the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 01 Nov 2023
Assessment of the Spectral MIsaLignment Effect (SMILE) on EarthCARE’s Multi-Spectral Imager aerosol and cloud property retrievals
Abstract. The Multi-spectral Imager (MSI) on board the Earth Cloud, Aerosol and Radiation Explorer (EarthCARE) will provide horizontal information about aerosols and clouds. These measurements are needed to extend vertical cloud and aerosol property information, which are obtained from EarthCARE's active sensors, in order to obtain a full three dimensional view on cloud and aerosol conditions. Especially, meso-scale weather systems will be characterized. The discovery of a non-compliance of the MSI VNS camera’s visible (VIS) and shortwave-infrared (SWIR1) channels regarding a spectral central wavelength (CWVL) shift across track of up to 14 nm (VIS) and 20 nm (SWIR1), led to the need for an analysis regarding its impact on MSI Level 2A aerosol and cloud products. A significant influence of the Spectral MIsaLignment Effect (SMILE) on MSI retrievals is identified due to the spectral variation of gas absorption, surface reflectance as well as aerosol and cloud properties within the spectral ranges of these MSI bands. For example, the VIS channel is positioned in close proximity to the red edge of green vegetation and is impacted by residual absorption of water vapour and ozone. Small central wavelength variations introduce uncertainties due to the rapid change in surface reflectance for conditions with low optical thickness. The present central wavelength shift in the VIS towards shorter wavelengths than at nadir introduces a relative error in transmission of up to 3.3 % due to the increasing influence of water vapour and ozone absorption. We found relative errors in the TOA signal due to the SMILE of up to 30 % for low optical thickness over a land surface in that band. Since the magnitude of the impact strongly depends on the underlying surface and atmospheric conditions, we conclude that accounting for the SMILE in Level 2 retrievals or correcting the Level 1 signal will improve MSI aerosol and cloud product quality.
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Notice on discussion status
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
(2834 KB)
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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.
- Preprint
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- Final revised paper
Journal article(s) based on this preprint
Interactive discussion
Status: closed
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RC1: 'Comment on egusphere-2023-2002', Anonymous Referee #1, 23 Nov 2023
General comments:
This manuscript presents an investigation of the spectral misalignment Effect (SMILE) on aerosol and cloud retrievals with a focus over land. A number of potentially important sources of biases are assessed. The paper is well suitable to the scope of AMT, in terms of the question proposed to address.
I suggest major revision, considering the limitations and potential improvements as follows.
Writing could be improved. There are quite some grammar mistakes. I provided corrections for some of them, but there are more to be corrected. Please remove the words that were repeatedly used but unnecessarily or incorrectly, such as “already”, ‘e.g.’, ‘i.e.’, and “therefore”(used when there was no causal relationship). Also, the tense was misused sometimes. A thorough examination must be performed while authors revise the manuscript.
In Section 3.2 and 4, natural variability of cloud microphysical properties is not sufficiently considered with liquid cloud and ice effective radius as well as COT and AOT assumed to be constant. This source of uncertainty should be examined, given VIS and SWIR channels are strong functions of these key properties, serving as main constraints for retrievals. Also, there is a lack of explanation on why these values are chosen. With a few constants assumed, the findings would only apply to a limited subset in nature.
Why do you assume the surfaces to be barren or forest in Section 4 instead of snow and grass surfaces, which seem to cause large biases based on Fig. 5. Or why barren and forest are not taken into account in Fig.5, if they are important?
For your experiments, surface types can be selected, while in reality determining the surface type over land can be very complex. How does this uncertainty affect your evaluation?
SMILE is not a new issue but I did not see evaluations of your results against previous work.
Specific comments:
Line 41: Please explain why you focus on smaller optical thickness. How small?
Line 110: What is the value of “absolute accuracy requirement for aerosol optical thickness”? Please also add references for it.
Line 131-135: The underlying physics that allows for retrievals and possible sources of uncertainties should be more clearly discussed here.
