the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Dust transport and horizontal fluxes measurement with spaceborne lidars ALADIN, CALIOP and model reanalysis data
Abstract. In this paper, a long-term large-scale Sahara dust transport event occurred during 14 June and 27 June 2020 is tracked with the spaceborne lidars ALADIN and CALIOP observations and the models ECMWF and HYSPLIT analysis. We evaluate the performance of the ALADIN and CALIOP on the observations of dust optical properties and wind fields and explore the capability in tracking the dust events and in calculating the dust horizontal mass fluxes with the combination of measurement data from ALADIN and CALIOP coupled with the products from ECMWF and HYSPLIT. Compared with the traditional assessments based on the data from CALIOP and models, the complement of Aeolus-produced aerosol optical properties and wind data will significantly improve the accuracy of dust horizontal flux estimations. The dust plumes are identified with AIRS/Aqua Dust Score Index and with the Vertical Feature Mask products from CALIPSO. The emission, dispersion, transport and deposition of the dust event are monitored using the data from HYSPLIT, CALIPSO and AIRS/Aqua. With the quasi-synchronization observations by ALADIN and CALIOP, combining the wind vectors and relative humidity, the dust horizontal fluxes are calculated. From this study, it is found that the dust event generated on 14 and 15 June 2020 from Sahara Desert in North Africa, and then dispersed and transported westward over the Atlantic Ocean, and finally deposited in the Atlantic Ocean, the Americas and the Caribbean Sea. During the transport and deposition processes, the dust plumes are trapped in the Northeasterly Trade-wind zone between the latitudes of 5° N and 30° N and altitudes of 0 km and 6 km (in this paper we name this space area as “Saharan dust eastward transport tunnel”). From the measurement results on 19 June 2020, influenced by the hygroscopic effect and mixing with other types aerosols, the backscatter coefficients of dust plumes are increasing along the transport routes, with 3.88 × 10−6 ± 2.59 × 10−6 m−1 sr−1 in “dust portion during emission phase”, 7.09 × 10−6 ± 3.34 × 10−6 m−1 sr−1 in “dust portion during development phase” and 7.76 × 10−6 ± 3.74 × 10−6 m−1 sr−1 in “dust portion during deposition phase”. Finally, the horizontal fluxes at different dust parts and heights on 19 June and on entire transport routine during transportation are computed. On 19 June, the dust horizontal fluxes are about 2.17 ± 1.83 mg m−2 s−1 in dust portion during emission phase, 2.72 ± 1.89 mg m−2 s−1 in dust portion during development phase and 3.01 ± 2.77 mg m−2 s−1 in dust portion during deposition phase. In the whole life-time of the dust event, the dust horizontal fluxes are about 1.30 ± 1.07 mg m−2 s−1 on 15 June 2020, 2.62 ± 1.88 mg m−2 s−1 on 16 June 2020, 2.72 ± 1.89 mg m−2 s−1 on 19 June 2020, 1.98 ± 1.41 mg m−2 s−1 on 24 June 2020 and 2.11 ± 1.74 mg m−2 s−1 on 27 June 2020. From this study, it is found that the minimum of the fluxes appears when the dust event is initially generated on 15 June. During the dust development stage, the horizontal fluxes gradually increase and reach to the maximum value on 19 June with the enhancement of the dust event. Then, the horizontal fluxes gradually decrease since most of the dust deposited in the Atlantic Ocean, the Americas and the Caribbean Sea. Combining the Chlorophyll concentrations data provided by MODIS-Aqua, the Saharan Dust is found transported across the oligotrophic regions Atlantic Ocean towards the Americas and Caribbean Sea, which are also oligotrophic regions. The mineral dust delivers micronutrients including soluble Fe and P to the deposition zones and has the potential to fertilizing the ocean and increase the primary productivity in the Atlantic Ocean and Caribbean Sea.
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RC1: 'Comment on acp-2021-219', Thomas FLAMENT, 07 May 2021
Overall comments:
The paper present a large event of dust transport originating from the Sahara and croissing the tropical Atlantic ocean westward, until it reaches Central America and the Carribean sea.
This event was large in the amount of dust transported and was reported in newspapers (e.g. https://www.nytimes.com/2020/06/22/science/saharan-dust-plume.html). Such a large plume is an easy target for Aeolus detection of aerosols and is one of the first uses of this High Spectral Resolution Lidar optical properties product. For both these aspects, this paper makes an interesting contribution to the observation of the atmosphere and is relevant to the journal scope.
