the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 21 Aug 2023
Multi-wavelength dataset of aerosol extinction profiles retrieved from GOMOS stellar occultation measurements
Abstract. In this paper, we present the new multi-wavelength dataset of aerosol extinction profiles, which are retrieved from the averaged transmittance spectra by the Global Ozone Monitoring by Occultation of Stars instrument on board the Envisat satellite.
Using monthly and zonally averaged transmittances as a starting point for the retrievals enables us to improve the signal-to-noise ratio and eliminate possible modulation of transmittance spectra by uncorrected scintillations. The two-step retrieval method is used: the spectral inversion is followed by the vertical inversion. The spectral inversion relies on the removal of contributions from ozone, NO2, NO3 and Rayleigh scattering from the optical depth spectra, for each ray perigee altitude. In the vertical inversion, the profiles of aerosol extinction coefficients at several wavelengths are retrieved from the collection of slant aerosol optical depth profiles.
The retrieved aerosol extinction profiles (FMI-GOMOSaero dataset v1) are provided in the altitude range 10–40 km at wavelengths 400, 440, 452, 470, 500, 525, 550, 672 and 750 nm, for the whole GOMOS operating period from August 2002 to March 2012.
The retrieved aerosol extinction profiles show a realistic wavelength dependence. Intercomparisons have shown that FMI-GOMOSaero aerosol profiles are in good agreement with other datasets and have suggested a better data quality compared to the previous GOMOS aerosol data.
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RC1: 'Comment on amt-2023-179', Robert Damadeo, 08 Sep 2023
Summary
This paper details a new aerosol retrieval product from the GOMOS instrument. This new retrieval is available only as monthly zonal means because it is performed by averaging the Level 1 transmittances from the stellar occultations and then looking at the residual spectra to solve for aerosol using a spectral smoothing/fitting function. This methodology has its pros (less sensitivity to noise and scintillation) and cons (increased sensitivity to interference from clouds that cannot be filtered later). Comparisons with other instruments seem to suggest an overall improvement in the spectral dependence of the resulting aerosol extinction coefficient profiles from the current production version of GOMOS aerosol data. This paper is well-written and presented and suitable for publication in AMT. My comments below are mostly minor, though I believe there is a source of additional bias being introduced from this new methodology that should be addressed somewhere.
Comments:
I find it strange that one of the wavelengths from the residual spectra that is used for aerosol is one in which the instrument does not even measure (i.e., 750 nm). I understand the motivation is to match wavelengths measured by other instruments for validation purposes, but this wavelength is now essentially an extrapolation of the smoothing that is performed on the residual spectra. Additionally, why is it that the spectra shown in Fig. 2 do not cover the entire range of the IR spectrometer (thus further reducing the reliability of the smoothing out in this spectral region)?
Why not show the comparative Angstrom exponent in Figure 5 as is done for Figure 6?
Pg 10, Ln 210: “The reason for positive bias near the tropical tropopause is GOMOS aerosol retrievals is not fully understood at the moment.”
How is it not just clouds? If you are averaging all of the transmission profiles without filtering for clouds, then of course clouds are going to bias your aerosol retrievals near the tropopause. Perhaps you cannot easily quantify how much of the bias is from clouds versus any other potential source, but the expected presence of clouds will obviously create a bias like what is shown in Fig. 7. I think the better question is why does the bias appear significantly larger than the averaged SAGE II profiles in Fig. 4?
Pg 10, Ln 213: “We tried to apply various methods for cloud filtering in averaging GOMOS transmittances– according to absolute values of extinction and ratio at different wavelengths.”
This is no trivial task, and it may not be possible for all but the thickest clouds.
As a curiosity, I wondered what the impact of using averaged transmittances would be for your comparisons. The authors compute an average transmittance profile, then convert it to an average optical depth profile, then perform the retrieval. The comparison profiles from other instruments are averages of individual profiles. As a simple test, the following relationship is true: MEAN(-LOG(T_i)) > -LOG(MEAN(T_i)). In other words, averaging the transmittance profiles first will always result in a slight low bias to your optical depths when compared with the mean of the optical depths derived from individual profiles. I am unsure of how this propagates through the two inversion steps. I would imagine the spectral inversion is less affected by this, allowing the bias to mostly propagate into the residuals. I cannot intuit how this would propagate through the vertical inversion step. If the bias still propagates proportionally (as opposed to inversely in some fashion) into the resulting extinctions, it would mean the positive bias you see in your comparisons is actually smaller than the true comparisons because a small amount of negative bias should be introduced from averaging transmissions first.
