Deep learning of extreme rainfall events from convective atmospheres

Bürger, Gerd; Heistermann, Maik

doi:https://doi.org/10.5194/egusphere-2022-1159

Preprints

https://doi.org/10.5194/egusphere-2022-1159

Preprints

07 Nov 2022

| 07 Nov 2022

Deep learning of extreme rainfall events from convective atmospheres

Gerd Bürger and Maik Heistermann

Abstract. Our subject is a new Catalogue of radar-based heavy Rainfall Events (CatRaRE) over Germany, and how it relates to the concurrent atmospheric circulation. We classify daily atmospheric ERA5 fields of convective indices according to CatRaRE, using an array of conventional statistical and more recent machine learning (ML) algorithms, and apply them to corresponding fields of simulated present and future atmospheres from the CORDEX project. Due to the stochastic nature of ML optimization there is some spread in the results. The ALL-CNN network performs best on average, with several learning runs exceeding an Equitable Threat Score (ETS) of 0.52; the single best result was from ResNet with ETS = 0.54. The best performing classical scheme was a Random Forest with ETS = 0.51. Regardless of the method, increasing trends are predicted for the probability of CatRaRE-type events, from ERA5 as well as from the CORDEX fields.

Received: 25 Oct 2022 – Discussion started: 07 Nov 2022

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Journal article(s) based on this preprint

18 Sep 2023

Shallow and deep learning of extreme rainfall events from convective atmospheres

Gerd Bürger and Maik Heistermann

Nat. Hazards Earth Syst. Sci., 23, 3065–3077, https://doi.org/10.5194/nhess-23-3065-2023,https://doi.org/10.5194/nhess-23-3065-2023, 2023

Short summary

Gerd Bürger and Maik Heistermann

Interactive discussion

Status: closed

RC1:
'Comment on egusphere-2022-1159', Anonymous Referee #1, 28 Nov 2022
In their manuscript, Bürger and Heistermann trained and applied multiple ML/DL models to the (binary) classification task of detecting convectively enforced extreme rainfall in Germany. They compare a whole range of different models ranging from regression models through random forests and shallow neural nets to well-known DL models for image classification like AlexNet, GoogLeNet or ResNet. The study's classification task can be formulated as follows: Given the (daily aggregated) ERA5 fields of CAPE, convective rainfall, and total column water over Germany, does CatRaRE contain at least one event exceeding warning level 3 where the event does not exceed nine hours. Bürger and Heistermann used cross-entropy as the loss function during training and evaluated their final results based on the Equitable Threat Score. As their DL models use a stochastic gradient descent algorithm during the optimisation, they trained 20 individual realisations per DL model resulting in an ensemble. Based on that (simplistic) ensemble, they report that the ALL-CNN model shows the highest mean ETS (across the ensemble) of .52. At the same time, the ResNet architecture provides the ensemble member with the highest individual ETS (.54).

The manuscript is well structured, and I appreciate the extensive model selection used for comparison and acknowledge the effort spent to train all of these. Even though the intercomparison of different methods and architectures is interesting on its own, I have difficulties distilling the overall relevance (concrete use case) of the classification for meteorological applications. I have some more points of concern, as listed below, but I am confident that the authors can adequately address them and that the manuscript can provide an important contribution to the field. I wonder if the GMD/ESSD inter-Journal SI "Benchmark datasets and machine learning algorithms for Earth system science data" (https://gmd.copernicus.org/articles/special_issue386_1147.html) might be suited better to the manuscript's scope. At least for me, the study's focus lies more on the intercomparison aspect rather than the "monitoring of precursors of evolution".

Major Comments

As mentioned above, it does not become clear to me what consequences a statement like "There is an extreme convective event (somewhere) over Germany" might have for a meteorologist, climatologist or decision-maker. L 229f somehow reflects the ultimate goal; however, it might be good to further distil the gain also in the introduction.

I wonder how a cross-entropy or ETS analysis might contribute to a better understanding of the influence of 'deep' in DL models, as stated in l. 41f. For such a statement, I would have expected some explainable AI (XAI) methods or some sensitivity analysis of each model type, like varying the number of inception blocks in the 'GoogLeNet-style' model. Here the introduction raises expectations that the conclusion does not reflect.

