A band may be less affected by aerosol than UV/VIS, possibly introducing an overcorrecting issue. Such overcorrecting can be amplified by using the un-corrected CP.
Table 1 is a bit heavy, and I recommend to remove redundant information, such as “daily” in a priori profiles. One typo in POMINO HCHO CF is 340 instead of 440.
What is the meaning of “VCD from QA4ECV” for Mohali in Table2?
Please enlarge the font size of xaxis label in Figures 10 and 11.
Citation: https://doi.org/10.5194/amt-2024-182-RC1 -
RC2: 'Comment on amt-2024-182', Anonymous Referee #2, 13 Dec 2024
The paper presents TROPOMI formaldehyde and nitrogen dioxide retrievals. This new algorithm uses the operational slant columns distributed by ESA and focusses on improving the “operational” Air Mass Factors (AMF). It is built on the Peking University OMI nitrogen dioxide algorithm (POMINO). In comparison with the “operational” retrievals the products presented here use improved AMFs. The most important differences is the explicit consideration of aerosols in radiative transfer calculations, the derivation of cloud fractions accounting for the presence of aerosols, the consideration of anisotropic surface reflectance and the use of a higher resolution chemical forecast as source of a priori vertical profiles.
These products show good performance, slightly better than TROPOMI operational products, providing important information regarding the influence of aerosols in AMF uncertainties. Given the clarity of the manuscript and the science these new products may enable the publication of this work to be well justified. The manuscript already has good quality. Hopefully the comments provided below will marginally help to improve the clarity and soundness of the paper.
Line 63: When listing past sensors with NO2 and HCHO capabilities would the authors consider adding TEMPO and Suomi-NPP/JPSS satellites that carry the OMPS-NM instrument?
Line 78: Some NO2 strat/trop separation schemes use AMFs during the process. See for example Bucsela et al. (2013) and Geddes et al. (2018)
Line 79: Please specify these studies relate to TROPOMI
Line 122: Slightly different spatial resolution for TROPOMI NO2 is reported in van Geffen et al. 2022: 7.2 km x 3.6 km and 5.6 x 3.6 km. Would it be possible to clarify the situation?
Line 165: Does POMINO directly use MODS MYD04_L2 or is it only through GEOS-CF assimilation? Figure S1 seems to indicate the second situation.
Line 177: Is it MCD43C2 061 or 006, there is conflict with figure S1.
Line 202: What is the meaning of structural uncertainty? The experiments described here evaluate the sensitivity with respect to different parameters sources but don’t consider the uncertainty on those sources or how they relate to each other.
Line 236: Pandonia HCHO retrievals vary in quality drastrically depending on station. They can also degrade their accuracy over time. Have the authors made any effort to quality control Pandora datasets? If so, it would be very helpful to include a description of that work. If not, this situation with Pandora HCHO retrievals should be discussed and acknowledged and if possible mitigated.
Line 233: Grammatical suggestion in table caption, remove “the” before “alphabetical”.
Line 249: How do the comparisons between satellite and MAX-DOAS/PNG change with the selection of time window and integration radius? It seems that all MAX-DOAS/PNG are in pollution hotspots. It would be interesting to see how these comparisons work in background areas particularly given the smaller influence of aerosols in those. Has this been explored during the comparison exercise?
Line 328: There are studies evaluating AMF and cloud pressure uncertainties. This is not the first time AMF uncertainties associated with cloud pressure are evaluated.
Line 380: It may be worth adding the work by Latsch et al., 2022 when discussing the characteristics of the different cloud products.
Section 4.1.2: This is an interesting analysis of the impact associated with different cloud top pressures. However, both products get cloud information from very different spectral ranges (440nm vs. 760nm) and therefore they must be different. Given the wavelengths used in HCHO and NO2 retrievals ROCINN cloud top pressure shoud be used here with extreme caution. It would be good if the authors could comment on these considerations.
Section 4.3: Could the authors discuss the uncertainties associated with MODIS BRDF products and how the atmospheric correction in the BRDF retrieval accounts for aerosols or not? This could be relevant given the complicated nature of the BRDF atmospheric correction. What happens if the BRDF does not account for aerosols properly therefore has a bias that then is not considered in the POMINO aerosol correction.
Section 4.4: Does the collocation of GEOS-CF, TM5 and MAX-DOAS observations follow the same methodology described above for TROPOMI data? Please clarify.
Section 5: The error analysis in Chong et al., 2024 (for BrO) and Ayazpour et al. 2024 (HCHO preprint) is done at an individual pixel level and includes information about the BRDF uncertainties relevant to this analysis. It may be worth commenting on their results.
Table S1: The reference spectrum I0 in the case of NO2 retrievals is a solar irradiance recorded by TROPOMI (van Geffet et al., 2020)
Figure S1: MODIS MCD43C2.061 product has a resolution of 0.05 x 0.05
References:
Zolal Ayazpour, Gonzalo González Abad, Caroline R. Nowlan, et al. Aura Ozone Monitoring Instrument (OMI) Collection 4 Formaldehyde Product. ESS Open Archive . June 10, 2024. DOI: 10.22541/essoar.171804891.19520982/v1
Bucsela, E. J., Krotkov, N. A., Celarier, E. A., Lamsal, L. N., Swartz, W. H., Bhartia, P. K., Boersma, K. F., Veefkind, J. P., Gleason, J. F., and Pickering, K. E.: A new stratospheric and tropospheric NO2 retrieval algorithm for nadir-viewing satellite instruments: applications to OMI, Atmos. Meas. Tech., 6, 2607–2626, https://doi.org/10.5194/amt-6-2607-2013, 2013.
