Evaluation of the smile effect on the Earth Clouds, Aerosols and Radiation Explorer (EarthCARE)/Multi-Spectral Imager (MSI) cloud product

Wang, Minrui; Nakajima, Takashi Y.; Roh, Woosub; Satoh, Masaki; Suzuki, Kentaroh; Kubota, Takuji; Yoshida, Mayumi

doi:https://doi.org/10.5194/egusphere-2022-736

Preprints

https://doi.org/10.5194/egusphere-2022-736

Preprints

05 Aug 2022

| 05 Aug 2022

Evaluation of the smile effect on the Earth Clouds, Aerosols and Radiation Explorer (EarthCARE)/Multi-Spectral Imager (MSI) cloud product

Minrui Wang, Takashi Y. Nakajima, Woosub Roh, Masaki Satoh, Kentaroh Suzuki, Takuji Kubota, and Mayumi Yoshida

Abstract. A cloud identification and profiling algorithm is being developed for the Multi-Spectral Imager (MSI), which is one of the four instruments that the Earth Clouds, Aerosols and Radiation Explorer (EarthCARE) spacecraft will feature. During recent work, we noticed that the MSI response function could shift substantially among some wavelengths (0.67 and 1.65 µm bands) owing to the smile effect, that is an effect in which a shift in the center wavelength appears as a distortion in the spectral image. We evaluated how the smile effect affects the cloud retrieval product qualitatively and quantitatively. We chose four detector pixels from bands 1 and 3 with the nadir pixel as the reference to elucidate how the smile effect error affects the cloud optical thickness (τ) and effective cloud droplet radius (r_e) by simulating the MSI forward radiation with Comprehensive Analysis Program for Cloud Optical Measurement (CAPCOM). We also evaluated the error in simulated scenes from a global cloud system resolving model and a satellite simulator to measure the effect on actual observation scenes. For typical shallow warm clouds (τ = 8, r_e = 8 μm), the smile effect on the cloud retrieval was not significant in most cases (up to 6 % error). For typical deep convective clouds (τ = 8, r_e = 40 μm), the smile effect on the cloud retrieval was even less significant in most cases (up to 4 % error). Moreover, our results from two oceanic scenes using the synthetic MSI data agreed well with the forward radiation simulation, indicating that the error from the smile effect was generally within 10 %.

Received: 02 Aug 2022 – Discussion started: 05 Aug 2022

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Journal article(s) based on this preprint

31 Jan 2023

Evaluation of the spectral misalignment on the Earth Clouds, Aerosols and Radiation Explorer/multi-spectral imager cloud product

Minrui Wang, Takashi Y. Nakajima, Woosub Roh, Masaki Satoh, Kentaroh Suzuki, Takuji Kubota, and Mayumi Yoshida

Atmos. Meas. Tech., 16, 603–623, https://doi.org/10.5194/amt-16-603-2023,https://doi.org/10.5194/amt-16-603-2023, 2023

Short summary

Minrui Wang et al.

Interactive discussion

Status: closed

RC1:
'Comment on egusphere-2022-736', Anonymous Referee #1, 15 Sep 2022
Full title: Evaluation of the smile effect on the Earth Clouds, Aerosols and Radiation Explorer (EarthCARE) /Multi-Spectral Imager (MSI) cloud product

Authors: Wang et al.

This paper investigated the potential errors of retrieved cloud properties (COT, CER) for EarthCARE/MSI based on both theorical calculation and numerical simulation. Results indict that errors caused by the smile effect was generally within 10% for typical shallow warm clouds and deep convective clouds. Results are interesting, and can be used as references for other instruments. Overall, this manuscript is clear. However, there are several issues that need to be taken care of before this paper becomes acceptable for publication.

Specific comments:

radiation transfer model, or radiative transfer model? Please check which is better. I think the words “radiative transfer model” are better.

L121, the cloud particle size distribution in this paper is

n(r) = c/r exp[-(lnr – lnr0)^2 / (2ð^2)]

While the Nakajima and Nakajima (1995), the n(r) is:

n(r) = N / (sqrt(2pi) ð) exp[-(lnr – lnr0)^2 / (2ð^2)].