As far as I understand, you are using ERA5 data (cape, cp, tcw) as input X and CatRaRE as target y for training (2001-2010) and validation (2011-2020). Finally, you apply the trained model to data from HIST and RCP85. In l 144, you correctly state that the second dataset is not independent of the DL models, as you use those for model selection. As overfitting can happen on both - parameters (training set) and hyperparameters (validation set), why do you not split your data into three sets (training, validation, test)? Especially as you apply the trained models to data from different sources that likely have different properties, I think it would be beneficial to compare the test set's performance against the same (sub-)period of RCP85. Thus, you could detect differences in model performance that might serve as a guide towards interpreting all RCP85 data where you do not have any labels.

I suggest broadening the analysis of the predicted probabilities over the entire detection period. For example, replacing Fig. 5 with a reliability diagram where the predicted probability is plotted against the observed relative frequency might reveal model-specific differences.

Given the close range of ETS values across the different models, I suggest providing uncertainty quantifications and/or statistical tests to demonstrate the significance of your findings.

How do already existing 'classical' findings of the expected change of extreme precipitation align with your classification results? Can you discuss the concept drift in the data that the classifier faces?

In that regard, which period do you use to calculate the mean and std for the z-transformation?

Minor Comments

L. 22ff Besides the references to the 'classical' DL introductions, I encourage the authors to also focus on the recent discussions on ML/DL applications in atmospheric sciences like Reichstein et al. (2019) and Schultz et al. (2021).

L. 158f How do you analyse the influence of cape? In l. 126 you state that you are using cape, cp and tcw as channels similar to RGB. Please clarify how you create the "non-cape" classifications. Do you train the models with two channels only? Do you replace the cape channel with zeros or another variable?

Fig. 1 shows cape values jointly with the CatRaRe events used to define the extreme labels. The selected model domain contains pixels outside of Germany. CatRaRE, however, covers Germany only. Did you check (most likely with some other dataset) how often (if at all) extreme events occur outside of Germany but within your defined model domain? For me, that seems to be a potential source of introducing labelling errors.

Fig. 4: I suggest using a more colourblind-friendly palette.

Even though Table S1 lists several tuned hyperparameters, how does the learning rate change under the poly policy?

I suggest adding a column reporting the number of trainable parameters of your modified versions

Did you consider also using architectures already focussing on precipitation (for example (your) RainNet model (Ayzel et al., 2020)) and adjusting details for your classification task?

L. 59 I am wondering if a log transformation for cp before applying the standardisation might be beneficial

Please provide some more details on the EOF reduction. For example, how many components are you using?

From the first sentence in your abstract, I expect this manuscript to focus on creating a new data set that can be used for ML/DL applications. In its current state, the abstract does not adequately transport the enormous (DL-)model comparison you performed.

Formal Comments

As a reviewer, I was asked helping to ensure that manuscripts comply with the journal's guidelines. Therefore I'd like to point out some formal aspects:

Please add a "competing interests" statement as required by Copernicus Publication (see https://www.natural-hazards-and-earth-system-sciences.net/submission.html#manuscriptcomposition §16)

Software Code: You refer to your GitHub repository but to the best of my knowledge Copernicus Journals prefer software provided through a DOI (e.g. through zenodo)

URLs: Please add the last access dates to all URLs

A legend is missing in Fig. 3

References

Ayzel, G., Scheffer, T., and Heistermann, M.: RainNet v1.0: a convolutional neural network for radar-based precipitation nowcasting, Geoscientific Model Development, 13, 2631–2644, https://doi.org/10.5194/gmd-13-2631-2020, number: 6, 2020.

Reichstein, M., Camps-Valls, G., Stevens, B., Jung, M., Denzler, J., Carvalhais, N., and Prabhat: Deep learning and process understanding for data-driven Earth system science, Nature, 566, 195–204, https://doi.org/10.1038/s41586-019-0912-1, 2019.

Schultz, M. G., Betancourt, C., Gong, B., Kleinert, F., Langguth, M., Leufen, L. H., Mozaffari, A., and Stadtler, S.: Can deep learning beat numerical weather prediction?, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 379, 20200 097, https://doi.org/10.1098/rsta.2020.0097, 2021.
Citation: https://doi.org/10.5194/egusphere-2022-1159-RC1
- AC1: 'Reply on RC1', Gerd Bürger, 10 Jan 2023
  
  see pdf.
  
