the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 27 May 2024
Tropospheric NO2 retrieval algorithm for geostationary satellite instruments: applications to GEMS
Abstract. In this study, we develop an advanced retrieval algorithm for tropospheric nitrogen dioxide (NO2) from the geostationary satellite instruments and apply it to Geostationary Environment Monitoring Spectrometer (GEMS) observations. Overall, the algorithm follows previous heritage for the polar orbiting satellites GOME-2 and TROPOMI, but several improvements are implemented to account for specific features of geostationary satellites.
The DLR GEMS NO2 retrieval employs an extended fitting window compared to the current fitting window used in GEMS operational v2.0 NO2 retrieval, which results in improved spectral fit quality and lower uncertainties. For the stratosphere-troposphere separation in GEMS measurements, two methods are developed and evaluated: (1) STRatospheric Estimation Algorithm from Mainz (STREAM) as used in the DLR TROPOMI NO2 retrieval and adapted to GEMS, and (2) estimation of stratospheric NO2 columns from the Copernicus Atmosphere Monitoring Service (CAMS) forecast Cy48R1 model data, which introduce full stratospheric chemistry as it will be used in the operational Sentinel-4 NO2 retrieval. While STREAM provides hourly estimates of stratospheric NO2, it has limitations in describing small-scale variations and exhibits systematic biases near the boundary of the field of view. In this respect, the use of estimated stratospheric NO2 columns from the CAMS forecast model profile demonstrates better applicability by describing not only diurnal variation but also small-scale variations.
For the improved air mass factor (AMF) calculation, sensitivity tests are performed using different input data. In our algorithm, cloud fractions retrieved from the Optical Cloud Recognition Algorithm (OCRA) adapted to GEMS level 1 data are applied instead of GEMS v2.0 cloud fraction. OCRA is used operationally in TROPOMI and Sentinel-4. Compared to GEMS level 2 cloud fraction which is typically set to around 0.1 for clear-sky scenes, OCRA sets cloud fractions close or at 0. The OCRA-based cloud corrections result in increased tropospheric AMFs and decreased tropospheric NO2 vertical columns, leading to better agreement with results from existing TROPOMI observations. The effects of surface albedo on GEMS tropospheric NO2 retrievals are assessed by comparing the GEMS v2.0 background surface reflectance (BSR) and TROPOMI Lambertian-equivalent reflectivity (LER) climatology v2.0 product. The differences between the two surface albedo products and their impact on tropospheric AMF are particularly pronounced over snow/ice scenes during winter. A priori NO2 profiles from the CAMS forecast model, applied in the DLR GEMS algorithm, effectively capture variations in NO2 concentrations throughout the day with high spatial resolution and advanced chemical mechanism, which demonstrates its suitability for geostationary satellite measurements.
The retrieved DLR GEMS tropospheric NO2 columns show good capability to capture hotspot signals at the scale of city clusters and describe spatial gradients from city centers to surrounding areas. Diurnal variations of tropospheric NO2 columns over Asia are well described through hourly sampling of GEMS. Evaluation of DLR GEMS tropospheric NO2 columns against TROPOMI v2.4 and GEMS v2.0 operational products show overall good agreement. The uncertainty of DLR GEMS tropospheric NO2 vertical columns varies based on observation scenarios. In regions with low pollution levels such as open ocean and remote rural areas, retrieval uncertainties typically range from 10 % to 30 %, primarily due to uncertainties in slant columns. For heavily polluted regions, uncertainties in tropospheric NO2 columns are mainly driven by errors in tropospheric AMF calculations. Notably, the total uncertainty in GEMS tropospheric NO2 columns is most significant in winter, particularly over heavily polluted regions with low-level clouds below or near the NO2 peak.
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The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
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Preprint
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The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
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Journal article(s) based on this preprint
Interactive discussion
Status: closed
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RC1: 'Comment on egusphere-2024-1137', Anonymous Referee #1, 17 Jun 2024
The manuscript by Seo et al. describes the application of the DOAS algorithm to the GEMS visible spectra to retrieve tropospheric NO2 vertical columns. Compared to the current (v2.0) operational NO2 retrieval from GEMS, The DOAS implementation by Seo et al. included significant improvements in slant NO2 column estimations and their conversions into vertical columns. This manuscript is well-written, and I recommend its publication. I have a few comments that the authors may address to help a reader better appreciate the algorithm implementation.