The structure of the paper is clear. The syntax and grammar are understandable but will require an extensive work to make it easier to read.
The event is large enough that the transported dust is visible without a sophisticated analysis. However, the method used to match CALIPSO and Aeolus data, to estimate the mass flux and the associated errors needs to be described in more details. Many mathematical details are missing to provide a complete understanding of the work. The flow charts give a good overview of the procedure but are not enough.
We need a reference about the L2A processor. I would suggest Flamant et al. (2008, https://doi.org/10.1111/j.1600-0870.2007.00287.x). Alternatively, we, the L2A development team, are currently preparing about the current L2A product which will be submitted soon to the Atmospheric Measurement Techniques journal. It will present up to date information about the product.
Also, the Aeolus L2A dataset was not widely used yet: more information needs to be provided about the content and overall data quality of the product. Additionally, a discussion of the quality controls applied by the authors would be welcome.
On a technical aspect of data analysis: we recommend to use the "mid bin" product of the SCA. The authors do not mention the existence of two products in the SCA. This is especially important when looking at extinction coefficients, which are more sensitive to noise and better retrieved through this averaged version of the algorithm.
As few papers are available on the subject, this is a technical detail that needs to be mentioned. See also Baars et al. (2021, https://doi.org/10.1029/2020GL092194, “As for the extinction, the SCA mid bin algorithm is to be preferred against the SCA for the lidar ratio analysis.”).
Detailed line by line comments:
l. 145-147: It is right to use the SCA rather than the ICA (this is what we recommend as developers of the product).
However, as said above, the SCA provides two sets of extinction and backscatter coefficients. If the extinction is needed, I recommend using the “mid bin” solution. If this was not the case in the current analysis, this might change results significantly. Known defects of the SCA algorithm will be discussed in the future paper we are preparing. In the mean time, I am available to discuss this directly if necessary.
l. 152: “Since the footprints of Aeolus and CALIPSO are not exactly matched, the missing wind data between their tracks have to be filled in using the ERA5 wind field data”. This sentence is very unclear. Does that mean that you use ERA5 winds at the location of the CALIPSO track? This whole “Methodology” section needs to be rewritten with much more detail.
l.153: “and the measurement uncertainty is on the order of 20%.” Which measurement uncertainty is discussed here? Aeolus estimated errors are often larger than 20 %.
l. 160:
- References are provided regarding the determination of aerosol mass (namely, Müller et al. 1999) but a discussion about the method is needed. For instance, Müller et al. say the algorithm can cope with errors of ~20% on the lidar data. Is this verified here?- The inversion method requires information at three different wavelength. What is the procedure to match the CALIPSO and Aeolus profiles? (e.g. advection of CALIPSO profiles towards the Aeolus profile using ERA5 winds?)
As the time and space matching of the two observations cannot be perfect, how is the mismatch propagated into the error estimate?The combination of the two satellites observation is really interesting but the method really needs to be described precisely enough so that the results can be reproduced.
l. 161: The authors reject data where meteorological data has more than 90 % of relative humidity. I suppose this is the method they chose for cloud screening but this needs to be stated explicitly. As said in the general comments, more information on data quality control and selection needs to be provided, e.g. Do you integrate all of the particles in a given cross-section? Could you discuss contamination by particles other than Saharan dust? (clouds, marine aerosols ...)
l. 163: The mass flux is said to be derived by eddy covariance, but it is not clear to me why we would consider turbulent transport and how this would be done. Is the mass flux derived as the integral of m.v for each pixel? Or is it " m'. v' " as stated? If it's actually the second option, how are m' and v' derived?
As a second point, does this requires an interpolation of m and/or v on a common pixel grid? How is this done?
Fig. 4:
- Could you show the lidar profile from which the location of the stars is chosen?- Using squares triangles and circles separately for each starting point would allow you to pack more information into this figure (e.g. northernmost point is associated with triangles, middle point with squares etc.).
l.192: “deposition” is ambiguous. The HySPLIT data shows a downdraft of the air mass. The dust could also settle while the air mass has no vertical movement.
