the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 13 Jun 2022
A Storm-centered Multivariate Modeling of Extreme Precipitation Frequency Based on Atmospheric Water Balance
Abstract. Conventional rainfall frequency analysis faces several limitations. These include difficulty incorporating relevant atmospheric variables beyond precipitation and limited ability to depict the frequency of rainfall over large areas that is relevant for flooding. This study proposes a storm-based model of extreme precipitation frequency based on the atmospheric water balance equation. We developed a storm tracking and regional characterization (STARCH) method to identify precipitation systems in space and time from hourly ERA5 precipitation fields over the contiguous United States from 1951 to 2020. Extreme “storm catalogs” were created by selecting annual maximum storms with specific areas and durations over a chosen region. The annual maximum storm precipitation was then modeled via multivariate distributions of atmospheric water balance components using vine copula models. We applied this approach to estimate precipitation average recurrence intervals for storm areas from 5,000 to 100,000 km2 and durations from 2 to 72 hours in the Mississippi Basin and its five major subbasins. The estimated precipitation distributions show a good fit to the reference data from the original storm catalogs and are close to the estimates from conventional univariate GEV distributions. Our approach explicitly represents the contributions of water balance components in extreme precipitation. Of these, water vapor flux convergence is the main contributor, while precipitable water and a mass residual term can also be important, particularly for short durations and small storm footprints. We also found that ERA5 shows relatively good water balance closure for extreme storms, with a mass residual on average 10 % of precipitation. The approach can incorporate nonstationarities in water balance components and their dependence structures and can benefit from further advancements in reanalysis products and storm tracking techniques.
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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
(9038 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-2022-391', Geoff Pegram, 04 Jul 2022
Morin-Liu-egusphere-2022-391
HESS by any other name!
I could find only two minor 1-word corrections in all of the 13905 +/- words in the article:
- ‘hewing’ in line 334, which means ‘cutting or chopping’ (in the Concise Oxford Dictionary) to be replaced. Try "cleaving" = 'stick fast' or 'adhere to' [COD again]
- ‘from all the co-authors’ in line 659 , to be replaced by ‘from BOTH the authors’!
These are the ONLY words I want to alter that I can find need treatment in this article. Nice work – I have never found such precision in all my 300+ reviews. Bravo! Good interesting and cloud breaking (!) paper.
I will ask the Editor to accept the submission after these minor glitches have been attended to.
Geoff Pegram
4 July 2022
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AC1: 'Reply on RC1', Yuan Liu, 17 Aug 2022
We thank the referee for reviewing and providing support to our manuscript, and we are pleased by the enthusiastic response! We corrected the two places in the manuscript according to the reviewer’s comments.
Citation: https://doi.org/10.5194/egusphere-2022-391-AC1
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RC2: 'Comment on egusphere-2022-391', Anonymous Referee #2, 19 Jul 2022
I have read over the paper. I think it is very well written and if I was reviewing this I would only ask for moderate or minor revisions.
The authors developed a storm tracking algorithm (using a combination of existing algorithms in a novel way) and use it to create a dataset of large storm events which they perform frequency analysis on. Despite the novel storm tracking algorithm, performing frequency analysis on a storm dataset (as opposed to gauge records) is not in itself novel. This paper’s key contribution comes from the way the authors uses copula based multivariate analysis on atmospheric variables from ERA5 to develop a way to stochastically generate annual maxima series representative of the observed storm catalogues.
Major comments
My first major concern with the paper is that the authors do not make enough attempts to validate their method or at least compare it to external data sources.
The only comparison to other methods they make is with a GEV fitted to storm annual maxima in Figure 6-7. I believe there is also opportunity to compare DAD curves in Figure 8 to an external data sources. The authors mention the relationship between DAD curves and ARFs, so any ARF information available for the Mississippi basin could be used to formulate a comparison here.
I appreciate that these comparisons may be difficult to facilitate because of the authors have taken a storm-centred approach while the majority of other datasets are based on gauge-centred data. Still for their approach to be applied outside of research we need to understand how it compares to existing approaches.
My second major concern is about the use of empirical CDFs for all atmospheric variables except the divergence term for which a GEV is fitted. While divergence shows the highest correlation to precipitation I find this insufficient justification for why only this variable is modelled using a GEV. I also note that other terms such as the residual also have non-negligible contributions to rainfall. I would be interested to know if there is any change in results if similar extreme value distributions are used for other atmospheric variables.
Minor comments
I’d prefer the use of spelling gauge to gage, I think it’s most common in modern literature
Section 3.1: I believe the authors could draw more attention to their storm search method being a novel combination of existing approaches
Line 170-175: I think the explanation of the ‘binary search’ is not clear and could be improved
Line 255: The GEV scale parameter must be greater than zero (σ>0)
Line 354: Should reference Figures A1-2?