Why your eq. for the first term contains “r”, while the Nakajima’s is “N”?

Nakajima, T.Y., Nakajima, T., 1995. Wide-Area Determination of Cloud Microphysical Properties from NOAA AVHRR Measurements for FIRE and ASTEX Regions. Journal of the Atmospheric Sciences.

L160, the reciprocal of dL/ ðð is ðð/dL, not dt/dL. Please check it.

L195, where is the Fsw from? Please give the reference. And what’s the S0, n, k mean in Eq. (5)-(11)?

L209, Eq. (13) presents the relation between Fsw and CDR, if CDR decreased by 10%, Fsw would decrease by about 4.2Wm-2. So, what the relationship between Fsw and COT? I want to know that how COT changes, resulting Fsw changes?

The CAPCOM can used for retrieval of COT, CER, and CTT or CTH. The authors investigated the smile error on COT and CER, how about the CTH?
Citation: https://doi.org/10.5194/egusphere-2022-736-RC1
- AC1:
  'Reply on RC1', Minrui Wang, 20 Oct 2022
  Dear referee:
  
  Thank you for providing these insights. We appreciate the time and effort you and each of the reviewers have dedicated to providing insightful feedback on ways to strengthen our paper.
  
  The following is a point-by-point response to the specific comments:
  
  radiation transfer model, or radiative transfer model? Please check which is better. I think the words “radiative transfer model” are better.
  
  RESPONSE: We agreed with your assessment that the words “radiative transfer model” are better. We will change the words in the revised manuscript.
  
  L121, the cloud particle size distribution in this paper is
  
  n(r) = c/r exp[-(lnr – lnr0)^2 / (2ð^2)]
  While the Nakajima and Nakajima (1995), the n(r) is:
  n(r) = N / (sqrt(2pi) ð) exp[-(lnr – lnr0)^2 / (2ð^2)].
  Why your eq. for the first term contains “r”, while the Nakajima’s is “N”?
  Nakajima, T.Y., Nakajima, T., 1995. Wide-Area Determination of Cloud Microphysical Properties from NOAA AVHRR Measurements for FIRE and ASTEX Regions. Journal of the Atmospheric Sciences.
  RESPONSE: The “N” in Nakajima and Nakajima (1995) means total particle number, which is an arbitrary constant.
  While the equation used in L121 of our manuscript is originally based on Nakajima and King (1990), in which “C” is a constant.
  Both equations show the same lognormal distribution function for cloud particle size distribution.
  Of course, the particle size distribution is considered in the calculation of the COT in CAPCOM. However, the particle size distribution is just used as a relative value to perceive the frequency dependence of the optical thickness. The COT is not directly calculated from the particle size distribution.
  
  Nakajima, T. and King, M. D.: Determination of the optical thickness and effective particle radius of clouds from reflected solar radiation measurements. Part I: Theory, J. Atmos. Sci., 47, 1878–1893, doi:10.1175/1520- 0469(1990)047<1878:DOTOTA>2.0.CO;2, 1990.
  
  L160, the reciprocal of dL/ ð ð is ð ð /dL, not dt/dL. Please check it.
  
  RESPONSE: We have checked the line and confirmed that dt/dL was a mistype. We will fix it in the revised manuscript.
  
  L195, where is the Fsw from? Please give the reference. And what’s the S0, n, k mean in Eq. (5)-(11)?
  
  RESPONSE: Eq. (5) is used to represent a theoretical relationship between shortwave radiation (Fsw), solar constant (S0), cloud cover (n), and the change of cloud albedo (Δα).
  Since the optical thickness of the gas-only atmosphere is approximately 0.2, the changes in global mean shortwave radiation according to Δα can be expressed as Eq. (5).
  Eq. (9) is also a theoretical relationship that can be found in Brenguier et al. 2011, and “k” equals to 3/2.
  We will add the explanations about S0, n, k as well as the reference to the revised manuscript.
  
  Brenguier, J. -L., Burnet, F., and Geoffroy, O.: Cloud optical thickness and liquid water path – does the k coefficient vary with droplet concentration? Atmospheric Chemistry and Physics, 11, 9771-9786, doi:10.5194/acp-11-9771-2011, 2011.
  
