the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 25 Oct 2024
A Better Understanding of an Extremely High Ozone Episode with Ensemble Simulation
Abstract. Severe ozone pollutions may occur in the Great Bay Area (GBA) when typhoons approach South China. However, numerical models often fail to capture the high ozone concentrations during the episodes, leading to uncertainties in understanding their formation mechanisms. This study conducted an ensemble simulation with 30 members (EMs) using the WRF-Chem model, coupled with a self-developed ozone source apportionment method, to analyze an extremely high ozone episode associated with Typhoon NIDA in the summer of 2016. The newly proposed index effectively distinguished between well-performing (good) and poorly performing (bad) EMs. Compared to the bad EMs, the good EMs accurately reproduced surface ozone variations, particularly capturing the extremely high concentrations observed in the afternoon of July 31. The formation of such high ozone levels was attributed to the retention of ozone in the residual layer at night and the enhanced photochemistry during daytime. As Typhoon NIDA approached, weak winds confined large amounts of ozone in the residual layer at night. The development of planetary boundary layer (PBL) facilitated the downward transport of ozone aloft, contributing to the rapid increase in surface ozone in the following morning. The enhanced photochemistry was primarily driven by increased ozone precursors resulting from favorable accumulation conditions and enhanced biogenic emissions. During the period of high ozone concentrations, contributions from local and surrounding regions increased. Additionally, ozone from southeastern Asia could transport to the GBA at high altitudes and then contribute to surface ozone when the PBL developed.
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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
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Supplement
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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
(7954 KB) - Metadata XML
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Supplement
(686 KB) - BibTeX
- EndNote
- Final revised paper
Journal article(s) based on this preprint
Interactive discussion
Status: closed
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RC1: 'Comment on egusphere-2024-3070', Anonymous Referee #1, 22 Nov 2024
The present article aims to investigate the physical and chemical drivers of extreme levels of Ozone in the Great Bay area of South China during the typhoon approach. The article shows the results of an intercomparison work of numerical simulations and the application of a source apportionment method to analyse the causes of extreme events and ozone high levels.
The content of the present manuscript is of interest to the scientific community both for the occurrence, more frequency of extreme weather events and their link with air pollution, and for the evaluation of the performance of CTM such as WRF-Chem in reproducing these events.
From this point of view, the manuscript has certain relevance in the field and for the journal. On the other hand, I found the manuscript need more work to improve the clarity of the facts exposed and a better choice of images to show.
One of the starting questions reading the manuscript, is relative to the configuration of the model, the spatial resolution, and the inputs used for the weather and emissions. These elements should be mentioned in the text and in a table to let other scientists replicate the experiment in the future.Secondly, the validation of the model performance should be more evidently quantitative. I appreciate the tentative to make the evaluation easy to understand by keywords (good/bad) and colours (red/blue) but it would be also good to know the real numbers behind the model performance. How is the best bias? How’s the worst? These are numbers that in the most important intercomparison exercises are provided.
Said that I found interesting and intuitive the use of the Index linking MNB and R to show the model performance and I believe it’s a kind of combined metric that would find replication in other works.
Finally, the images chosen for the manuscript are too small and too many. I suggest selecting those that need to stay in the main text and making them bigger in order to allow the reader to have a clearer way to examine them. Alternatively, I suggest combining the information provided in some multi-panel images in a small number.Major comments:
- Section 3.2.1: How do the authors calculate the average time series that they show in Figure 4? Are they averaging the values in the whole GBA? Are they accounting only for the land part or also the water? Considering that the two models start to diverge in a night-day cycle could be possible to see also how the observations perform in that cycle?
- Section 3.2.2: Lines 250 - 255: this change in the wind direction between two simulations that should use the same re-analysis data as input of meteorology needs more clarification. Assuming the spin-up between the simulations is the same, the input data are the same and all the other parameters are the same, how do the authors justify this difference? Do the authors use any kind of nudging option in WRF to constrain the model outputs to the initial re-analysis fields?
- Minor comments:
the authors should provide a more detailed description of the model configuration adopted for the simulations. In particular, there is information relative to spatial resolution, initial and boundary conditions used to feed the model, emission inventories for the chemistry and input of meteorology for WRF that should be mentioned and motivated in the methodology section to make the experiment replicable. - Even if we appreciate that the authors are trying to make the validation easier to read it would be advisable to generate a range of confidence in model performance on the basis of the values of MNB, R and Index.
