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https://doi.org/10.5194/egusphere-2023-1683
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/egusphere-2023-1683
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
20 Sep 2023
| 20 Sep 2023
Analysis of the phase space of the downburst that occurred on 25 June 2021 in Sânnicolau Mare (Romania)
Abstract. Downbursts winds, characterized by strong, localized downdrafts and subsequent horizontal straight-line winds, presents significant risk to civil structures. The transient nature and limited spatial extent present measurements challenges, necessitating analytical models for accurate understanding and predicting their action on structures. This study analyzes the Sânnicolau Mare downburst event in Romania, from June 25, 2021, using a bi-dimensional analytical model coupled with the Teaching Learning Optimization Algorithm (TLBO). The intent is to understand the distinct solutions generated by the optimization algorithm and assess their physical validity. Supporting this examination is a damage survey and wind speed data recorded during the downburst event. Employed techniques include agglomerative hierarchical clustering with the K-means algorithm (AHK-MC) and principal component analysis (PCA) to categorize and interpret the solutions. Three main clusters emerge, each displaying different storm characteristics. Comparing the simulated maximum velocity with hail damage trajectories indicates that the optimal solution offers the best overlap, affirming its effectiveness in reconstructing downburst wind fields. However, these findings are specific to the Sânnicolau Mare event, underlining the need for a similar examination of multiple downburst events for broader validity.
How to cite. Xhelaj, A. and Burlando, M.: Analysis of the phase space of the downburst that occurred on 25 June 2021 in Sânnicolau Mare (Romania), EGUsphere [preprint], https://doi.org/10.5194/egusphere-2023-1683, 2023.
Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this preprint. The responsibility to include appropriate place names lies with the authors.
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Journal article(s) based on this preprint
14 May 2024
Application of the teaching–learning-based optimization algorithm to an analytical model of thunderstorm outflows to analyze the variability of the downburst kinematic and geometric parameters
Andi Xhelaj and Massimiliano Burlando
Nat. Hazards Earth Syst. Sci., 24, 1657–1679, https://doi.org/10.5194/nhess-24-1657-2024, https://doi.org/10.5194/nhess-24-1657-2024, 2024
Short summary
Short summary
The study provides an in-depth analysis of a severe downburst event in Sânnicolau Mare, Romania, utilizing an analytical model and optimization algorithm. The goal is to explore a multitude of generating solutions and to identify potential alternatives to the optimal solution. Advanced data analysis techniques help to discern three main distinct storm scenarios. For this particular event, the best overall solution from the optimization algorithm shows promise in reconstructing the downburst.
Interactive discussion
Status: closed
Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor
| : Report abuse
-
RC1: 'Comment on egusphere-2023-1683', Anonymous Referee #1, 16 Oct 2023
Overview
This manuscript describes the methodology of finding the optimal solution for a combination of parameters used in an analytical model of downburst outflows. This methodology is then applied to a particular case of downburst that occurred in Rumania in the summer of 2021. It is clear to me that the authors are knowledgeable in this field and the description of the method and results is very comprehensive. Perhaps even too comprehensive in some instances. While I have a number of comments below, I consider most of them to be minor. I believe the manuscript is well suited for this journal and it is most certainly a topic of importance in downburst research and wind engineering.
Specific comments:
- Begin instead of begins.
- Is the scale of less than a kilometer related to a downburst or thunderstorm? It should be related to a downburst, but it is not clear based on how the sentence is structured.
- Xhelah et al. is not the proper reference. I suggest the authors write “Xhelaj et al. (2020) presented…” The reader is automatically informed that the research was published in 2020.
- L46 and elsewhere. I believe that “et” should not have a dot.
- L22–L72. This paragraph is well written, but it is too long. Please split this large paragraph into 2–3 smaller paragraphs to increase readability of this section. For example, L56 (goals of this research) can be the beginning of a separate paragraph.
- I suggest rewriting this sentence as follows: …was produced during the passage of an intense mesoscale convective system in the form of a bow echo over the town of Sânnicolau Mare.
- Some parts of the manuscript can be shortened. For example, the definition of thunderstorms in L100 is probably not needed. Even if needed, that general discussion should be in the introduction. Similarly, L103 and the difference between downburst and atmospheric boundary layer winds is probably not needed as well.