  Citation: https://doi.org/10.5194/egusphere-2022-1159-AC1
RC2:
'Comment on egusphere-2022-1159', Anonymous Referee #2, 21 Feb 2023

The authors applied a series of classification approaches to classify ERA5 data to binary catalogues of convective precipitation indices with an existing catalogue data (CatRaRE) as target. The series of classification approaches include several conventional methods (Lasso, random forests, logistic, and shallow neural network) and more sophisticated CNN based approaches that have been used in computer vision area (LeNet-5, AlexNet, ResNet, and so on). All the models are trained with the daily data from 2001 to 2010 and validated with the data from 2011 to 2020. The trained models were also applied to predict extreme indices in the future with future projections from GCM data.
The manuscript is relatively extensive. However, it basically lays out all the methods and is not driven by any research questions and objectives. Without finely tuning each method, especially for the sophisticated CNN models, it is unclear why to compare these methods and also the very similar results for each method give readers very limited insights from their studies. The manuscript appears more like a machine learning exercise instead of scientific research and it is not suitable for publication in this research journal.

Citation: https://doi.org/10.5194/egusphere-2022-1159-RC2
- AC2: 'Reply on RC2', Gerd Bürger, 28 Feb 2023
  
  We would like to thank the referee for taking the time to review our manuscript, and for the open and clear criticism. The central part of the comment is, as we understand, as follows:
  „[…] [the manuscript] basically [...] is not driven by any research questions and objectives. Without finely tuning each method, especially for the sophisticated CNN models, it is unclear why to compare these methods and also the very similar results for each method give readers very limited insights from their studies.“
  The referee‘s critique falls into three parts, which we rephrase to our understanding and respond to as follows:
  1. The study lacks research questions and objectives.
  The overall objective of this study is to link the occurence of impact-relevant convective rainfall events to the large scale ciculation, and to explore this link in order to quantify changes in the frequency of such events under past and future climate conditions. The specific research questions following from this objective are: (i) is there a sufficiently stable link between convective heavy rainfall events, as represented by the recently published event catalogue CatRaRE, and indices of the prevailing convective atmosphere? (ii) which set of indices should be chosen? (iii) do conventional methods still stand the challenge to compete with up-to-date deep learning (DL) methods with regard to the previous question? (iv) if we can establish such a link and apply it to simulated atmospheres, how does past and future climate affect the frequency of such impact-relevant convective rainfall events?
  In our view, these research questions are relevant and, to date, not sufficiently addressed in the scientific literature. So while we disagree with the referee‘s notion, we admit that the objectives and research questions could be elaborated more concisely, and we will revise the manuscript accordingly.
  2. The results pertaining to the DL models are inconclusive because their application requires more fine-tuning.
  With regard to DL tuning, we have described that we purposefully used the basic model structure as is from the corresponding image recognition tasks, but fine-tuned settings to achieve convergence in the learning curves. This was successful, and the residual uncertainty is likely a genuine one. This topic is widely discussed in the DL community (cf. https://machinelearningmastery.com/randomness-in-machine-learning) and represents in itself an interesting scientific result for which we have not found any reference in the corresponding literature. Our dealing with randomness is exactly as proposed in the above-linked document. We will discuss the issue more explicitly in the revised version of the manuscript.
  3. The results are not relevant to the reader because the performance of all benchmarked models is very similar.
  First, Fig. 4 clearly shows that the models are not all very similar. Second, we see the comprehensive assessment of a large collection of methods as part of a transparent and objective methodological approach, and also as a service to the scientific community in order to identfy promising model architectures. While it may seem more exciting if a model had emerged as a clear „winner“, we do not see why lacking such an obvious discrimination should make our results less relevant."
  
  Citation: https://doi.org/10.5194/egusphere-2022-1159-AC2
EC1: 'Comment on egusphere-2022-1159', Andreas Hense, 22 Feb 2023

There are now two reviews available: Reviewer 1 suggest major revisions based on an extensive lists of suggestions, Reviewer 2 is more pessimistic and suggests rejection but leaves open the possibility to declare in more details the scientific questions behind the described approaches. As responsible handling editor I do read this as a major major revision, which I would like to be incorporated into a revised version of the manuscript.
Therefore I am looking forward to receive first the authors comments and then a revised version acc to the suggestions by both reviewers.