  L209, Eq. (13) presents the relation between Fsw and CDR, if CDR decreased by 10%, Fsw would decrease by about 4.2Wm-2. So, what the relationship between Fsw and COT? I want to know that how COT changes, resulting Fsw changes?
  
  RESPONSE: From Eq. (11) in L204 we can know that when W is a constant (ΔW = 0), then
  Δτ/τ = -Δre/re,
  and we can rewrite Eq. (13) as
  Fsw = - 42 x Δτ/τ.
  So, if COT increased by 10%, then Fsw would decrease by about 4.2 W/m².
  
  The CAPCOM can used for retrieval of COT, CER, and CTT or CTH. The authors investigated the smile error on COT and CER, how about the CTH?
  
  RESPONSE: In CAPCOM, CTH is determined by comparing CTT with temperature vertical profile T(z), which is from global objective analysis data. Therefore, the error of CTH is ascribed to the error of CTT, directly.
  Since this paper centers on discussing the smile effect on COT and CDR, we did not talk much about CTH or CTT. We believe that the error in CTH (CTT) is expected to be small, at least to have little effect on the shortwave radiation budget. This is because CTT is related to the emissivity determined by the cloud characteristics, and the emissivity does not fluctuate so much, so we believe that the smile effect does not affect the CTT very much.
  
  Citation: https://doi.org/10.5194/egusphere-2022-736-AC1
RC2:
'Comment on egusphere-2022-736', Anonymous Referee #2, 19 Oct 2022

General Comments:

The manuscript is well written and structured. Basic information about the mission, instruments and retrieval algorithms is provided, such that also readers not familiar with the mission or instrument can follow. For details about the retrieval algorithms, sufficient references are given to allow the interested reader to get the necessary background information where needed. The methodology and approach for data analysis is well explained and the results are presented in a clear and concise manner. Limitations and areas for further development are also stated clearly. The research topic addressed is relevant to the remote sensing community focused on cloud retrieval. I recommend the publication of this manuscript after some minor corrections as suggested in the specific comments and technical corrections below.

Specific Comments:

Line 100:

“sunny” does not necessarily imply clear-sky. Therefore, I propose to replace the term “sunny” with the term “cloud-free” or “clear-sky”.

Line 137, Table 2:

I assume the “393” in column D refers to the 393 km altitude of the mission orbit? I suggest to add this information in the Table caption or better to add it in Table 1 of the general mission characteristics.

Line 196, 198, equations (5) and (6):

Add an explanation what S₀, n and g are

Line 209, equation (13):

The unit for FSW is missing. Please add.

Line 336, Figure 15:

There seem to be regions in the error distribution plots (b) and (c) at positions around x=20-100 and y=850-900 as well as x=350 and y=450 where no error is found but clouds are present according to panel (a). Does this mean that these regions are not shallow warm clouds or does that mean that the error is off the scale? A short explanation would be appreciated.

Line 363-365:

The structure of this sentence is confusing. Please try to reformulate. Also, the statement that delta tau on pix_BND1_min and pix_BND1_max are generally larger than on pix_BND3_min and pix_BND3_max seems contradictory to Tables 3 and 4. From there I read that the error pix_BND1_min > pix_BND3_min but pix_BND1_max < pix_BND3_max and vice versa for re. Please clarify.

Line 384:

What does “extreme” error mean here? Please clarify or quantify.

Line 388-389:

I would suggest to add here that this statement is only true for the water surfaces that were analyzed in this study. As indicated later on, the effect for scenes over land are not quantified yet and therefore the statement that an onboard correction is generally not necessary would probably require an analysis of the land cases too.

Line 390-397:

It is very good that the authors have pointed out that the impact of the smile effect for scenes over land might be much more difficult to quantify and will require more work. It should therefore be made clear in the abstract that the basic conclusion, i.e. that the impact of the smile effect is negligible, is true for water surfaces but needs to be investigated further for land surfaces.