Chong, H., González Abad, G., Nowlan, C. R., Chan Miller, C., Saiz-Lopez, A., Fernandez, R. P., Kwon, H.-A., Ayazpour, Z., Wang, H., Souri, A. H., Liu, X., Chance, K., O'Sullivan, E., Kim, J., Koo, J.-H., Simpson, W. R., Hendrick, F., Querel, R., Jaross, G., Seftor, C., and Suleiman, R. M.: Global retrieval of stratospheric and tropospheric BrO columns from the Ozone Mapping and Profiler Suite Nadir Mapper (OMPS-NM) on board the Suomi-NPP satellite, Atmos. Meas. Tech., 17, 2873–2916, https://doi.org/10.5194/amt-17-2873-2024, 2024.
Geddes, J. A., Martin, R. V., Bucsela, E. J., McLinden, C. A., and Cunningham, D. J. M.: Stratosphere–troposphere separation of nitrogen dioxide columns from the TEMPO geostationary satellite instrument, Atmos. Meas. Tech., 11, 6271–6287, https://doi.org/10.5194/amt-11-6271-2018, 2018.
Latsch, M., Richter, A., Eskes, H., Sneep, M., Wang, P., Veefkind, P., Lutz, R., Loyola, D., Argyrouli, A., Valks, P., Wagner, T., Sihler, H., van Roozendael, M., Theys, N., Yu, H., Siddans, R., and Burrows, J. P.: Intercomparison of Sentinel-5P TROPOMI cloud products for tropospheric trace gas retrievals, Atmos. Meas. Tech., 15, 6257–6283, https://doi.org/10.5194/amt-15-6257-2022, 2022.
van Geffen, J., Boersma, K. F., Eskes, H., Sneep, M., ter Linden, M., Zara, M., and Veefkind, J. P.: S5P TROPOMI NO2 slant column retrieval: method, stability, uncertainties and comparisons with OMI, Atmos. Meas. Tech., 13, 1315–1335, https://doi.org/10.5194/amt-13-1315-2020, 2020.
van Geffen, J., Eskes, H., Compernolle, S., Pinardi, G., Verhoelst, T., Lambert, J.-C., Sneep, M., ter Linden, M., Ludewig, A., Boersma, K. F., and Veefkind, J. P.: Sentinel-5P TROPOMI NO2 retrieval: impact of version v2.2 improvements and comparisons with OMI and ground-based data, Atmos. Meas. Tech., 15, 2037–2060, https://doi.org/10.5194/amt-15-2037-2022, 2022.
Citation: https://doi.org/10.5194/amt-2024-182-RC2 -
RC3: 'Comment on amt-2024-182', Anonymous Referee #3, 13 Dec 2024
This paper presents global tropospheric HCHO and NO2 vertical column retrievals from TROPOMI, utilizing the POMINO algorithm developed by Peking University, which focuses on improvements in air mass factor (AMF) calculations. The study includes comprehensive sensitivity tests on AMF input parameters. The research topic fits well in the scope of AMT and is well-structured, providing valuable scientific insights.
Below are my comments for improving the current manuscript. I recommend its publication after the authors address the following points.
1. While I understand the reason for using the TROPOMI RPRO product for the study period, 'general readers' may not be familiar with the differences between the OFFL and RPRO v2.4.1 products (e.g. the application of the reprocessed Level 1 version) and the specific improvements reflected in RPRO product. Including a brief explanation of RPRO v2.4.1 and its distinctions from the OFFL product would enhance the clarity of the manuscript.
2. Although the flow chart of global POMINO-TROPOMI algorithm is included in the supplementary material (Fig. S1), it would be helpful to mention in a sentence in Section 2.1 or 2.2 that the POMINO-TROPOMI algorithm performs only sensitivity tests on AMF, using tropospheric slant columns directly from the RPRO products. It should be clarified that slant column retrieval and stratosphere-troposphere separation are not part of the POMINO-TROPOMI algorithm in this study.
3. In this study, you used cloud top pressure from FRESCO-S and recalculated the cloud fraction at 340 nm (for HCHO) and 440 nm (for NO2) by simulating the TOA reflectance using auxiliary input parameters. As FRESCO cloud products does not explicitly correct for the presence of aerosols but retrieve parameters based on the O2-O2 absorption, aerosols are implicitly included in the FRESCO. This raises a concern that the POMINO-TROPOMI algorithm may 'overcorrect' for aerosols here. I recommend providing a more detailed explanation of the potential overcorrection effects on aerosols and their impact on the derived results.
4. In sect. 4.1.1, for both HCHO and NO2, the differences of clear-sky AMF and total AMF across cloud top pressure ranges (Fig. 3) using all global pixels in both summer (July 2021) and winter (January 2022) shows different patterns (negative and positive) based on the 700 hPa cloud top pressure threshold. These patterns include the combined effects of seasonal and global variations. Could you describe if the cloud correction pattern for cloud top pressure differs depending on season (e.g. summer vs winter) and region (e.g. polluted vs clean)?
5. In Sect. 4.4, the descriptions of the chemistry transport models GEOS-CF and TM5-MP are insufficient. Please provide more detailed information on the specifications of the CTMs, such as vertical resolution, tropospheric chemistry, emissions, and meteorological fields and so on. Additionally, please include relevant references.
6. In Sect. 5, please provide a summary table for estimates of the contributions to the AMF uncertainties from individual error sources for HCHO and NO2 retrievals.
Table 1: there is a typo in POMINO HCHO CF: re-calculated at 340 nm.
Figure 3: Please add the unit for cloud top pressure [hPa].
Citation: https://doi.org/10.5194/amt-2024-182-RC3
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