Fig. 5:
- Why show 4 lidar profiles but only exploit 3 of them? If it’s not necessary, removing the first one would make the figure easier to read. Overall the 3D figures are pretty but contain too much information to be fully readable.- On panel (a), could you label the date and time of each satellite overpass?
l. 205-206: The origin of these numbers and of the associated error needs to be detailed (cf comment on l. 160).
l. 225: same as l. 161, I suppose this is a method for rejecting cloud contaminated pixels. It would be better if this was stated explicitly.
l. 233-234: how can the dust concentration be highest in the cross-section 3, farthest from the source. I would expect dispersion of aerosols rather than concentration. Could this be justified?
Fig. 7 and Fig. 10: Estimations of mass flux are provided with an error bar but it is nowhere described how these error bars are derived. We need some proper description of this.
Fig. 8: There is too much on this figure. It is hard to read. You need to simplify the presentation.
l. 276-284: It is no obvious from Fig. 11 that the low chlorophyll concentration is due to a lack of iron. More references would be needed to support this claim.
Conclusion: The L2A data was not available publicly at the time the article was submitted. Authors could state that they were allowed to access the data through their participation as a Calibration and Validation team.
Citation: https://doi.org/10.5194/acp-2021-219-RC1 -
AC1: 'Reply on RC1', Guangyao Dai, 21 Jul 2021
Dear Prof. Thomas Flament,
Thank you for reviewing our manuscript. We greatly appreciate the substantial amount of time and effort that you dedicated to this review process.
Many thanks for your valueable suggestions in the usage of the Aeolus L2A data. We have revised the manuscript according to your comments and the response is presented as supplement/attachment.
Many thanks and best regards.
Guangyao Dai and Songhua Wu
On behalf of the co-authors
-
AC1: 'Reply on RC1', Guangyao Dai, 21 Jul 2021
-
CC1: 'Comment on acp-2021-219', Dimitri Trapon, 10 May 2021
Section 2.1 ALADIN/Aeolus:
Indicate which L2A baseline (i.e. baseline 10 or 11 referring to the L2Ap v3.10 or v3.11) has been used for processing might be useful for traceability (e.g. new radiometric correction being included in v3.11 using telescope temperatures oscillations).Section 4.1 Measurement case with CALIOP, ALADIN, ECMWF and HYSPLIT:
The differcences between Aeolus/ALADIN and CALIPSO/CALIOP instrumentation principle and geometry could be highlighted (i.e. ALADIN poiting 35° offset from nadir with the ground) as the time gap between acquisitions (e.g. for intercompaison cross-section 3 on June 19, 2020 showed in Figure 5 Aeolus hovered the West of Cape Verde from 08:00 to 08:30 UTC four hours later than CALIOP from 04:07 to 04/20 UTC). It is fair to assume that the particles distribution within the plume might have evolved during the time offset, hence a limit of the data intercomparison.Dimitri Trapon – L2A development team within Meteo-France CNRM/GMEI
Citation: https://doi.org/10.5194/acp-2021-219-CC1 -
AC2: 'Reply on CC1', Guangyao Dai, 21 Jul 2021
Dear Prof. Dimitri Trapon,
Thank you for your comments on our manuscript. We greatly appreciate the substantial amount of time and effort that you dedicated to this review process.
We have revised the manuscript according to your comments and the response is presented as supplement/attachment.
Many thanks and best regards.
Guangyao Dai and Songhua Wu
On behalf of the co-authors
-
AC2: 'Reply on CC1', Guangyao Dai, 21 Jul 2021
-
RC2: 'Comment on acp-2021-219', Anonymous Referee #2, 09 Jun 2021
In the present manuscript, Guangyao Dai and coauthors track a Saharan dust plume across the Atlantic Ocean and calculate the dust horizontal fluxes. The novel approach in their manuscript is the combination of two satellites (CALIPSO and Aeolus) measuring at different wavelengths. To bridge the gap between the overpasses of the two satellites ERA5 model reanalysis and HYSPLIT trajectories are used. However, the satellite data are not treated in a correct manner with the result that the whole proposed method is not valid. Therefore, I have to reject the manuscript.
The following points underline my decision and may help the authors to improve their work:
- Aeolus is providing the circular co-polarized component of the backscatter and not the total backscatter coefficient. The missing cross-polarized component is not negligible in dust cases as used in the manuscript. You are missing a significant part of the backscatter coefficient at 355 nm.