Typos:
Line 75: The authors mention " black square" but they don't mention Figure 1. Modify to (Figure 1, black square)
Line 133: Fig.2 to substitute into Fig.2 (b)- AC1: 'Reply on RC1', Jinhui Gao, 25 Apr 2025
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CC1: 'Comment on egusphere-2024-3070', H. Kang, 16 Dec 2024
This manuscript presents an interesting and well-executed study on the formation of high ozone episodes in the Great Bay Area associated with typhoon NIDA. By utilizing an ensemble simulation approach in the WRF-Chem model, the authors effectively explore the variations in ozone concentrations during the extreme pollution event preceding the typhoon's landfall. The study focuses on distinguishing between well-performing and poorly performing simulations, providing valuable insights into the physical and chemical processes that govern the formation of high ozone concentrations. However, the manuscript would benefit from improvements in figure presentation, particularly regarding the excessive number of small figures. Additionally, some expressions could be made clearer to enhance the overall understanding of the results.
Specific comments:
1. The number of small figures in the manuscript is excessive. It is recommended to simplify the figures wherever possible:
(1) Figure 1a could be combined with Figure 2a. The wind field in Figure 1a should be moved to Figure 2a, while the O3 concentrations from the stations in Figure 1a could be placed in Figure 2b. In Figure 2b, the meteorological stations should be shown in black or gray. Additionally, consider swapping the positions of Figures 1 and 2.
(2) The information in Figure 4a overlaps with that in Figures 3b and 3c. It is suggested to combine Figures 3b and 3c into one and present them as Figure 4a.
(3) There are too many subplots in Figure 5, which makes it difficult to interpret the figure references in the text. For example, it is unclear whether Fig. 5a1 corresponds to 10:00 LT or 11:00 LT. I recommend reducing the number of subplots.
2. In Figure 3a, it would be better to use abs(MNB) on the Y-axis, as MNB can take both positive and negative values.
3. Line 247：“ADV is influenced wind field...” should be “The contribution of ADV is influenced by wind field …”
4. Line 305: "The enhanced ADV of CO was also due to weak winds." This sentence may cause confusion. Strong winds accompanied by large positive/negative concentration gradients can lead to high positive/negative ADV contributions, while weak wind speeds result in lowerCO inflow or outflow. In Figure 8g, the positive ΔADV_CO could indicate that under good EMs, weaker winds have reduced the outflow of CO from the GBA region, which may be an important source of CO.
5. Lines 338-341: "It’s clear that … 14%, respectively." These sentences are unclear. Do the authors intend to convey that compared to the bad EMs, the good EMs show a significant increase in contributions from regions like GBA? Among the increased contributions, the proportions from various source regions are 73% … 14%?
6. Many studies suggest that subsiding airflows in the periphery of typhoons can transport O3 from the upper troposphere or even the lower stratosphere to the surface. Did your simulations identify any similar vertical transport processes? If so, what is the contribution of this vertical transport to surface O3 levels?
Citation: https://doi.org/10.5194/egusphere-2024-3070-CC1 - AC3: 'Reply on CC1', Jinhui Gao, 25 Apr 2025
-
RC2: 'Comment on egusphere-2024-3070', Anonymous Referee #2, 05 Mar 2025
- AC2: 'Reply on RC2', Jinhui Gao, 25 Apr 2025
Interactive discussion
Status: closed
-
RC1: 'Comment on egusphere-2024-3070', Anonymous Referee #1, 22 Nov 2024
The present article aims to investigate the physical and chemical drivers of extreme levels of Ozone in the Great Bay area of South China during the typhoon approach. The article shows the results of an intercomparison work of numerical simulations and the application of a source apportionment method to analyse the causes of extreme events and ozone high levels.
The content of the present manuscript is of interest to the scientific community both for the occurrence, more frequency of extreme weather events and their link with air pollution, and for the evaluation of the performance of CTM such as WRF-Chem in reproducing these events.
From this point of view, the manuscript has certain relevance in the field and for the journal. On the other hand, I found the manuscript need more work to improve the clarity of the facts exposed and a better choice of images to show.
One of the starting questions reading the manuscript, is relative to the configuration of the model, the spatial resolution, and the inputs used for the weather and emissions. These elements should be mentioned in the text and in a table to let other scientists replicate the experiment in the future.Secondly, the validation of the model performance should be more evidently quantitative. I appreciate the tentative to make the evaluation easy to understand by keywords (good/bad) and colours (red/blue) but it would be also good to know the real numbers behind the model performance. How is the best bias? How’s the worst? These are numbers that in the most important intercomparison exercises are provided.
Said that I found interesting and intuitive the use of the Index linking MNB and R to show the model performance and I believe it’s a kind of combined metric that would find replication in other works.