Technical corrections:

Line 42:

…degrades the spectral information and reduces classification…

Line 93:

All resolutions --> All spatial resolutions

Line 93:

The algorithms calculate the MSI standard product --> The algorithms used to calculate the MSI standard product

Line 160:

It says dt/dL instead of d(tau)/dL. Replace t --> Greek letter tau

Line 177:

in-cluding --> including

Line 236:

up to 150 m --> up to 150 μm

Line 249:

session 2.1.2 --> section 2.1.2

Line251, caption of Table 3:

r_e = 8 m --> r_e = 8 μm

Line 261:

The red frames show the position of a typical shallow warm clouds --> The red frames show the position of typical shallow warm clouds

Line 338, caption of Figure 15:

There seem to be internal comments left over at the end of the caption: (change the “tau” “efr” to COT CDR) (boundary area = convective zone of cloud and clear sky). Please remove or integrate into the text.

Line 373:

“numerous works are settled to reduce errors…” is suggested to be reformulated to e.g. “numerous studies have been performed to characterize errors…”

Line 374:

our work based --> our work is based

Line 374:

calculation --> calculations

Line 375:

simulation --> simulations

Line 376:

futural --> future

Line 377:

quantity --> quantities

Line 376 -378:

I would suggest to break this long sentence into two, e.g.: “…affects the retrieval of cloud physical quantities. This provides a useful reference for the development of future cloud observation instruments.”

Line 385:

special --> spatial

Line 387:

I would suggest to replace “appreciable” with “significant”.

Line 400:

special --> spatial

Citation: https://doi.org/10.5194/egusphere-2022-736-RC2
- AC2: 'Reply on RC2', Minrui Wang, 24 Oct 2022
  
  Dear referee:
  Thank you for providing these insights. We appreciate the time and effort you and each of the reviewers have dedicated to providing insightful feedback, and we will make the corrections according to the comments in the revised manuscript.
  
  For the technical corrections, we will fix them in the revised manuscript, thank you very much for pointing them out.
  The following is our response to the specific comments:
  Line 100:
  
  “sunny” does not necessarily imply clear-sky. Therefore, I propose to replace the term “sunny” with the term “cloud-free” or “clear-sky”.
  
  RESPONSE: We agreed with your assessment. We will replace the term “sunny” with the term “clear-sky” in the revised manuscript.
  Line 137, Table 2:
  
  I assume the “393” in column D refers to the 393 km altitude of the mission orbit? I suggest to add this information in the Table caption or better to add it in Table 1 of the general mission characteristics.
  
  RESPONSE: Yes, the “393” in column D does refer to the 393 km altitude of the mission orbit. We will add this information directly in Table 1.
  Line 196, 198, equations (5) and (6):
  
  Add an explanation what S0, n and g are
  
  Line 209, equation (13):
  
  The unit for FSW is missing. Please add.
  
  RESPONSE: We will add the explanation and the unit for Fsw in the revised manuscript.
  Line 336, Figure 15:
  
  There seem to be regions in the error distribution plots (b) and (c) at positions around x=20-100 and y=850-900 as well as x=350 and y=450 where no error is found but clouds are present according to panel (a). Does this mean that these regions are not shallow warm clouds or does that mean that the error is off the scale? A short explanation would be appreciated.
  
  RESPONSE: The region around x=20-100 and y=850-900 is not defined as shallow warm clouds, while the error is off the scale in the region around x=350 and y=450. We will add a short explanation about these two regions.
  Line 363-365:
  
  The structure of this sentence is confusing. Please try to reformulate. Also, the statement that delta tau on pix_BND1_min and pix_BND1_max are generally larger than on pix_BND3_min and pix_BND3_max seems contradictory to Tables 3 and 4. From there I read that the error pix_BND1_min > pix_BND3_min but pix_BND1_max < pix_BND3_max and vice versa for re. Please clarify.
  
  RESPONSE: We will reformulate the sentence here according to every case in Table 3 and 4, both for COT error and CDR error.
  Line 384:
  
  What does “extreme” error mean here? Please clarify or quantify.
  