- Figure 1. Labels in panel (b) should be of higher resolution and the red dot that indicates the tower location should be larger.
- Figure 2. In principle, I have no problems with this figure but is it really that the researchers don’t have their own photograph of the tower and have to use Google Street View? I suggest replacing this figure with their own photograph.
- Figure 3b. It is very interesting to note that the squall line seems to be stratiform parallel, which is one of the rarest types of squall lines. See Markowski and Richardson (2010) and references therein on squall lines. The authors, of course, do not need to pursue this comment further, it’s just an interesting observation from this reviewer.
- Figure 5a. Tmin = 14.5 degC should not be over the line. There is plenty of space to move it elsewhere.
- L171–174. Downburst is a wind event and hail is hailstones falling from the cloud. If hailstones cause the damage, that is not the same as caused by wind (i.e., downburst). One might rephrase this to state that the downburst was also associated with hail that caused substantial damage. Then the reader knows that the damage was not wind-driven but hail-driven.
- Figure 6. Indicate the North direction in this figure.
- Table 1. The units should not be italicized.
- This sentence is the same statement as the previous sentence, which is that Figure 7 shows the convergence pattern of the objective function.
- Figure 7. The mean convergence curve does not converge at about 70 iterations. Indeed the envelop curves seem to converge at about that value, but not the mean and standard deviation curves.
- Remove the comma after 1024.
- Symmetry rather than simmetry.
- (2) The word and should not be italicized.
- Probably an incorrect reference format. Please double-check.
- Rewrite to “The hierarchical tree in Figure 9 (i.e., dendrogram) is constructed following the Wards’ method (Ward, 1963).” and then delete the following sentence because it contains the same information.
- Figure 9. Indicate in the figure caption that the three colors serve to visualize three identified clusters.
- Discussion about Figure 10. When this method is applied to other problems in engineering and/or atmospheric sciences, is clustering that represents ~60% of the total variance an acceptable value? In other words, this problem is related to finding the optimal combination of downburst parameters, and Figures 9 and 10 show that the clustering of solutions in the present way explains ~60% of variance among all solutions. If one looks at other (similar) problems in meteorology, engineering, earth sciences, etc., does one observe a similar level of model confidence? A few references on this subject might help.
- Section 5.1 and other sections. Please separate your sections into multiple paragraphs. Having one paragraph that covers more almost one whole page reduces the readability of your manuscript.
- Related to my previous comment, this manuscript should be shorter. I think that the level of English is satisfactory, but certain parts of the manuscript can be shortened.
- How does the k-means algorithm work to improve the partitioning? What is the mechanism by which k-means algorithm moves the clusters from being overlapped to disjoint (Figure 11)?
- Table 4. Define p1, p2, and Vk in the caption of this table.
- Table 4 and the associated text. Explain what is the weight of a variable in the context of your analysis and the quality of representation (projection)?
- Rewrite the first sentence in this line for better English.
- Figure 17 and associated discussion. This is very nice. Is it possible to constrain your space of solutions by fixing some of the parameters using the observations (e.g., direction of damage, translation speed and direction of the cloud, etc.). In L545–546 you conclude that one needs to conduct many simulations, but wouldn’t make more sense to constrain simulations with known values of parameters rather than letting all parameters take arbitrary values?
- I think the title should more highlight the main topic of this paper and that’s the application of the Teaching Learning Optimization Algorithm to your analytical model. You are using an objective method to find the optimal solution from the space of all solutions and the particular downburst is just a case study that you used to validate your method. A title such as “The Application of Teaching Learning Optimization Algorithm to Analytical Model of Downburst Outflows” might better capture the main topic of this paper, but I leave it up to the authors to decide.
Citation: https://doi.org/10.5194/egusphere-2023-1683-RC1 -
AC1: 'Reply on RC1', Andi Xhelaj, 30 Nov 2023
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2023/egusphere-2023-1683/egusphere-2023-1683-AC1-supplement.pdf
-
RC2: 'Comment on egusphere-2023-1683', Anonymous Referee #2, 16 Nov 2023
Please see review_egusphere_2023_1683.pdf.