- CALIPSO measures the backscatter coefficient at 532 and 1064 nm, but not the extinction. The extinction provided by CALIPSO is retrieved by multiplying the backscatter coefficient with the aerosol-type-dependent lidar ratio. Therefore, the extinction coefficient is not an independent quantity. For your inversion calculation, you need independent measurements of the extinction coefficient, either by high spectral resolution (HSRL) or Raman lidar measurements.
- Following point 1 and 2, the main part of your data procedure, the calculation of the dust volume concentration is not correct. It can not be done in the presented manner. This is not an easy point to correct and leads to my decision to reject the paper.
- The horizontal flux is not well defined. The horizontal velocity is a vector with two components (East-West, North-South), so the horizontal flux should have a direction. If you just take the absolute value of the velocity, your flux may have different directions at every point. What does this help us in understanding the dust transport?
- Your result, that the minimum flux occurs at dust emission (line 271 and 322) is misleading. Why should the flux be lowest at emission? Looking at your back trajectories (Fig. 8a) indicates that a significant amount of dust originated from regions west of the track on 15 June. This dust is not observed on 15 June, but on 16 June leading to a greater horizontal flux.
- The combination of the two satellites is a great new idea. However, you should highlight the scientific question behind. You speak about ocean fertilization, but it remains open, which amount of dust is deposited to the Ocean. With Fig. 11, you show the low chlorophyll concentration in the studied area, but you do not quantify the effect of the discussed dust event on the ocean fertility. Your description remains very general stating that dust add nutrients to the Ocean.
Citation: https://doi.org/10.5194/acp-2021-219-RC2 -
AC3: 'Reply on RC2', Guangyao Dai, 21 Jul 2021
Dear Reviewer,
Thank you for handling and reviewing our manuscript. We greatly appreciate the substantial amount of time and effort that you dedicated to this review process.
We have revised the manuscript according to your comments and the response is presented as supplement.
Many thanks and best regards.
Guangyao Dai and Songhua Wu
On behalf of the co-authors
Status: closed
-
RC1: 'Comment on acp-2021-219', Thomas FLAMENT, 07 May 2021
Overall comments:
The paper present a large event of dust transport originating from the Sahara and croissing the tropical Atlantic ocean westward, until it reaches Central America and the Carribean sea.
This event was large in the amount of dust transported and was reported in newspapers (e.g. https://www.nytimes.com/2020/06/22/science/saharan-dust-plume.html). Such a large plume is an easy target for Aeolus detection of aerosols and is one of the first uses of this High Spectral Resolution Lidar optical properties product. For both these aspects, this paper makes an interesting contribution to the observation of the atmosphere and is relevant to the journal scope.
The structure of the paper is clear. The syntax and grammar are understandable but will require an extensive work to make it easier to read.
The event is large enough that the transported dust is visible without a sophisticated analysis. However, the method used to match CALIPSO and Aeolus data, to estimate the mass flux and the associated errors needs to be described in more details. Many mathematical details are missing to provide a complete understanding of the work. The flow charts give a good overview of the procedure but are not enough.
We need a reference about the L2A processor. I would suggest Flamant et al. (2008, https://doi.org/10.1111/j.1600-0870.2007.00287.x). Alternatively, we, the L2A development team, are currently preparing about the current L2A product which will be submitted soon to the Atmospheric Measurement Techniques journal. It will present up to date information about the product.
Also, the Aeolus L2A dataset was not widely used yet: more information needs to be provided about the content and overall data quality of the product. Additionally, a discussion of the quality controls applied by the authors would be welcome.
On a technical aspect of data analysis: we recommend to use the "mid bin" product of the SCA. The authors do not mention the existence of two products in the SCA. This is especially important when looking at extinction coefficients, which are more sensitive to noise and better retrieved through this averaged version of the algorithm.
As few papers are available on the subject, this is a technical detail that needs to be mentioned. See also Baars et al. (2021, https://doi.org/10.1029/2020GL092194, “As for the extinction, the SCA mid bin algorithm is to be preferred against the SCA for the lidar ratio analysis.”).
Detailed line by line comments:
l. 145-147: It is right to use the SCA rather than the ICA (this is what we recommend as developers of the product).