  RESPONSE: The extreme error can be found from Table 3 and 4 for both COT error and CDR error, we will add an explanation to clarify the exact value of them in the sentence.
  Line 388-389:
  
  I would suggest to add here that this statement is only true for the water surfaces that were analyzed in this study. As indicated later on, the effect for scenes over land are not quantified yet and therefore the statement that an onboard correction is generally not necessary would probably require an analysis of the land cases too.
  
  Line 390-397:
  
  It is very good that the authors have pointed out that the impact of the smile effect for scenes over land might be much more difficult to quantify and will require more work. It should therefore be made clear in the abstract that the basic conclusion, i.e. that the impact of the smile effect is negligible, is true for water surfaces but needs to be investigated further for land surfaces.
  
  RESPONSE: We will add the explanation in both the conclusion part and the abstract part to state that further works are still needed to analysis the land cases.
  
  Citation: https://doi.org/10.5194/egusphere-2022-736-AC2

Interactive discussion

Status: closed

RC1:
'Comment on egusphere-2022-736', Anonymous Referee #1, 15 Sep 2022
Full title: Evaluation of the smile effect on the Earth Clouds, Aerosols and Radiation Explorer (EarthCARE) /Multi-Spectral Imager (MSI) cloud product

Authors: Wang et al.

This paper investigated the potential errors of retrieved cloud properties (COT, CER) for EarthCARE/MSI based on both theorical calculation and numerical simulation. Results indict that errors caused by the smile effect was generally within 10% for typical shallow warm clouds and deep convective clouds. Results are interesting, and can be used as references for other instruments. Overall, this manuscript is clear. However, there are several issues that need to be taken care of before this paper becomes acceptable for publication.

Specific comments:

radiation transfer model, or radiative transfer model? Please check which is better. I think the words “radiative transfer model” are better.

L121, the cloud particle size distribution in this paper is

n(r) = c/r exp[-(lnr – lnr0)^2 / (2ð^2)]

While the Nakajima and Nakajima (1995), the n(r) is:

n(r) = N / (sqrt(2pi) ð) exp[-(lnr – lnr0)^2 / (2ð^2)].

Why your eq. for the first term contains “r”, while the Nakajima’s is “N”?

Nakajima, T.Y., Nakajima, T., 1995. Wide-Area Determination of Cloud Microphysical Properties from NOAA AVHRR Measurements for FIRE and ASTEX Regions. Journal of the Atmospheric Sciences.

L160, the reciprocal of dL/ ðð is ðð/dL, not dt/dL. Please check it.

L195, where is the Fsw from? Please give the reference. And what’s the S0, n, k mean in Eq. (5)-(11)?

L209, Eq. (13) presents the relation between Fsw and CDR, if CDR decreased by 10%, Fsw would decrease by about 4.2Wm-2. So, what the relationship between Fsw and COT? I want to know that how COT changes, resulting Fsw changes?

The CAPCOM can used for retrieval of COT, CER, and CTT or CTH. The authors investigated the smile error on COT and CER, how about the CTH?
Citation: https://doi.org/10.5194/egusphere-2022-736-RC1
- AC1:
  'Reply on RC1', Minrui Wang, 20 Oct 2022
  Dear referee:
  
  Thank you for providing these insights. We appreciate the time and effort you and each of the reviewers have dedicated to providing insightful feedback on ways to strengthen our paper.
  
  The following is a point-by-point response to the specific comments:
  
  radiation transfer model, or radiative transfer model? Please check which is better. I think the words “radiative transfer model” are better.
  
  RESPONSE: We agreed with your assessment that the words “radiative transfer model” are better. We will change the words in the revised manuscript.
  
  L121, the cloud particle size distribution in this paper is
  
  n(r) = c/r exp[-(lnr – lnr0)^2 / (2ð^2)]
  While the Nakajima and Nakajima (1995), the n(r) is:
  n(r) = N / (sqrt(2pi) ð) exp[-(lnr – lnr0)^2 / (2ð^2)].
  Why your eq. for the first term contains “r”, while the Nakajima’s is “N”?
  Nakajima, T.Y., Nakajima, T., 1995. Wide-Area Determination of Cloud Microphysical Properties from NOAA AVHRR Measurements for FIRE and ASTEX Regions. Journal of the Atmospheric Sciences.
  RESPONSE: The “N” in Nakajima and Nakajima (1995) means total particle number, which is an arbitrary constant.
  While the equation used in L121 of our manuscript is originally based on Nakajima and King (1990), in which “C” is a constant.
  Both equations show the same lognormal distribution function for cloud particle size distribution.
  Of course, the particle size distribution is considered in the calculation of the COT in CAPCOM. However, the particle size distribution is just used as a relative value to perceive the frequency dependence of the optical thickness. The COT is not directly calculated from the particle size distribution.
  
