the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 10 May 2022
Polarization Performance Simulation for the GeoXO Atmospheric Composition Instrument: NO2 Retrieval Impacts
Abstract. NOAA’s Geostationary Extended Observations (GeoXO) constellation will continue and expand on the capabilities of the current generation of geostationary satellite systems to support US weather, ocean, atmosphere, and climate operations. It is planned to consist of a dedicated atmospheric composition instrument (ACX) to support air quality forecasting and monitoring by providing similar capabilities to missions such as TEMPO (Tropospheric Emission: Monitoring Pollution), currently planned to launch in 2023, and Ozone Monitoring Instrument (OMI), TROPOMI (TROPOspheric Monitoring Instrument), and GEMS (Geostationary Environment Monitoring Spectrometer) currently in operation. As the early phases of ACX development are progressing, design trade-offs are being considered to understand the relationship between instrument design choices and trace gas retrieval impacts. Some of these choices will affect the instrument polarization sensitivity (PS), which can have radiometric impacts on environmental satellite observations. We conducted a study to investigate how such radiometric impacts can affect NO2 retrievals by exploring their sensitivities to time of day, location, and scene type with an ACX instrument model that incorporates PS. The study addresses the basic steps of operational NO2 retrievals: the spectral fitting step and the conversion of slant column to vertical column via the air mass factor (AMF). The spectral fitting step was performed by generating at-sensor radiance from a clear sky scene with a known NO2 amount, the application of an instrument model including both instrument PS and noise, and a physical retrieval. The spectral fitting step was found to mitigate the impacts of instrument PS. The AMF-related step was considered for clear sky and partially cloudy scenes, where instrument PS can lead to errors in interpreting the cloud content, propagating to AMF errors and finally to NO2 retrieval errors. For this step, the NO2 retrieval impacts were small but non-negligible for high NO2 amounts; we estimated that a typical high NO2 amount can cause a maximum retrieval error of 0.25 x 1015 molecules/cm2 for a PS of 5 %. These simulation capabilities were designed to aid in the development of a GeoXO atmospheric composition instrument that will improve our ability to monitor and understand the Earth’s atmosphere.
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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
(2199 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-2022-207', Anonymous Referee #1, 30 May 2022
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AC1: 'Reply on RC1', Aaron Pearlman, 08 Jun 2022
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2022/egusphere-2022-207/egusphere-2022-207-AC1-supplement.pdf
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AC1: 'Reply on RC1', Aaron Pearlman, 08 Jun 2022
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RC2: 'Comment on egusphere-2022-207', Anonymous Referee #2, 31 May 2022
The authors present a closed-loop simulation study on the sensitivity of NO2 retrieval on instrument polarisation, for a future hyperspectral instrument with a small polarisation sensitivity of 5%.
This study is significant to give orientation in the definition/verification of instrument requirements for future missions.
The paper is generally well-written, but a few details are sloppy. There is a large amount of back-and-forth information, which sometimes makes it confusing for the reader, and this could be structured better.General comments:
- In section 2, several simulation methods are given, which include a multitude of parameters.
In section 3, the results of these simulations are presented. Here, the references to simulation parameters are given by (only) partly repeating these from section 2. At initial reading, this causes difficulty and a lot of back-and-forth reading in relating the results to the simulation parameters. And I still have to guess.
I strongly suggest to make this more structured, by clearly itemizing in section 2, like "simulation A: ...", "simulation B: ..."
and then referring to these cases in section 3 - It is shown in the paper that instrument polarisation sensitivity mainly affects the AMF retrieval, not the NO2 slant columns.
In this study, the polarisation sensitivity enters NO2 vertical column through the retrieval of cloud fraction.
But in operational retrievals, also the cloud altitude and(or) cloud optical thickness must be derived.
Especially for cloud retrieval that uses information from polarised radiance (e.g. rotational raman scattering or deep absorption bands of O2) it may be expected that instrument polarisation plays a role. It is understood that the cloud retrieval algorithm for an instrument in initial development is TBD and simulations are premature. Nevertheless this should be mentioned explicitly.
Specific comments:
- Figure 2: Surface spectra are shown here from ~400 to 3000+ nm. But the NO2 retrieval window is 420-455 nm and from these figures it is impossible to see any spectral structure there. Please reduce the spectral range of the figure and comment on the surface spectral resolution.
- lines 160-175: here the description becomes confusing/sloppy:
AMF in line 163 seems to be the height-dependent box-AMF which in Eq.(10) should be written as function of z.
What is the relation between AMF_tot in Eq.(7) and AMF_total in Eq.(10) ? Meant is probably that Eq.(7) in incorporated in Eq.(10). I suggest to write this out in Eq.(10).
In order to explain the alpha in Eq.(10) I suggest to say beforehand that the formulation follows Kuhlmann 2015.
In line 175, should not ∂AMF_tot be ∂AMF_total, and is its only dependence on PS through Eq.(8) ? Please rewrite to make that explicit. - section 2.2.2:
- were the same aerosol parameters as for clear sky used? or no aerosol at all?