1) The authors used a broader spectral range (425 - 480 nm) to improve the slant column estimation, particularly in reducing its noise level compared to that from the narrower range (432 - 450 nm) used by the operational algorithm. However, the manuscript contains little discussion about the slant column biases. How do the biases from the broader window compare with those of the narrower window of the operational GEMS NO2? Are the biases higher, lower, or similar in magnitudes and north-south behavior?
2) The broader spectral range (425 - 480 nm) includes possible soil signatures (Richter et al., 2011). Does slant column fitting include a soil signature term over areas where it may be present?
3) Eq. (1) (page 4) contains the offset term (offset(λ), a linear function of wavelength λ). Please describe the impact on the slant column estimation. Does its inclusion reduce the noise level of the slant column or change (consistently increase or decrease) the slant columns to reduce biases? In short, please justify this offset term.
4) On Page 5, line 141, a pseudo-cross-section for polarization correction is added to the slant column fitting. Please describe its impact on the slant column.
5) On Page 10, line 267, a low-order bivariate polynomial is mentioned. Figure 5 suggests that it is only a single variable (i.e., latitude) polynomial. Looks like the UTC is a label only. Since the polynomial is time-dependent, the polynomial should indeed be bivariate, with the second variable being the longitude. However, the time difference is probably sufficiently small that the longitudinal dependence may be neglected.
6) The authors used the OCRA cloud fraction in AMF calculations, avoiding the cloud fractions from the GEMS standard product, which likely biased high due to high (likely ~8% or higher) biases in GEMS sun-normalized radiances. However, the authors selected GEMS (v2.0) cloud pressure and GEMS (v2.0) background surface reflectance (BSR) for AMF calculations. Please comment on the possible biases in these products due to radiance biases.
Citation: https://doi.org/10.5194/egusphere-2024-1137-RC1 -
AC1: 'Reply on RC1', Sora Seo, 07 Aug 2024
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2024/egusphere-2024-1137/egusphere-2024-1137-AC1-supplement.pdf
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AC1: 'Reply on RC1', Sora Seo, 07 Aug 2024
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RC2: 'Comment on egusphere-2024-1137', Anonymous Referee #2, 24 Jun 2024
The authors describe a new algorithm to retrieve NO2 from the GEMS satellite instrument. The algorithm is based on what has been used for polar-orbiting satellites and has been modified for application to geostationary satellites. The authors evaluate the retrieved NO2 columns using TROPOMI and the operational GEMS retrievals and discuss the uncertainties in the retrievals.
This is a high-quality manuscript. It includes a complete description of the new algorithm and a thorough analysis of its strengths and weaknesses. It is well-written and makes good use of figures. I point out below a few points that could be better addressed.
1. The reader would benefit from a brief description of the operational GEMS algorithms near the beginning of the paper. It would help to include a table describing the major similarities and differences between the new and the operational GEMS algorithm.
2. The uncertainty analysis (Section 3.4) could be improved by discussing uncertainties specific to retrievals from geostationary satellites. For example, how do the retrieval uncertainties vary with the time of day, or how different are they near the edge of the field of view compared to the center?
3. The effect of aerosols on the NO2 retrievals is important for Asia, and in most retrievals, it is considered implicitly in the cloud parameters retrieved using O2-O2 absorption. It is unclear whether the OCRA algorithm used in this work does the same. Does it instead correct for the presence of aerosols and could that partly explain why it retrieves lower cloud fractions (figures 9 and 10) compared to the GEMS retrieval?
4. Equation 6: Is there an error correlation between albedo and cloud fraction (Boersma et al. 2018; doi: 10.5194/amt-11-6651-2018)?
5. Line 547-9: NO2 is also photolyzed by visible radiation, not just UV. Another factor for the low noontime values of NO2 is oxidation by OH.
Citation: https://doi.org/10.5194/egusphere-2024-1137-RC2 - AC2: '<strong>Publisher’s note: the content of this comment was removed on 12 August 2024 and is replaced by AC3. </strong>', Sora Seo, 07 Aug 2024
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AC3: 'Reply on RC2', Sora Seo, 09 Aug 2024
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2024/egusphere-2024-1137/egusphere-2024-1137-AC3-supplement.pdf
Interactive discussion
Status: closed
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RC1: 'Comment on egusphere-2024-1137', Anonymous Referee #1, 17 Jun 2024
The manuscript by Seo et al. describes the application of the DOAS algorithm to the GEMS visible spectra to retrieve tropospheric NO2 vertical columns. Compared to the current (v2.0) operational NO2 retrieval from GEMS, The DOAS implementation by Seo et al. included significant improvements in slant NO2 column estimations and their conversions into vertical columns. This manuscript is well-written, and I recommend its publication. I have a few comments that the authors may address to help a reader better appreciate the algorithm implementation.