Additional - uncertainty in the ERA data is not accounted for in the approach here. Alternate reanalyses do not agree with each other, even for atmospheric moisture - see Moalafhi, D. B., Evans, J. P. & Sharma, A. Influence of reanalysis datasets on dynamically downscaling the recent past. Climate Dynamics 49, 1239-1255 (2017). Some discussion on the impact of this uncertainty and how it could be included in the GEV modeling may be helpful.
Citation: https://doi.org/10.5194/egusphere-2022-391-RC2 - AC2: 'Reply on RC2', Yuan Liu, 17 Aug 2022
Interactive discussion
Status: closed
-
RC1: 'Comment on egusphere-2022-391', Geoff Pegram, 04 Jul 2022
Morin-Liu-egusphere-2022-391
HESS by any other name!
I could find only two minor 1-word corrections in all of the 13905 +/- words in the article:
- ‘hewing’ in line 334, which means ‘cutting or chopping’ (in the Concise Oxford Dictionary) to be replaced. Try "cleaving" = 'stick fast' or 'adhere to' [COD again]
- ‘from all the co-authors’ in line 659 , to be replaced by ‘from BOTH the authors’!
These are the ONLY words I want to alter that I can find need treatment in this article. Nice work – I have never found such precision in all my 300+ reviews. Bravo! Good interesting and cloud breaking (!) paper.
I will ask the Editor to accept the submission after these minor glitches have been attended to.
Geoff Pegram
4 July 2022
-
AC1: 'Reply on RC1', Yuan Liu, 17 Aug 2022
We thank the referee for reviewing and providing support to our manuscript, and we are pleased by the enthusiastic response! We corrected the two places in the manuscript according to the reviewer’s comments.
Citation: https://doi.org/10.5194/egusphere-2022-391-AC1
-
RC2: 'Comment on egusphere-2022-391', Anonymous Referee #2, 19 Jul 2022
I have read over the paper. I think it is very well written and if I was reviewing this I would only ask for moderate or minor revisions.
The authors developed a storm tracking algorithm (using a combination of existing algorithms in a novel way) and use it to create a dataset of large storm events which they perform frequency analysis on. Despite the novel storm tracking algorithm, performing frequency analysis on a storm dataset (as opposed to gauge records) is not in itself novel. This paper’s key contribution comes from the way the authors uses copula based multivariate analysis on atmospheric variables from ERA5 to develop a way to stochastically generate annual maxima series representative of the observed storm catalogues.
Major comments
My first major concern with the paper is that the authors do not make enough attempts to validate their method or at least compare it to external data sources.
The only comparison to other methods they make is with a GEV fitted to storm annual maxima in Figure 6-7. I believe there is also opportunity to compare DAD curves in Figure 8 to an external data sources. The authors mention the relationship between DAD curves and ARFs, so any ARF information available for the Mississippi basin could be used to formulate a comparison here.
I appreciate that these comparisons may be difficult to facilitate because of the authors have taken a storm-centred approach while the majority of other datasets are based on gauge-centred data. Still for their approach to be applied outside of research we need to understand how it compares to existing approaches.
My second major concern is about the use of empirical CDFs for all atmospheric variables except the divergence term for which a GEV is fitted. While divergence shows the highest correlation to precipitation I find this insufficient justification for why only this variable is modelled using a GEV. I also note that other terms such as the residual also have non-negligible contributions to rainfall. I would be interested to know if there is any change in results if similar extreme value distributions are used for other atmospheric variables.
Minor comments
I’d prefer the use of spelling gauge to gage, I think it’s most common in modern literature
Section 3.1: I believe the authors could draw more attention to their storm search method being a novel combination of existing approaches
Line 170-175: I think the explanation of the ‘binary search’ is not clear and could be improved
Line 255: The GEV scale parameter must be greater than zero (σ>0)
Line 354: Should reference Figures A1-2?
Additional - uncertainty in the ERA data is not accounted for in the approach here. Alternate reanalyses do not agree with each other, even for atmospheric moisture - see Moalafhi, D. B., Evans, J. P. & Sharma, A. Influence of reanalysis datasets on dynamically downscaling the recent past. Climate Dynamics 49, 1239-1255 (2017). Some discussion on the impact of this uncertainty and how it could be included in the GEV modeling may be helpful.
Citation: https://doi.org/10.5194/egusphere-2022-391-RC2 - AC2: 'Reply on RC2', Yuan Liu, 17 Aug 2022
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Daniel Benjamin Wright
The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
- Preprint
(9038 KB) - Metadata XML