the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 15 Mar 2024
First evaluation of the GEMS glyoxal products against TROPOMI and ground-based measurements
Abstract. The Geostationary Environment Monitoring Spectrometer (GEMS) aboard the GEO-KOMPSAT-2B satellite is the first geostationary satellite launched to monitor the environment. GEMS conducts hourly measurements during the day over East and Southeast Asia. This work presents glyoxal (CHOCHO) vertical column densities (VCDs) retrieved from GEMS, with optimal settings for glyoxal retrieval based on sensitivity tests involving reference spectrum sampling and fitting window selection. We evaluated GEMS glyoxal VCDs by comparing them to TROPOMI and MAX-DOAS ground-based observations. On average, GEMS and TROPOMI VCDs show a spatial correlation coefficient of 0.63, increasing to 0.87 for Northeast Asia. While GEMS and TROPOMI demonstrate similar monthly variations in the Indochinese peninsula regions (R > 0.67), variations differ in other areas. Specifically, GEMS VCDs are lower in the summer and higher in the winter than TROPOMI VCDs in Northeast Asia, potentially due to a polluted reference spectrum and high NO2 concentrations. This trend also occurs in comparing monthly variations between GEMS and MAX-DOAS VCDs. When averaged hourly, GEMS and MAX-DOAS VCDs exhibit similar diurnal variations, especially at stations in Japan (Chiba, Kasuga, and Fukue).
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Status: open (until 06 May 2024)
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RC1: 'Comment on egusphere-2024-589', Anonymous Referee #1, 07 Apr 2024
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Summary
Ha et al. introduce the GEMS glyoxal retrieval algorithm and show comparisons against similar observations made from TROPOMI, and a set of MAX-DOAS instruments in the GEMS field of regard. In general they find reasonable agreement with both, however the TROPOMI comparison revealed a bias in GEMS glyoxal at high NO2 concentrations. The correction required in the spectral-fitting algorithm is not currently implemented in the GEMS algorithm.
The paper is well written and will be a useful reference for other researchers who plan to use the product. I recommend publication after the following comments are addressed.
Specific Comments
L80 - Would the spectral fitting not be stable for the retrieval to be performed at native spatial resolution? Aggregation generally causes problems, the most important probably being increased cloud contamination. In general I would have thought the main reason for aggregation is computational expediency (which is fine). I would just have thought that you would get at least equivalent precision/accuracy by aggregating the retrieved glyoxal at native resolution compared to aggregating at L1.
L85 - I know this will be in basically every NO2, HCHO and CHOCHO retrieval paper, but you probably should add equation 6 from Kwon to make the paper self contained.
L90 - The spectra region used for the radiance reference may contain significant liquid water absorption, which has caused significant negative biases in retrieved glyoxal, as demonstrated from the previous instruments (GOME-2, OMI) cited in the introduction. If these are used as a radiance reference it may artificially increase glyoxal concentrations over land. Perhaps some of this may be mitigated by the updated Mason and Fry cross section. How does this compare to the previous Pope and Fry liquid water cross section used by the previous studies?
L97 - The retrieval optimization is mentioned in the intro and conclusion, but not really presented in the text. I think it would be worth adding a figure showing the fit window optimization, and provide more details of the analysis.
L117 - Is the OMI LER product the most appropriate surface reflectance database for GEMS? Given the different instrument viewing geometries, the equivalent GEMS LER may be significantly different due to BRDF effects, and the OMI LER database spatial resolution is coarse compared to the GEMS pixel size. Are there plans to update this in the future?
L174 - Is this paper describing “GEMS glyoxal V2.0”? It probably should be mentioned explicitly somewhere earlier, as it is helpful for users of the product.
L194 - Could some of the influence from the polluted background on the reference be eliminated by expanding the time averaging window of the reference radiance, and screening regions that are typically impacted by pollutant outflow? The generally higher retrieved columns in Fig. 7(a) are also what I would expect from the liquid water interference discussed earlier, as the larger reference sector is incorporating more of the open ocean water scenes to the east.
L222 - It may be helpful to mention the magnitude of MAX-DOAS instrument-to-instrument biases to aid the interpretation of Fig. 8. Does CERES have some sort of side-by-side intercomparison?
Corrections
L36 - “visible wavelength” to “visible wavelengths”
Table 1 - there is no number for the spatial resolution at Seoul
Table 1 - it may be worth adding a footnote in to say what the calibration window is (i assume its for wavelength calibration>), as it is not mentioned in the main text
L122 - Woo reference missing a date
L145 - Equation 3 should be Equation 15 of Kwon et al. (2019)
L167 - Figure 5 is referenced in the text before Figure 4 - maybe this is ok as I don’t think the AMT style guide prohibits, but flagging just in case.
L233 - “MAS-DOAS” to “MAX-DOAS”
L237 - “Inaugural” sounds a bit weird in this context. Maybe just “first”
L237 “from the geostationary” to “from a geostationary satellite”
L239 “involving reference spectrum…” to “involving the selection of reference spectrum…”
L239 “AMF” to “AMFs”
Figure 1 - “values of the spectral fitting” to “values from the spectral fit residuals”
Figure 3 - Maybe change “the hexagon filled with colormap indicate” to “the hexagonal heatmap indicates”
Citation: https://doi.org/10.5194/egusphere-2024-589-RC1
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