- it is tacitly assumed that cloudy pixels have unpolarised radiance. Please mention/explain this explicitly. - line 208: "other retrieval techniques that do not use a spectral fitting approach" should be
"other retrieval techniques that do not use a polynomial correction term in the spectral fitting approach" - Figure 5b. Why is the standard deviation over Water so much smaller than over Land?
Usually the reflectances over water are smaller so S/N should be worse, unless you force S/N to be constant (as suggested in the text). Or is something else the case like an aerosol effect or a spectral surface effect? Please explain. - section 3.2 . Confusing: which simulation from section 2.2.2 was used to generate Figure 7 and which one for Figure 9 ?
See my general comment.
Figure 7 has fixed surface type thus seems to be the second simulation from section 2.2.2. Figure 9 uses GEOS-5 data thus also seems to be the second simulation ??
What means " water, rural, urban scene covers CONUS [...] for each surface? use for each grid cell the most abundant type?
What means "with a fixed scene type over the CONUS grid" ? use 1 type for all grid cells?
Please provide a bit less condensed description. - line 220: parenthesis typo in Zoogman reference
- Retrieval results for Figure 6: Is it correct that these simulations were done with a small (<0.04) cloud fraction? "Cloud radiance fraction" refers to the retrieved result (doesn't it ?). Please specify which cloud fraction was used in the forward simulation for this figure.
- Figure 8: data for NO2 amounts 0f 5.0E+15 and 8.6E+15 are difficult to read in this figure. Is the relative error ("percent error") approximately equal for all three NO2 amounts? Please adjust figure or mention in the text.
- Retrieval results for Figure 9: are these retrievals with "fixed scene type" as suggested for the scenario with GEOS profiles in line 181? That would not be very realistic. If water is used for the extreme East/West (Atlantic/Pacific) why are the errors so much smaller than in fig 7c? Your text says "The higher cloud fraction decrease the retrieval errors" but also clear scenes at high solar zenith angle have much smaller errors. Is this because of the NO2 amounts? It would be useful to show a figure with NO2 input column.
Citation: https://doi.org/10.5194/egusphere-2022-207-RC2 -
AC2: 'Reply on RC2', Aaron Pearlman, 08 Jun 2022
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2022/egusphere-2022-207/egusphere-2022-207-AC2-supplement.pdf
-
AC3: 'Reply on RC2', Aaron Pearlman, 20 Jun 2022
The referee is correct that particularly rotational Raman scattering can be significantly affected by polarization. Although in our previous comment, we partially addressed this in a general way by adding:
"Our simplified retrieval approach may have neglected factors used in operational retrievals that could be affected by instrument PS and contribute to additional retrieval errors related to estimates of aerosols, surface reflectance, and cloud parameters."
We now also include the following to be more explicit: "Rotational Raman scattering, which has been used in cloud height retrievals (e.g., Vasilkov et al., 2008), can be particularly sensitive to polarization. Other approaches for cloud height retrievals such as oxygen dimer absorption (Acarreta et al., 2003) should be much less sensitive. We do not account for the PS to cloud height retrievals. The PS to cloud optical thickness is implicitly accounted for within the effective cloud fraction estimation."
Citation: https://doi.org/10.5194/egusphere-2022-207-AC3
- In section 2, several simulation methods are given, which include a multitude of parameters.
Interactive discussion
Status: closed
-
RC1: 'Comment on egusphere-2022-207', Anonymous Referee #1, 30 May 2022
-
AC1: 'Reply on RC1', Aaron Pearlman, 08 Jun 2022
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2022/egusphere-2022-207/egusphere-2022-207-AC1-supplement.pdf
-
AC1: 'Reply on RC1', Aaron Pearlman, 08 Jun 2022
-
RC2: 'Comment on egusphere-2022-207', Anonymous Referee #2, 31 May 2022
The authors present a closed-loop simulation study on the sensitivity of NO2 retrieval on instrument polarisation, for a future hyperspectral instrument with a small polarisation sensitivity of 5%.
This study is significant to give orientation in the definition/verification of instrument requirements for future missions.
The paper is generally well-written, but a few details are sloppy. There is a large amount of back-and-forth information, which sometimes makes it confusing for the reader, and this could be structured better.General comments:
- In section 2, several simulation methods are given, which include a multitude of parameters.
In section 3, the results of these simulations are presented. Here, the references to simulation parameters are given by (only) partly repeating these from section 2. At initial reading, this causes difficulty and a lot of back-and-forth reading in relating the results to the simulation parameters. And I still have to guess.
I strongly suggest to make this more structured, by clearly itemizing in section 2, like "simulation A: ...", "simulation B: ..."
and then referring to these cases in section 3 - It is shown in the paper that instrument polarisation sensitivity mainly affects the AMF retrieval, not the NO2 slant columns.
In this study, the polarisation sensitivity enters NO2 vertical column through the retrieval of cloud fraction.