Citation: https://doi.org/10.5194/amt-2023-179-RC1 - AC1: 'Reply on RC1', Viktoria Sofieva, 22 Dec 2023
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RC2: 'Comment on amt-2023-179', Anonymous Referee #2, 03 Nov 2023
The comment was uploaded in the form of a supplement: https://amt.copernicus.org/preprints/amt-2023-179/amt-2023-179-RC2-supplement.pdf
- AC2: 'Reply on RC2', Viktoria Sofieva, 22 Dec 2023
Status: closed
-
RC1: 'Comment on amt-2023-179', Robert Damadeo, 08 Sep 2023
Summary
This paper details a new aerosol retrieval product from the GOMOS instrument. This new retrieval is available only as monthly zonal means because it is performed by averaging the Level 1 transmittances from the stellar occultations and then looking at the residual spectra to solve for aerosol using a spectral smoothing/fitting function. This methodology has its pros (less sensitivity to noise and scintillation) and cons (increased sensitivity to interference from clouds that cannot be filtered later). Comparisons with other instruments seem to suggest an overall improvement in the spectral dependence of the resulting aerosol extinction coefficient profiles from the current production version of GOMOS aerosol data. This paper is well-written and presented and suitable for publication in AMT. My comments below are mostly minor, though I believe there is a source of additional bias being introduced from this new methodology that should be addressed somewhere.
Comments:
I find it strange that one of the wavelengths from the residual spectra that is used for aerosol is one in which the instrument does not even measure (i.e., 750 nm). I understand the motivation is to match wavelengths measured by other instruments for validation purposes, but this wavelength is now essentially an extrapolation of the smoothing that is performed on the residual spectra. Additionally, why is it that the spectra shown in Fig. 2 do not cover the entire range of the IR spectrometer (thus further reducing the reliability of the smoothing out in this spectral region)?
Why not show the comparative Angstrom exponent in Figure 5 as is done for Figure 6?
Pg 10, Ln 210: “The reason for positive bias near the tropical tropopause is GOMOS aerosol retrievals is not fully understood at the moment.”
How is it not just clouds? If you are averaging all of the transmission profiles without filtering for clouds, then of course clouds are going to bias your aerosol retrievals near the tropopause. Perhaps you cannot easily quantify how much of the bias is from clouds versus any other potential source, but the expected presence of clouds will obviously create a bias like what is shown in Fig. 7. I think the better question is why does the bias appear significantly larger than the averaged SAGE II profiles in Fig. 4?
Pg 10, Ln 213: “We tried to apply various methods for cloud filtering in averaging GOMOS transmittances– according to absolute values of extinction and ratio at different wavelengths.”
This is no trivial task, and it may not be possible for all but the thickest clouds.
As a curiosity, I wondered what the impact of using averaged transmittances would be for your comparisons. The authors compute an average transmittance profile, then convert it to an average optical depth profile, then perform the retrieval. The comparison profiles from other instruments are averages of individual profiles. As a simple test, the following relationship is true: MEAN(-LOG(T_i)) > -LOG(MEAN(T_i)). In other words, averaging the transmittance profiles first will always result in a slight low bias to your optical depths when compared with the mean of the optical depths derived from individual profiles. I am unsure of how this propagates through the two inversion steps. I would imagine the spectral inversion is less affected by this, allowing the bias to mostly propagate into the residuals. I cannot intuit how this would propagate through the vertical inversion step. If the bias still propagates proportionally (as opposed to inversely in some fashion) into the resulting extinctions, it would mean the positive bias you see in your comparisons is actually smaller than the true comparisons because a small amount of negative bias should be introduced from averaging transmissions first.
Citation: https://doi.org/10.5194/amt-2023-179-RC1 - AC1: 'Reply on RC1', Viktoria Sofieva, 22 Dec 2023
-
RC2: 'Comment on amt-2023-179', Anonymous Referee #2, 03 Nov 2023
The comment was uploaded in the form of a supplement: https://amt.copernicus.org/preprints/amt-2023-179/amt-2023-179-RC2-supplement.pdf
- AC2: 'Reply on RC2', Viktoria Sofieva, 22 Dec 2023
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