However, as said above, the SCA provides two sets of extinction and backscatter coefficients. If the extinction is needed, I recommend using the “mid bin” solution. If this was not the case in the current analysis, this might change results significantly. Known defects of the SCA algorithm will be discussed in the future paper we are preparing. In the mean time, I am available to discuss this directly if necessary.
l. 152: “Since the footprints of Aeolus and CALIPSO are not exactly matched, the missing wind data between their tracks have to be filled in using the ERA5 wind field data”. This sentence is very unclear. Does that mean that you use ERA5 winds at the location of the CALIPSO track? This whole “Methodology” section needs to be rewritten with much more detail.
l.153: “and the measurement uncertainty is on the order of 20%.” Which measurement uncertainty is discussed here? Aeolus estimated errors are often larger than 20 %.
l. 160:
- References are provided regarding the determination of aerosol mass (namely, Müller et al. 1999) but a discussion about the method is needed. For instance, Müller et al. say the algorithm can cope with errors of ~20% on the lidar data. Is this verified here?- The inversion method requires information at three different wavelength. What is the procedure to match the CALIPSO and Aeolus profiles? (e.g. advection of CALIPSO profiles towards the Aeolus profile using ERA5 winds?)
As the time and space matching of the two observations cannot be perfect, how is the mismatch propagated into the error estimate?The combination of the two satellites observation is really interesting but the method really needs to be described precisely enough so that the results can be reproduced.
l. 161: The authors reject data where meteorological data has more than 90 % of relative humidity. I suppose this is the method they chose for cloud screening but this needs to be stated explicitly. As said in the general comments, more information on data quality control and selection needs to be provided, e.g. Do you integrate all of the particles in a given cross-section? Could you discuss contamination by particles other than Saharan dust? (clouds, marine aerosols ...)
l. 163: The mass flux is said to be derived by eddy covariance, but it is not clear to me why we would consider turbulent transport and how this would be done. Is the mass flux derived as the integral of m.v for each pixel? Or is it " m'. v' " as stated? If it's actually the second option, how are m' and v' derived?
As a second point, does this requires an interpolation of m and/or v on a common pixel grid? How is this done?
Fig. 4:
- Could you show the lidar profile from which the location of the stars is chosen?- Using squares triangles and circles separately for each starting point would allow you to pack more information into this figure (e.g. northernmost point is associated with triangles, middle point with squares etc.).
l.192: “deposition” is ambiguous. The HySPLIT data shows a downdraft of the air mass. The dust could also settle while the air mass has no vertical movement.
Fig. 5:
- Why show 4 lidar profiles but only exploit 3 of them? If it’s not necessary, removing the first one would make the figure easier to read. Overall the 3D figures are pretty but contain too much information to be fully readable.- On panel (a), could you label the date and time of each satellite overpass?
l. 205-206: The origin of these numbers and of the associated error needs to be detailed (cf comment on l. 160).
l. 225: same as l. 161, I suppose this is a method for rejecting cloud contaminated pixels. It would be better if this was stated explicitly.
l. 233-234: how can the dust concentration be highest in the cross-section 3, farthest from the source. I would expect dispersion of aerosols rather than concentration. Could this be justified?
Fig. 7 and Fig. 10: Estimations of mass flux are provided with an error bar but it is nowhere described how these error bars are derived. We need some proper description of this.
Fig. 8: There is too much on this figure. It is hard to read. You need to simplify the presentation.
l. 276-284: It is no obvious from Fig. 11 that the low chlorophyll concentration is due to a lack of iron. More references would be needed to support this claim.
Conclusion: The L2A data was not available publicly at the time the article was submitted. Authors could state that they were allowed to access the data through their participation as a Calibration and Validation team.
Citation: https://doi.org/10.5194/acp-2021-219-RC1 -
AC1: 'Reply on RC1', Guangyao Dai, 21 Jul 2021
Dear Prof. Thomas Flament,
Thank you for reviewing our manuscript. We greatly appreciate the substantial amount of time and effort that you dedicated to this review process.
Many thanks for your valueable suggestions in the usage of the Aeolus L2A data. We have revised the manuscript according to your comments and the response is presented as supplement/attachment.
Many thanks and best regards.