But in operational retrievals, also the cloud altitude and(or) cloud optical thickness must be derived.
Especially for cloud retrieval that uses information from polarised radiance (e.g. rotational raman scattering or deep absorption bands of O2) it may be expected that instrument polarisation plays a role. It is understood that the cloud retrieval algorithm for an instrument in initial development is TBD and simulations are premature. Nevertheless this should be mentioned explicitly.
Specific comments:
- Figure 2: Surface spectra are shown here from ~400 to 3000+ nm. But the NO2 retrieval window is 420-455 nm and from these figures it is impossible to see any spectral structure there. Please reduce the spectral range of the figure and comment on the surface spectral resolution.
- lines 160-175: here the description becomes confusing/sloppy:
AMF in line 163 seems to be the height-dependent box-AMF which in Eq.(10) should be written as function of z.
What is the relation between AMF_tot in Eq.(7) and AMF_total in Eq.(10) ? Meant is probably that Eq.(7) in incorporated in Eq.(10). I suggest to write this out in Eq.(10).
In order to explain the alpha in Eq.(10) I suggest to say beforehand that the formulation follows Kuhlmann 2015.
In line 175, should not ∂AMF_tot be ∂AMF_total, and is its only dependence on PS through Eq.(8) ? Please rewrite to make that explicit. - section 2.2.2:
- were the same aerosol parameters as for clear sky used? or no aerosol at all?
- it is tacitly assumed that cloudy pixels have unpolarised radiance. Please mention/explain this explicitly. - line 208: "other retrieval techniques that do not use a spectral fitting approach" should be
"other retrieval techniques that do not use a polynomial correction term in the spectral fitting approach" - Figure 5b. Why is the standard deviation over Water so much smaller than over Land?
Usually the reflectances over water are smaller so S/N should be worse, unless you force S/N to be constant (as suggested in the text). Or is something else the case like an aerosol effect or a spectral surface effect? Please explain. - section 3.2 . Confusing: which simulation from section 2.2.2 was used to generate Figure 7 and which one for Figure 9 ?
See my general comment.
Figure 7 has fixed surface type thus seems to be the second simulation from section 2.2.2. Figure 9 uses GEOS-5 data thus also seems to be the second simulation ??
What means " water, rural, urban scene covers CONUS [...] for each surface? use for each grid cell the most abundant type?
What means "with a fixed scene type over the CONUS grid" ? use 1 type for all grid cells?
Please provide a bit less condensed description. - line 220: parenthesis typo in Zoogman reference
- Retrieval results for Figure 6: Is it correct that these simulations were done with a small (<0.04) cloud fraction? "Cloud radiance fraction" refers to the retrieved result (doesn't it ?). Please specify which cloud fraction was used in the forward simulation for this figure.
- Figure 8: data for NO2 amounts 0f 5.0E+15 and 8.6E+15 are difficult to read in this figure. Is the relative error ("percent error") approximately equal for all three NO2 amounts? Please adjust figure or mention in the text.
- Retrieval results for Figure 9: are these retrievals with "fixed scene type" as suggested for the scenario with GEOS profiles in line 181? That would not be very realistic. If water is used for the extreme East/West (Atlantic/Pacific) why are the errors so much smaller than in fig 7c? Your text says "The higher cloud fraction decrease the retrieval errors" but also clear scenes at high solar zenith angle have much smaller errors. Is this because of the NO2 amounts? It would be useful to show a figure with NO2 input column.
Citation: https://doi.org/10.5194/egusphere-2022-207-RC2 -
AC2: 'Reply on RC2', Aaron Pearlman, 08 Jun 2022
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2022/egusphere-2022-207/egusphere-2022-207-AC2-supplement.pdf
-
AC3: 'Reply on RC2', Aaron Pearlman, 20 Jun 2022
The referee is correct that particularly rotational Raman scattering can be significantly affected by polarization. Although in our previous comment, we partially addressed this in a general way by adding:
"Our simplified retrieval approach may have neglected factors used in operational retrievals that could be affected by instrument PS and contribute to additional retrieval errors related to estimates of aerosols, surface reflectance, and cloud parameters."
We now also include the following to be more explicit: "Rotational Raman scattering, which has been used in cloud height retrievals (e.g., Vasilkov et al., 2008), can be particularly sensitive to polarization. Other approaches for cloud height retrievals such as oxygen dimer absorption (Acarreta et al., 2003) should be much less sensitive. We do not account for the PS to cloud height retrievals. The PS to cloud optical thickness is implicitly accounted for within the effective cloud fraction estimation."
Citation: https://doi.org/10.5194/egusphere-2022-207-AC3
- In section 2, several simulation methods are given, which include a multitude of parameters.
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Aaron Pearlman
Monica Cook
Boryana Efremova
Francis Padula
Lok Lamsal
Joel McCorkel
Joanna Joiner
The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
- Preprint
(2199 KB) - Metadata XML