Line 138: What are the relevant assumptions in producing LUTs? You should provide a context for readers who may have little knowledge of this Level 2A retrieval. Please also provide references.
Line 140: Please summarize what is actually used from Baum et al. (2014).
Line 141: You assume effective radius to be 10 and 5 microns for ice and liquid clouds, respectively. What is their natural variability shown in observations? How does that variability affect your results?
Line 181: Please explain why these constants are chosen and provide references if necessary. They appear to be at the smaller end. How representative are they? This is important, as you are investigating spaceborne observations that provide global coverage.
Figure 9: To what extent the results would differ from what you have, if you assumed COT to be 50, or liquid cloud effective radius being 10 or 20 microns?
Line 196: references should be added.
Line 205: optical thickness of 10 is low for stratocumulus. Please justify your choice.
Line 236-237: I can not see “cloud properties play a main role” in your results as you stated. Cloud effective radius was assumed to be constant in your experiment design. Please elaborate.
Fig.12: Why are clouds not investigated over ocean surfaces?
Line 252-253: what is the value of “a higher cloud effective radius”?
Technical corrections:
Figures: colors of lines should be selected so that one line can be easily distinguished from another. For instance, the colors you picked in figures 6, 7, and 9 to indicate dust, fmless and fmstrg are quite similar, which makes it difficult to read your graphs. Please change the colors.
Fig. 1.: no color bar
Line 72: “Tab.” is not a common abbreviation for table. Please use Table instead.
Line 78: “in the respective” => on
Figure 4. Please change the relative error on the y axis to values in %, so it will be consistent with your text. Also, it will be easier to compare the magnitude differences among the 4 channels. Same applies to other similar figures.
Line 105 to 106: The meaning of this sentence is unclear. Please rephrase.
Line 121: “lower 10” => lower than 10
Line 127: This cannot be described as “same behavior”, as you pointed out in the following sentences that magnitudes and even signs are different.
Line 129: I think it should be Fig. 5d here.
Line 131-132: “VIS-to-NIR” => VIS to NIR
Line 147: band => bands
Figure 6: What do “fmless” and “fmstrg” indicate, respectively?
Line 153: “While,” => “While”
Line 164: 2 verbs
Line 177: “it has not been accounted for gas absorption” => gas absorption is not accounted for (if I understand correctly).
Line 180: “Exemplary” => For example
Line 206: “are altering” => vary
Line 217: “of e.g.” => such as
Line 231: “optical” => optically
Line 239: “implication” => implications
“aerosol” => aerosols
Line 240: Please rephrase the 1st sentence of this paragraph.
Line 249: what is “TIR”? Not previously mentioned.
Line 287: “mitigate already existing retrieval algorithms” => mitigate the relevant errors in existing retrieval algorithms.
Citation: https://doi.org/10.5194/egusphere-2023-2002-RC1 -
AC1: 'Reply on RC1', Nicole Docter, 16 Feb 2024
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2023/egusphere-2023-2002/egusphere-2023-2002-AC1-supplement.pdf
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AC1: 'Reply on RC1', Nicole Docter, 16 Feb 2024
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RC2: 'Comment on egusphere-2023-2002', Anonymous Referee #3, 09 Jan 2024
General comments:
This manuscript discusses the impact of the Spectral Misalignment Effect (SMILE) on the EarthCARE Multi-spectral Imager (MSI) in retrieving aerosol and cloud properties. The paper is well suited to the scope of AMT. I suggest some minor revision considering the potential improvements as follows.
Generally, the colors in some figures are not clear enough to let readers to distinguish one from another. For example, the light-yellow line in Figure 5 is very hard to see, and similarly, the light-yellow lines that represents salt in Figure 6, 7 and 9 are seems easily mixed with other color lines. Besides, lines in Figure 12 and Figure 13 (b) are all in reddish colors, which color arrangement should be considered. Finally, the dark purple color in the bottom area of Figure 10 (c) makes the words “water” almost invisible, you should consider change the color into lighter bluish color, or change the color of the words “water” into white.