Guangyao Dai and Songhua Wu
On behalf of the co-authors
-
AC1: 'Reply on RC1', Guangyao Dai, 21 Jul 2021
-
CC1: 'Comment on acp-2021-219', Dimitri Trapon, 10 May 2021
Section 2.1 ALADIN/Aeolus:
Indicate which L2A baseline (i.e. baseline 10 or 11 referring to the L2Ap v3.10 or v3.11) has been used for processing might be useful for traceability (e.g. new radiometric correction being included in v3.11 using telescope temperatures oscillations).Section 4.1 Measurement case with CALIOP, ALADIN, ECMWF and HYSPLIT:
The differcences between Aeolus/ALADIN and CALIPSO/CALIOP instrumentation principle and geometry could be highlighted (i.e. ALADIN poiting 35° offset from nadir with the ground) as the time gap between acquisitions (e.g. for intercompaison cross-section 3 on June 19, 2020 showed in Figure 5 Aeolus hovered the West of Cape Verde from 08:00 to 08:30 UTC four hours later than CALIOP from 04:07 to 04/20 UTC). It is fair to assume that the particles distribution within the plume might have evolved during the time offset, hence a limit of the data intercomparison.Dimitri Trapon – L2A development team within Meteo-France CNRM/GMEI
Citation: https://doi.org/10.5194/acp-2021-219-CC1 -
AC2: 'Reply on CC1', Guangyao Dai, 21 Jul 2021
Dear Prof. Dimitri Trapon,
Thank you for your comments on our manuscript. We greatly appreciate the substantial amount of time and effort that you dedicated to this review process.
We have revised the manuscript according to your comments and the response is presented as supplement/attachment.
Many thanks and best regards.
Guangyao Dai and Songhua Wu
On behalf of the co-authors
-
AC2: 'Reply on CC1', Guangyao Dai, 21 Jul 2021
-
RC2: 'Comment on acp-2021-219', Anonymous Referee #2, 09 Jun 2021
In the present manuscript, Guangyao Dai and coauthors track a Saharan dust plume across the Atlantic Ocean and calculate the dust horizontal fluxes. The novel approach in their manuscript is the combination of two satellites (CALIPSO and Aeolus) measuring at different wavelengths. To bridge the gap between the overpasses of the two satellites ERA5 model reanalysis and HYSPLIT trajectories are used. However, the satellite data are not treated in a correct manner with the result that the whole proposed method is not valid. Therefore, I have to reject the manuscript.
The following points underline my decision and may help the authors to improve their work:
- Aeolus is providing the circular co-polarized component of the backscatter and not the total backscatter coefficient. The missing cross-polarized component is not negligible in dust cases as used in the manuscript. You are missing a significant part of the backscatter coefficient at 355 nm.
- CALIPSO measures the backscatter coefficient at 532 and 1064 nm, but not the extinction. The extinction provided by CALIPSO is retrieved by multiplying the backscatter coefficient with the aerosol-type-dependent lidar ratio. Therefore, the extinction coefficient is not an independent quantity. For your inversion calculation, you need independent measurements of the extinction coefficient, either by high spectral resolution (HSRL) or Raman lidar measurements.
- Following point 1 and 2, the main part of your data procedure, the calculation of the dust volume concentration is not correct. It can not be done in the presented manner. This is not an easy point to correct and leads to my decision to reject the paper.
- The horizontal flux is not well defined. The horizontal velocity is a vector with two components (East-West, North-South), so the horizontal flux should have a direction. If you just take the absolute value of the velocity, your flux may have different directions at every point. What does this help us in understanding the dust transport?
- Your result, that the minimum flux occurs at dust emission (line 271 and 322) is misleading. Why should the flux be lowest at emission? Looking at your back trajectories (Fig. 8a) indicates that a significant amount of dust originated from regions west of the track on 15 June. This dust is not observed on 15 June, but on 16 June leading to a greater horizontal flux.
- The combination of the two satellites is a great new idea. However, you should highlight the scientific question behind. You speak about ocean fertilization, but it remains open, which amount of dust is deposited to the Ocean. With Fig. 11, you show the low chlorophyll concentration in the studied area, but you do not quantify the effect of the discussed dust event on the ocean fertility. Your description remains very general stating that dust add nutrients to the Ocean.
Citation: https://doi.org/10.5194/acp-2021-219-RC2 -
AC3: 'Reply on RC2', Guangyao Dai, 21 Jul 2021
Dear Reviewer,
Thank you for handling and reviewing our manuscript. We greatly appreciate the substantial amount of time and effort that you dedicated to this review process.
We have revised the manuscript according to your comments and the response is presented as supplement.
Many thanks and best regards.
Guangyao Dai and Songhua Wu
On behalf of the co-authors
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