  Nakajima, T. and King, M. D.: Determination of the optical thickness and effective particle radius of clouds from reflected solar radiation measurements. Part I: Theory, J. Atmos. Sci., 47, 1878–1893, doi:10.1175/1520- 0469(1990)047<1878:DOTOTA>2.0.CO;2, 1990.
  
  L160, the reciprocal of dL/ ð ð is ð ð /dL, not dt/dL. Please check it.
  
  RESPONSE: We have checked the line and confirmed that dt/dL was a mistype. We will fix it in the revised manuscript.
  
  L195, where is the Fsw from? Please give the reference. And what’s the S0, n, k mean in Eq. (5)-(11)?
  
  RESPONSE: Eq. (5) is used to represent a theoretical relationship between shortwave radiation (Fsw), solar constant (S0), cloud cover (n), and the change of cloud albedo (Δα).
  Since the optical thickness of the gas-only atmosphere is approximately 0.2, the changes in global mean shortwave radiation according to Δα can be expressed as Eq. (5).
  Eq. (9) is also a theoretical relationship that can be found in Brenguier et al. 2011, and “k” equals to 3/2.
  We will add the explanations about S0, n, k as well as the reference to the revised manuscript.
  
  Brenguier, J. -L., Burnet, F., and Geoffroy, O.: Cloud optical thickness and liquid water path – does the k coefficient vary with droplet concentration? Atmospheric Chemistry and Physics, 11, 9771-9786, doi:10.5194/acp-11-9771-2011, 2011.
  
  L209, Eq. (13) presents the relation between Fsw and CDR, if CDR decreased by 10%, Fsw would decrease by about 4.2Wm-2. So, what the relationship between Fsw and COT? I want to know that how COT changes, resulting Fsw changes?
  
  RESPONSE: From Eq. (11) in L204 we can know that when W is a constant (ΔW = 0), then
  Δτ/τ = -Δre/re,
  and we can rewrite Eq. (13) as
  Fsw = - 42 x Δτ/τ.
  So, if COT increased by 10%, then Fsw would decrease by about 4.2 W/m².
  
  The CAPCOM can used for retrieval of COT, CER, and CTT or CTH. The authors investigated the smile error on COT and CER, how about the CTH?
  
  RESPONSE: In CAPCOM, CTH is determined by comparing CTT with temperature vertical profile T(z), which is from global objective analysis data. Therefore, the error of CTH is ascribed to the error of CTT, directly.
  Since this paper centers on discussing the smile effect on COT and CDR, we did not talk much about CTH or CTT. We believe that the error in CTH (CTT) is expected to be small, at least to have little effect on the shortwave radiation budget. This is because CTT is related to the emissivity determined by the cloud characteristics, and the emissivity does not fluctuate so much, so we believe that the smile effect does not affect the CTT very much.
  
  Citation: https://doi.org/10.5194/egusphere-2022-736-AC1
RC2:
'Comment on egusphere-2022-736', Anonymous Referee #2, 19 Oct 2022

General Comments:

The manuscript is well written and structured. Basic information about the mission, instruments and retrieval algorithms is provided, such that also readers not familiar with the mission or instrument can follow. For details about the retrieval algorithms, sufficient references are given to allow the interested reader to get the necessary background information where needed. The methodology and approach for data analysis is well explained and the results are presented in a clear and concise manner. Limitations and areas for further development are also stated clearly. The research topic addressed is relevant to the remote sensing community focused on cloud retrieval. I recommend the publication of this manuscript after some minor corrections as suggested in the specific comments and technical corrections below.