Finally, the images chosen for the manuscript are too small and too many. I suggest selecting those that need to stay in the main text and making them bigger in order to allow the reader to have a clearer way to examine them. Alternatively, I suggest combining the information provided in some multi-panel images in a small number.Major comments:
- Section 3.2.1: How do the authors calculate the average time series that they show in Figure 4? Are they averaging the values in the whole GBA? Are they accounting only for the land part or also the water? Considering that the two models start to diverge in a night-day cycle could be possible to see also how the observations perform in that cycle?
- Section 3.2.2: Lines 250 - 255: this change in the wind direction between two simulations that should use the same re-analysis data as input of meteorology needs more clarification. Assuming the spin-up between the simulations is the same, the input data are the same and all the other parameters are the same, how do the authors justify this difference? Do the authors use any kind of nudging option in WRF to constrain the model outputs to the initial re-analysis fields?
- Minor comments:
the authors should provide a more detailed description of the model configuration adopted for the simulations. In particular, there is information relative to spatial resolution, initial and boundary conditions used to feed the model, emission inventories for the chemistry and input of meteorology for WRF that should be mentioned and motivated in the methodology section to make the experiment replicable. - Even if we appreciate that the authors are trying to make the validation easier to read it would be advisable to generate a range of confidence in model performance on the basis of the values of MNB, R and Index.
Typos:
Line 75: The authors mention " black square" but they don't mention Figure 1. Modify to (Figure 1, black square)
Line 133: Fig.2 to substitute into Fig.2 (b)- AC1: 'Reply on RC1', Jinhui Gao, 25 Apr 2025
-
CC1: 'Comment on egusphere-2024-3070', H. Kang, 16 Dec 2024
This manuscript presents an interesting and well-executed study on the formation of high ozone episodes in the Great Bay Area associated with typhoon NIDA. By utilizing an ensemble simulation approach in the WRF-Chem model, the authors effectively explore the variations in ozone concentrations during the extreme pollution event preceding the typhoon's landfall. The study focuses on distinguishing between well-performing and poorly performing simulations, providing valuable insights into the physical and chemical processes that govern the formation of high ozone concentrations. However, the manuscript would benefit from improvements in figure presentation, particularly regarding the excessive number of small figures. Additionally, some expressions could be made clearer to enhance the overall understanding of the results.
Specific comments:
1. The number of small figures in the manuscript is excessive. It is recommended to simplify the figures wherever possible:
(1) Figure 1a could be combined with Figure 2a. The wind field in Figure 1a should be moved to Figure 2a, while the O3 concentrations from the stations in Figure 1a could be placed in Figure 2b. In Figure 2b, the meteorological stations should be shown in black or gray. Additionally, consider swapping the positions of Figures 1 and 2.
(2) The information in Figure 4a overlaps with that in Figures 3b and 3c. It is suggested to combine Figures 3b and 3c into one and present them as Figure 4a.
(3) There are too many subplots in Figure 5, which makes it difficult to interpret the figure references in the text. For example, it is unclear whether Fig. 5a1 corresponds to 10:00 LT or 11:00 LT. I recommend reducing the number of subplots.
2. In Figure 3a, it would be better to use abs(MNB) on the Y-axis, as MNB can take both positive and negative values.
3. Line 247：“ADV is influenced wind field...” should be “The contribution of ADV is influenced by wind field …”
4. Line 305: "The enhanced ADV of CO was also due to weak winds." This sentence may cause confusion. Strong winds accompanied by large positive/negative concentration gradients can lead to high positive/negative ADV contributions, while weak wind speeds result in lowerCO inflow or outflow. In Figure 8g, the positive ΔADV_CO could indicate that under good EMs, weaker winds have reduced the outflow of CO from the GBA region, which may be an important source of CO.
5. Lines 338-341: "It’s clear that … 14%, respectively." These sentences are unclear. Do the authors intend to convey that compared to the bad EMs, the good EMs show a significant increase in contributions from regions like GBA? Among the increased contributions, the proportions from various source regions are 73% … 14%?
6. Many studies suggest that subsiding airflows in the periphery of typhoons can transport O3 from the upper troposphere or even the lower stratosphere to the surface. Did your simulations identify any similar vertical transport processes? If so, what is the contribution of this vertical transport to surface O3 levels?
Citation: https://doi.org/10.5194/egusphere-2024-3070-CC1 - AC3: 'Reply on CC1', Jinhui Gao, 25 Apr 2025
-
RC2: 'Comment on egusphere-2024-3070', Anonymous Referee #2, 05 Mar 2025
- AC2: 'Reply on RC2', Jinhui Gao, 25 Apr 2025
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Jinhui Gao
Hui Xiao
The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
- Preprint
(7954 KB) - Metadata XML
-
Supplement
(686 KB) - BibTeX
- EndNote
- Final revised paper