Specific comments:
Line 31-35: Please add some reference to support the story of “mitigation strategies have been implemented by ESA and industry”.
Figure 1: There is no color bar to explain the range of the MSI response functions shown, please add it.
Line 141-142: You noted the effective radii of both types of cloud droplets, how about the definition of optical thickness? Are they as same as you noted in Line 181-182?
Line 178: “level2” --> “Level 2”, “retrieval” --> “retrievals”
Line 185-186: Can you provide some references for this?
Line 205: I think you should at least add another case with a larger COT for the water cloud, only 10 is not sufficient to represent all water clouds.
Citation: https://doi.org/10.5194/egusphere-2023-2002-RC2 -
AC2: 'Reply on RC2', Nicole Docter, 16 Feb 2024
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2023/egusphere-2023-2002/egusphere-2023-2002-AC2-supplement.pdf
-
AC2: 'Reply on RC2', Nicole Docter, 16 Feb 2024
Interactive discussion
Status: closed
-
RC1: 'Comment on egusphere-2023-2002', Anonymous Referee #1, 23 Nov 2023
General comments:
This manuscript presents an investigation of the spectral misalignment Effect (SMILE) on aerosol and cloud retrievals with a focus over land. A number of potentially important sources of biases are assessed. The paper is well suitable to the scope of AMT, in terms of the question proposed to address.
I suggest major revision, considering the limitations and potential improvements as follows.
Writing could be improved. There are quite some grammar mistakes. I provided corrections for some of them, but there are more to be corrected. Please remove the words that were repeatedly used but unnecessarily or incorrectly, such as “already”, ‘e.g.’, ‘i.e.’, and “therefore”(used when there was no causal relationship). Also, the tense was misused sometimes. A thorough examination must be performed while authors revise the manuscript.
In Section 3.2 and 4, natural variability of cloud microphysical properties is not sufficiently considered with liquid cloud and ice effective radius as well as COT and AOT assumed to be constant. This source of uncertainty should be examined, given VIS and SWIR channels are strong functions of these key properties, serving as main constraints for retrievals. Also, there is a lack of explanation on why these values are chosen. With a few constants assumed, the findings would only apply to a limited subset in nature.
Why do you assume the surfaces to be barren or forest in Section 4 instead of snow and grass surfaces, which seem to cause large biases based on Fig. 5. Or why barren and forest are not taken into account in Fig.5, if they are important?
For your experiments, surface types can be selected, while in reality determining the surface type over land can be very complex. How does this uncertainty affect your evaluation?
SMILE is not a new issue but I did not see evaluations of your results against previous work.
Specific comments:
Line 41: Please explain why you focus on smaller optical thickness. How small?
Line 110: What is the value of “absolute accuracy requirement for aerosol optical thickness”? Please also add references for it.
Line 131-135: The underlying physics that allows for retrievals and possible sources of uncertainties should be more clearly discussed here.
Line 138: What are the relevant assumptions in producing LUTs? You should provide a context for readers who may have little knowledge of this Level 2A retrieval. Please also provide references.
Line 140: Please summarize what is actually used from Baum et al. (2014).
Line 141: You assume effective radius to be 10 and 5 microns for ice and liquid clouds, respectively. What is their natural variability shown in observations? How does that variability affect your results?
Line 181: Please explain why these constants are chosen and provide references if necessary. They appear to be at the smaller end. How representative are they? This is important, as you are investigating spaceborne observations that provide global coverage.
Figure 9: To what extent the results would differ from what you have, if you assumed COT to be 50, or liquid cloud effective radius being 10 or 20 microns?
Line 196: references should be added.
Line 205: optical thickness of 10 is low for stratocumulus. Please justify your choice.
Line 236-237: I can not see “cloud properties play a main role” in your results as you stated. Cloud effective radius was assumed to be constant in your experiment design. Please elaborate.
Fig.12: Why are clouds not investigated over ocean surfaces?
Line 252-253: what is the value of “a higher cloud effective radius”?