Specific Comments:

Line 100:

“sunny” does not necessarily imply clear-sky. Therefore, I propose to replace the term “sunny” with the term “cloud-free” or “clear-sky”.

Line 137, Table 2:

I assume the “393” in column D refers to the 393 km altitude of the mission orbit? I suggest to add this information in the Table caption or better to add it in Table 1 of the general mission characteristics.

Line 196, 198, equations (5) and (6):

Add an explanation what S₀, n and g are

Line 209, equation (13):

The unit for FSW is missing. Please add.

Line 336, Figure 15:

There seem to be regions in the error distribution plots (b) and (c) at positions around x=20-100 and y=850-900 as well as x=350 and y=450 where no error is found but clouds are present according to panel (a). Does this mean that these regions are not shallow warm clouds or does that mean that the error is off the scale? A short explanation would be appreciated.

Line 363-365:

The structure of this sentence is confusing. Please try to reformulate. Also, the statement that delta tau on pix_BND1_min and pix_BND1_max are generally larger than on pix_BND3_min and pix_BND3_max seems contradictory to Tables 3 and 4. From there I read that the error pix_BND1_min > pix_BND3_min but pix_BND1_max < pix_BND3_max and vice versa for re. Please clarify.

Line 384:

What does “extreme” error mean here? Please clarify or quantify.

Line 388-389:

I would suggest to add here that this statement is only true for the water surfaces that were analyzed in this study. As indicated later on, the effect for scenes over land are not quantified yet and therefore the statement that an onboard correction is generally not necessary would probably require an analysis of the land cases too.

Line 390-397:

It is very good that the authors have pointed out that the impact of the smile effect for scenes over land might be much more difficult to quantify and will require more work. It should therefore be made clear in the abstract that the basic conclusion, i.e. that the impact of the smile effect is negligible, is true for water surfaces but needs to be investigated further for land surfaces.

Technical corrections:

Line 42:

…degrades the spectral information and reduces classification…

Line 93:

All resolutions --> All spatial resolutions

Line 93:

The algorithms calculate the MSI standard product --> The algorithms used to calculate the MSI standard product

Line 160:

It says dt/dL instead of d(tau)/dL. Replace t --> Greek letter tau

Line 177:

in-cluding --> including

Line 236:

up to 150 m --> up to 150 μm

Line 249:

session 2.1.2 --> section 2.1.2

Line251, caption of Table 3:

r_e = 8 m --> r_e = 8 μm

Line 261:

The red frames show the position of a typical shallow warm clouds --> The red frames show the position of typical shallow warm clouds

Line 338, caption of Figure 15:

There seem to be internal comments left over at the end of the caption: (change the “tau” “efr” to COT CDR) (boundary area = convective zone of cloud and clear sky). Please remove or integrate into the text.

Line 373:

“numerous works are settled to reduce errors…” is suggested to be reformulated to e.g. “numerous studies have been performed to characterize errors…”

Line 374:

our work based --> our work is based

Line 374:

calculation --> calculations

Line 375:

simulation --> simulations

Line 376:

futural --> future

Line 377:

quantity --> quantities

Line 376 -378:

I would suggest to break this long sentence into two, e.g.: “…affects the retrieval of cloud physical quantities. This provides a useful reference for the development of future cloud observation instruments.”

Line 385:

special --> spatial

Line 387:

I would suggest to replace “appreciable” with “significant”.

Line 400:

special --> spatial

Citation: https://doi.org/10.5194/egusphere-2022-736-RC2
- AC2: 'Reply on RC2', Minrui Wang, 24 Oct 2022
  
  Dear referee:
  Thank you for providing these insights. We appreciate the time and effort you and each of the reviewers have dedicated to providing insightful feedback, and we will make the corrections according to the comments in the revised manuscript.
  
  For the technical corrections, we will fix them in the revised manuscript, thank you very much for pointing them out.
  The following is our response to the specific comments:
  Line 100:
  
  “sunny” does not necessarily imply clear-sky. Therefore, I propose to replace the term “sunny” with the term “cloud-free” or “clear-sky”.
  