Citation: https://doi.org/10.5194/egusphere-2022-1159-EC1

Interactive discussion

Status: closed

RC1:
'Comment on egusphere-2022-1159', Anonymous Referee #1, 28 Nov 2022
In their manuscript, Bürger and Heistermann trained and applied multiple ML/DL models to the (binary) classification task of detecting convectively enforced extreme rainfall in Germany. They compare a whole range of different models ranging from regression models through random forests and shallow neural nets to well-known DL models for image classification like AlexNet, GoogLeNet or ResNet. The study's classification task can be formulated as follows: Given the (daily aggregated) ERA5 fields of CAPE, convective rainfall, and total column water over Germany, does CatRaRE contain at least one event exceeding warning level 3 where the event does not exceed nine hours. Bürger and Heistermann used cross-entropy as the loss function during training and evaluated their final results based on the Equitable Threat Score. As their DL models use a stochastic gradient descent algorithm during the optimisation, they trained 20 individual realisations per DL model resulting in an ensemble. Based on that (simplistic) ensemble, they report that the ALL-CNN model shows the highest mean ETS (across the ensemble) of .52. At the same time, the ResNet architecture provides the ensemble member with the highest individual ETS (.54).

The manuscript is well structured, and I appreciate the extensive model selection used for comparison and acknowledge the effort spent to train all of these. Even though the intercomparison of different methods and architectures is interesting on its own, I have difficulties distilling the overall relevance (concrete use case) of the classification for meteorological applications. I have some more points of concern, as listed below, but I am confident that the authors can adequately address them and that the manuscript can provide an important contribution to the field. I wonder if the GMD/ESSD inter-Journal SI "Benchmark datasets and machine learning algorithms for Earth system science data" (https://gmd.copernicus.org/articles/special_issue386_1147.html) might be suited better to the manuscript's scope. At least for me, the study's focus lies more on the intercomparison aspect rather than the "monitoring of precursors of evolution".

Major Comments

As mentioned above, it does not become clear to me what consequences a statement like "There is an extreme convective event (somewhere) over Germany" might have for a meteorologist, climatologist or decision-maker. L 229f somehow reflects the ultimate goal; however, it might be good to further distil the gain also in the introduction.

I wonder how a cross-entropy or ETS analysis might contribute to a better understanding of the influence of 'deep' in DL models, as stated in l. 41f. For such a statement, I would have expected some explainable AI (XAI) methods or some sensitivity analysis of each model type, like varying the number of inception blocks in the 'GoogLeNet-style' model. Here the introduction raises expectations that the conclusion does not reflect.

As far as I understand, you are using ERA5 data (cape, cp, tcw) as input X and CatRaRE as target y for training (2001-2010) and validation (2011-2020). Finally, you apply the trained model to data from HIST and RCP85. In l 144, you correctly state that the second dataset is not independent of the DL models, as you use those for model selection. As overfitting can happen on both - parameters (training set) and hyperparameters (validation set), why do you not split your data into three sets (training, validation, test)? Especially as you apply the trained models to data from different sources that likely have different properties, I think it would be beneficial to compare the test set's performance against the same (sub-)period of RCP85. Thus, you could detect differences in model performance that might serve as a guide towards interpreting all RCP85 data where you do not have any labels.

I suggest broadening the analysis of the predicted probabilities over the entire detection period. For example, replacing Fig. 5 with a reliability diagram where the predicted probability is plotted against the observed relative frequency might reveal model-specific differences.

Given the close range of ETS values across the different models, I suggest providing uncertainty quantifications and/or statistical tests to demonstrate the significance of your findings.

How do already existing 'classical' findings of the expected change of extreme precipitation align with your classification results? Can you discuss the concept drift in the data that the classifier faces?

In that regard, which period do you use to calculate the mean and std for the z-transformation?

Minor Comments

L. 22ff Besides the references to the 'classical' DL introductions, I encourage the authors to also focus on the recent discussions on ML/DL applications in atmospheric sciences like Reichstein et al. (2019) and Schultz et al. (2021).

L. 158f How do you analyse the influence of cape? In l. 126 you state that you are using cape, cp and tcw as channels similar to RGB. Please clarify how you create the "non-cape" classifications. Do you train the models with two channels only? Do you replace the cape channel with zeros or another variable?