1) The authors used a broader spectral range (425 - 480 nm) to improve the slant column estimation, particularly in reducing its noise level compared to that from the narrower range (432 - 450 nm) used by the operational algorithm. However, the manuscript contains little discussion about the slant column biases. How do the biases from the broader window compare with those of the narrower window of the operational GEMS NO2? Are the biases higher, lower, or similar in magnitudes and north-south behavior?
2) The broader spectral range (425 - 480 nm) includes possible soil signatures (Richter et al., 2011). Does slant column fitting include a soil signature term over areas where it may be present?
3) Eq. (1) (page 4) contains the offset term (offset(λ), a linear function of wavelength λ). Please describe the impact on the slant column estimation. Does its inclusion reduce the noise level of the slant column or change (consistently increase or decrease) the slant columns to reduce biases? In short, please justify this offset term.
4) On Page 5, line 141, a pseudo-cross-section for polarization correction is added to the slant column fitting. Please describe its impact on the slant column.
5) On Page 10, line 267, a low-order bivariate polynomial is mentioned. Figure 5 suggests that it is only a single variable (i.e., latitude) polynomial. Looks like the UTC is a label only. Since the polynomial is time-dependent, the polynomial should indeed be bivariate, with the second variable being the longitude. However, the time difference is probably sufficiently small that the longitudinal dependence may be neglected.
6) The authors used the OCRA cloud fraction in AMF calculations, avoiding the cloud fractions from the GEMS standard product, which likely biased high due to high (likely ~8% or higher) biases in GEMS sun-normalized radiances. However, the authors selected GEMS (v2.0) cloud pressure and GEMS (v2.0) background surface reflectance (BSR) for AMF calculations. Please comment on the possible biases in these products due to radiance biases.
Citation: https://doi.org/10.5194/egusphere-2024-1137-RC1 -
AC1: 'Reply on RC1', Sora Seo, 07 Aug 2024
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2024/egusphere-2024-1137/egusphere-2024-1137-AC1-supplement.pdf
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AC1: 'Reply on RC1', Sora Seo, 07 Aug 2024
-
RC2: 'Comment on egusphere-2024-1137', Anonymous Referee #2, 24 Jun 2024
The authors describe a new algorithm to retrieve NO2 from the GEMS satellite instrument. The algorithm is based on what has been used for polar-orbiting satellites and has been modified for application to geostationary satellites. The authors evaluate the retrieved NO2 columns using TROPOMI and the operational GEMS retrievals and discuss the uncertainties in the retrievals.
This is a high-quality manuscript. It includes a complete description of the new algorithm and a thorough analysis of its strengths and weaknesses. It is well-written and makes good use of figures. I point out below a few points that could be better addressed.
1. The reader would benefit from a brief description of the operational GEMS algorithms near the beginning of the paper. It would help to include a table describing the major similarities and differences between the new and the operational GEMS algorithm.
2. The uncertainty analysis (Section 3.4) could be improved by discussing uncertainties specific to retrievals from geostationary satellites. For example, how do the retrieval uncertainties vary with the time of day, or how different are they near the edge of the field of view compared to the center?
3. The effect of aerosols on the NO2 retrievals is important for Asia, and in most retrievals, it is considered implicitly in the cloud parameters retrieved using O2-O2 absorption. It is unclear whether the OCRA algorithm used in this work does the same. Does it instead correct for the presence of aerosols and could that partly explain why it retrieves lower cloud fractions (figures 9 and 10) compared to the GEMS retrieval?
4. Equation 6: Is there an error correlation between albedo and cloud fraction (Boersma et al. 2018; doi: 10.5194/amt-11-6651-2018)?
5. Line 547-9: NO2 is also photolyzed by visible radiation, not just UV. Another factor for the low noontime values of NO2 is oxidation by OH.
Citation: https://doi.org/10.5194/egusphere-2024-1137-RC2 - AC2: '<strong>Publisher’s note: the content of this comment was removed on 12 August 2024 and is replaced by AC3. </strong>', Sora Seo, 07 Aug 2024
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AC3: 'Reply on RC2', Sora Seo, 09 Aug 2024
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2024/egusphere-2024-1137/egusphere-2024-1137-AC3-supplement.pdf
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Pieter Valks
Ronny Lutz
Klaus-Peter Heue
Pascal Hedelt
Diego Loyola
Hanlim Lee
Jhoon Kim
The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
- Preprint
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