Technical corrections:
Figures: colors of lines should be selected so that one line can be easily distinguished from another. For instance, the colors you picked in figures 6, 7, and 9 to indicate dust, fmless and fmstrg are quite similar, which makes it difficult to read your graphs. Please change the colors.
Fig. 1.: no color bar
Line 72: “Tab.” is not a common abbreviation for table. Please use Table instead.
Line 78: “in the respective” => on
Figure 4. Please change the relative error on the y axis to values in %, so it will be consistent with your text. Also, it will be easier to compare the magnitude differences among the 4 channels. Same applies to other similar figures.
Line 105 to 106: The meaning of this sentence is unclear. Please rephrase.
Line 121: “lower 10” => lower than 10
Line 127: This cannot be described as “same behavior”, as you pointed out in the following sentences that magnitudes and even signs are different.
Line 129: I think it should be Fig. 5d here.
Line 131-132: “VIS-to-NIR” => VIS to NIR
Line 147: band => bands
Figure 6: What do “fmless” and “fmstrg” indicate, respectively?
Line 153: “While,” => “While”
Line 164: 2 verbs
Line 177: “it has not been accounted for gas absorption” => gas absorption is not accounted for (if I understand correctly).
Line 180: “Exemplary” => For example
Line 206: “are altering” => vary
Line 217: “of e.g.” => such as
Line 231: “optical” => optically
Line 239: “implication” => implications
“aerosol” => aerosols
Line 240: Please rephrase the 1st sentence of this paragraph.
Line 249: what is “TIR”? Not previously mentioned.
Line 287: “mitigate already existing retrieval algorithms” => mitigate the relevant errors in existing retrieval algorithms.
Citation: https://doi.org/10.5194/egusphere-2023-2002-RC1 -
AC1: 'Reply on RC1', Nicole Docter, 16 Feb 2024
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2023/egusphere-2023-2002/egusphere-2023-2002-AC1-supplement.pdf
-
AC1: 'Reply on RC1', Nicole Docter, 16 Feb 2024
-
RC2: 'Comment on egusphere-2023-2002', Anonymous Referee #3, 09 Jan 2024
General comments:
This manuscript discusses the impact of the Spectral Misalignment Effect (SMILE) on the EarthCARE Multi-spectral Imager (MSI) in retrieving aerosol and cloud properties. The paper is well suited to the scope of AMT. I suggest some minor revision considering the potential improvements as follows.
Generally, the colors in some figures are not clear enough to let readers to distinguish one from another. For example, the light-yellow line in Figure 5 is very hard to see, and similarly, the light-yellow lines that represents salt in Figure 6, 7 and 9 are seems easily mixed with other color lines. Besides, lines in Figure 12 and Figure 13 (b) are all in reddish colors, which color arrangement should be considered. Finally, the dark purple color in the bottom area of Figure 10 (c) makes the words “water” almost invisible, you should consider change the color into lighter bluish color, or change the color of the words “water” into white.
Specific comments:
Line 31-35: Please add some reference to support the story of “mitigation strategies have been implemented by ESA and industry”.
Figure 1: There is no color bar to explain the range of the MSI response functions shown, please add it.
Line 141-142: You noted the effective radii of both types of cloud droplets, how about the definition of optical thickness? Are they as same as you noted in Line 181-182?
Line 178: “level2” --> “Level 2”, “retrieval” --> “retrievals”
Line 185-186: Can you provide some references for this?
Line 205: I think you should at least add another case with a larger COT for the water cloud, only 10 is not sufficient to represent all water clouds.
Citation: https://doi.org/10.5194/egusphere-2023-2002-RC2 -
AC2: 'Reply on RC2', Nicole Docter, 16 Feb 2024
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2023/egusphere-2023-2002/egusphere-2023-2002-AC2-supplement.pdf
-
AC2: 'Reply on RC2', Nicole Docter, 16 Feb 2024
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David Patrick Donovan
Rene Preusker
Jürgen Fischer
Jan Fokke Meirink
Piet Stammes
Michael Eisinger
The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
- Preprint
(2834 KB) - Metadata XML