-
AC2: 'Reply on RC2', Andi Xhelaj, 30 Nov 2023
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2023/egusphere-2023-1683/egusphere-2023-1683-AC2-supplement.pdf
-
AC2: 'Reply on RC2', Andi Xhelaj, 30 Nov 2023
Interactive discussion
Status: closed
Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor
| : Report abuse
-
RC1: 'Comment on egusphere-2023-1683', Anonymous Referee #1, 16 Oct 2023
Overview
This manuscript describes the methodology of finding the optimal solution for a combination of parameters used in an analytical model of downburst outflows. This methodology is then applied to a particular case of downburst that occurred in Rumania in the summer of 2021. It is clear to me that the authors are knowledgeable in this field and the description of the method and results is very comprehensive. Perhaps even too comprehensive in some instances. While I have a number of comments below, I consider most of them to be minor. I believe the manuscript is well suited for this journal and it is most certainly a topic of importance in downburst research and wind engineering.
Specific comments:
- Begin instead of begins.
- Is the scale of less than a kilometer related to a downburst or thunderstorm? It should be related to a downburst, but it is not clear based on how the sentence is structured.
- Xhelah et al. is not the proper reference. I suggest the authors write “Xhelaj et al. (2020) presented…” The reader is automatically informed that the research was published in 2020.
- L46 and elsewhere. I believe that “et” should not have a dot.
- L22–L72. This paragraph is well written, but it is too long. Please split this large paragraph into 2–3 smaller paragraphs to increase readability of this section. For example, L56 (goals of this research) can be the beginning of a separate paragraph.
- I suggest rewriting this sentence as follows: …was produced during the passage of an intense mesoscale convective system in the form of a bow echo over the town of Sânnicolau Mare.
- Some parts of the manuscript can be shortened. For example, the definition of thunderstorms in L100 is probably not needed. Even if needed, that general discussion should be in the introduction. Similarly, L103 and the difference between downburst and atmospheric boundary layer winds is probably not needed as well.
- Figure 1. Labels in panel (b) should be of higher resolution and the red dot that indicates the tower location should be larger.
- Figure 2. In principle, I have no problems with this figure but is it really that the researchers don’t have their own photograph of the tower and have to use Google Street View? I suggest replacing this figure with their own photograph.
- Figure 3b. It is very interesting to note that the squall line seems to be stratiform parallel, which is one of the rarest types of squall lines. See Markowski and Richardson (2010) and references therein on squall lines. The authors, of course, do not need to pursue this comment further, it’s just an interesting observation from this reviewer.
- Figure 5a. Tmin = 14.5 degC should not be over the line. There is plenty of space to move it elsewhere.
- L171–174. Downburst is a wind event and hail is hailstones falling from the cloud. If hailstones cause the damage, that is not the same as caused by wind (i.e., downburst). One might rephrase this to state that the downburst was also associated with hail that caused substantial damage. Then the reader knows that the damage was not wind-driven but hail-driven.
- Figure 6. Indicate the North direction in this figure.
- Table 1. The units should not be italicized.
- This sentence is the same statement as the previous sentence, which is that Figure 7 shows the convergence pattern of the objective function.
- Figure 7. The mean convergence curve does not converge at about 70 iterations. Indeed the envelop curves seem to converge at about that value, but not the mean and standard deviation curves.
- Remove the comma after 1024.
- Symmetry rather than simmetry.
- (2) The word and should not be italicized.
- Probably an incorrect reference format. Please double-check.
- Rewrite to “The hierarchical tree in Figure 9 (i.e., dendrogram) is constructed following the Wards’ method (Ward, 1963).” and then delete the following sentence because it contains the same information.
- Figure 9. Indicate in the figure caption that the three colors serve to visualize three identified clusters.
- Discussion about Figure 10. When this method is applied to other problems in engineering and/or atmospheric sciences, is clustering that represents ~60% of the total variance an acceptable value? In other words, this problem is related to finding the optimal combination of downburst parameters, and Figures 9 and 10 show that the clustering of solutions in the present way explains ~60% of variance among all solutions. If one looks at other (similar) problems in meteorology, engineering, earth sciences, etc., does one observe a similar level of model confidence? A few references on this subject might help.
- Section 5.1 and other sections. Please separate your sections into multiple paragraphs. Having one paragraph that covers more almost one whole page reduces the readability of your manuscript.
- Related to my previous comment, this manuscript should be shorter. I think that the level of English is satisfactory, but certain parts of the manuscript can be shortened.