  RESPONSE: We agreed with your assessment. We will replace the term “sunny” with the term “clear-sky” in the revised manuscript.
  Line 137, Table 2:
  
  I assume the “393” in column D refers to the 393 km altitude of the mission orbit? I suggest to add this information in the Table caption or better to add it in Table 1 of the general mission characteristics.
  
  RESPONSE: Yes, the “393” in column D does refer to the 393 km altitude of the mission orbit. We will add this information directly in Table 1.
  Line 196, 198, equations (5) and (6):
  
  Add an explanation what S0, n and g are
  
  Line 209, equation (13):
  
  The unit for FSW is missing. Please add.
  
  RESPONSE: We will add the explanation and the unit for Fsw in the revised manuscript.
  Line 336, Figure 15:
  
  There seem to be regions in the error distribution plots (b) and (c) at positions around x=20-100 and y=850-900 as well as x=350 and y=450 where no error is found but clouds are present according to panel (a). Does this mean that these regions are not shallow warm clouds or does that mean that the error is off the scale? A short explanation would be appreciated.
  
  RESPONSE: The region around x=20-100 and y=850-900 is not defined as shallow warm clouds, while the error is off the scale in the region around x=350 and y=450. We will add a short explanation about these two regions.
  Line 363-365:
  
  The structure of this sentence is confusing. Please try to reformulate. Also, the statement that delta tau on pix_BND1_min and pix_BND1_max are generally larger than on pix_BND3_min and pix_BND3_max seems contradictory to Tables 3 and 4. From there I read that the error pix_BND1_min > pix_BND3_min but pix_BND1_max < pix_BND3_max and vice versa for re. Please clarify.
  
  RESPONSE: We will reformulate the sentence here according to every case in Table 3 and 4, both for COT error and CDR error.
  Line 384:
  
  What does “extreme” error mean here? Please clarify or quantify.
  
  RESPONSE: The extreme error can be found from Table 3 and 4 for both COT error and CDR error, we will add an explanation to clarify the exact value of them in the sentence.
  Line 388-389:
  
  I would suggest to add here that this statement is only true for the water surfaces that were analyzed in this study. As indicated later on, the effect for scenes over land are not quantified yet and therefore the statement that an onboard correction is generally not necessary would probably require an analysis of the land cases too.
  
  Line 390-397:
  
  It is very good that the authors have pointed out that the impact of the smile effect for scenes over land might be much more difficult to quantify and will require more work. It should therefore be made clear in the abstract that the basic conclusion, i.e. that the impact of the smile effect is negligible, is true for water surfaces but needs to be investigated further for land surfaces.
  
  RESPONSE: We will add the explanation in both the conclusion part and the abstract part to state that further works are still needed to analysis the land cases.
  
  Citation: https://doi.org/10.5194/egusphere-2022-736-AC2

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision | EF: Editorial file upload

AR by Minrui Wang on behalf of the Authors (25 Nov 2022) Author's response

EF by Polina Shvedko (01 Dec 2022) Manuscript Author's tracked changes

ED: Publish subject to technical corrections (07 Dec 2022) by Jian Xu

AR by Minrui Wang on behalf of the Authors (14 Dec 2022) Manuscript

Journal article(s) based on this preprint

31 Jan 2023

Evaluation of the spectral misalignment on the Earth Clouds, Aerosols and Radiation Explorer/multi-spectral imager cloud product

Minrui Wang, Takashi Y. Nakajima, Woosub Roh, Masaki Satoh, Kentaroh Suzuki, Takuji Kubota, and Mayumi Yoshida

Atmos. Meas. Tech., 16, 603–623, https://doi.org/10.5194/amt-16-603-2023,https://doi.org/10.5194/amt-16-603-2023, 2023

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Minrui Wang et al.

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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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Smile effect (an effect in which a shift in the center wavelength appears as a distortion in the spectral image) was detected during our recent work. To evaluate how it affects the cloud retrieval product, we did a simulation of MSI forward radiation, then evaluated the error in simulated scenes from a global cloud system resolving model and a satellite simulator. Our results indicated that the error from the smile effect was generally small and could be seen as negligible for oceanic scenes.


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