- How does the k-means algorithm work to improve the partitioning? What is the mechanism by which k-means algorithm moves the clusters from being overlapped to disjoint (Figure 11)?
- Table 4. Define p1, p2, and Vk in the caption of this table.
- Table 4 and the associated text. Explain what is the weight of a variable in the context of your analysis and the quality of representation (projection)?
- Rewrite the first sentence in this line for better English.
- Figure 17 and associated discussion. This is very nice. Is it possible to constrain your space of solutions by fixing some of the parameters using the observations (e.g., direction of damage, translation speed and direction of the cloud, etc.). In L545–546 you conclude that one needs to conduct many simulations, but wouldn’t make more sense to constrain simulations with known values of parameters rather than letting all parameters take arbitrary values?
- I think the title should more highlight the main topic of this paper and that’s the application of the Teaching Learning Optimization Algorithm to your analytical model. You are using an objective method to find the optimal solution from the space of all solutions and the particular downburst is just a case study that you used to validate your method. A title such as “The Application of Teaching Learning Optimization Algorithm to Analytical Model of Downburst Outflows” might better capture the main topic of this paper, but I leave it up to the authors to decide.
Citation: https://doi.org/10.5194/egusphere-2023-1683-RC1 -
AC1: 'Reply on RC1', Andi Xhelaj, 30 Nov 2023
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2023/egusphere-2023-1683/egusphere-2023-1683-AC1-supplement.pdf
-
RC2: 'Comment on egusphere-2023-1683', Anonymous Referee #2, 16 Nov 2023
Please see review_egusphere_2023_1683.pdf.
-
AC2: 'Reply on RC2', Andi Xhelaj, 30 Nov 2023
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2023/egusphere-2023-1683/egusphere-2023-1683-AC2-supplement.pdf
-
AC2: 'Reply on RC2', Andi Xhelaj, 30 Nov 2023
Peer review completion
AR: Author's response | RR: Referee report | ED: Editor decision | EF: Editorial file upload
ED: Reconsider after major revisions (further review by editor and referees) (04 Dec 2023) by Gregor C. Leckebusch
AR by Andi Xhelaj on behalf of the Authors (28 Dec 2023)
Author's response
Author's tracked changes
Manuscript
ED: Referee Nomination & Report Request started (17 Jan 2024) by Gregor C. Leckebusch
RR by Anonymous Referee #1 (17 Jan 2024)
RR by Anonymous Referee #2 (23 Jan 2024)
ED: Publish as is (25 Jan 2024) by Gregor C. Leckebusch
AR by Andi Xhelaj on behalf of the Authors (29 Jan 2024)
Manuscript
Journal article(s) based on this preprint
14 May 2024
Application of the teaching–learning-based optimization algorithm to an analytical model of thunderstorm outflows to analyze the variability of the downburst kinematic and geometric parameters
Andi Xhelaj and Massimiliano Burlando
Nat. Hazards Earth Syst. Sci., 24, 1657–1679, https://doi.org/10.5194/nhess-24-1657-2024, https://doi.org/10.5194/nhess-24-1657-2024, 2024
Short summary
Short summary
The study provides an in-depth analysis of a severe downburst event in Sânnicolau Mare, Romania, utilizing an analytical model and optimization algorithm. The goal is to explore a multitude of generating solutions and to identify potential alternatives to the optimal solution. Advanced data analysis techniques help to discern three main distinct storm scenarios. For this particular event, the best overall solution from the optimization algorithm shows promise in reconstructing the downburst.
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Department of Civil, Chemical and Environmental Engineering Polytechnic School, University of Genoa, Via Montallegro 1, 16145 Genoa, Italy
Massimiliano Burlando
Department of Civil, Chemical and Environmental Engineering Polytechnic School, University of Genoa, Via Montallegro 1, 16145 Genoa, Italy
Download
The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
- Preprint
(4985 KB) - Metadata XML
Short summary
The study provides an in-depth analysis of a severe downburst event in Sânnicolau Mare, Romania, utilizing an analytical model and optimization algorithm. The goal is to explore a multitude of generating solutions and to identify potential alternatives to the optimal solution. Advanced data analysis techniques help to discern three main distinct storm scenarios. For this particular event, the best overall solution from the optimization algorithm shows promise in reconstructing the downburst.
The study provides an in-depth analysis of a severe downburst event in Sânnicolau Mare, Romania,...