Fig. 1 shows cape values jointly with the CatRaRe events used to define the extreme labels. The selected model domain contains pixels outside of Germany. CatRaRE, however, covers Germany only. Did you check (most likely with some other dataset) how often (if at all) extreme events occur outside of Germany but within your defined model domain? For me, that seems to be a potential source of introducing labelling errors.

Fig. 4: I suggest using a more colourblind-friendly palette.

Even though Table S1 lists several tuned hyperparameters, how does the learning rate change under the poly policy?

I suggest adding a column reporting the number of trainable parameters of your modified versions

Did you consider also using architectures already focussing on precipitation (for example (your) RainNet model (Ayzel et al., 2020)) and adjusting details for your classification task?

L. 59 I am wondering if a log transformation for cp before applying the standardisation might be beneficial

Please provide some more details on the EOF reduction. For example, how many components are you using?

From the first sentence in your abstract, I expect this manuscript to focus on creating a new data set that can be used for ML/DL applications. In its current state, the abstract does not adequately transport the enormous (DL-)model comparison you performed.

Formal Comments

As a reviewer, I was asked helping to ensure that manuscripts comply with the journal's guidelines. Therefore I'd like to point out some formal aspects:

Please add a "competing interests" statement as required by Copernicus Publication (see https://www.natural-hazards-and-earth-system-sciences.net/submission.html#manuscriptcomposition §16)

Software Code: You refer to your GitHub repository but to the best of my knowledge Copernicus Journals prefer software provided through a DOI (e.g. through zenodo)

URLs: Please add the last access dates to all URLs

A legend is missing in Fig. 3

References

Ayzel, G., Scheffer, T., and Heistermann, M.: RainNet v1.0: a convolutional neural network for radar-based precipitation nowcasting, Geoscientific Model Development, 13, 2631–2644, https://doi.org/10.5194/gmd-13-2631-2020, number: 6, 2020.

Reichstein, M., Camps-Valls, G., Stevens, B., Jung, M., Denzler, J., Carvalhais, N., and Prabhat: Deep learning and process understanding for data-driven Earth system science, Nature, 566, 195–204, https://doi.org/10.1038/s41586-019-0912-1, 2019.

Schultz, M. G., Betancourt, C., Gong, B., Kleinert, F., Langguth, M., Leufen, L. H., Mozaffari, A., and Stadtler, S.: Can deep learning beat numerical weather prediction?, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 379, 20200 097, https://doi.org/10.1098/rsta.2020.0097, 2021.
Citation: https://doi.org/10.5194/egusphere-2022-1159-RC1
- AC1: 'Reply on RC1', Gerd Bürger, 10 Jan 2023
  
  see pdf.
  
  Citation: https://doi.org/10.5194/egusphere-2022-1159-AC1
RC2:
'Comment on egusphere-2022-1159', Anonymous Referee #2, 21 Feb 2023

The authors applied a series of classification approaches to classify ERA5 data to binary catalogues of convective precipitation indices with an existing catalogue data (CatRaRE) as target. The series of classification approaches include several conventional methods (Lasso, random forests, logistic, and shallow neural network) and more sophisticated CNN based approaches that have been used in computer vision area (LeNet-5, AlexNet, ResNet, and so on). All the models are trained with the daily data from 2001 to 2010 and validated with the data from 2011 to 2020. The trained models were also applied to predict extreme indices in the future with future projections from GCM data.
The manuscript is relatively extensive. However, it basically lays out all the methods and is not driven by any research questions and objectives. Without finely tuning each method, especially for the sophisticated CNN models, it is unclear why to compare these methods and also the very similar results for each method give readers very limited insights from their studies. The manuscript appears more like a machine learning exercise instead of scientific research and it is not suitable for publication in this research journal.

Citation: https://doi.org/10.5194/egusphere-2022-1159-RC2
- AC2: 'Reply on RC2', Gerd Bürger, 28 Feb 2023
  
  We would like to thank the referee for taking the time to review our manuscript, and for the open and clear criticism. The central part of the comment is, as we understand, as follows:
  „[…] [the manuscript] basically [...] is not driven by any research questions and objectives. Without finely tuning each method, especially for the sophisticated CNN models, it is unclear why to compare these methods and also the very similar results for each method give readers very limited insights from their studies.“
  The referee‘s critique falls into three parts, which we rephrase to our understanding and respond to as follows:
  1. The study lacks research questions and objectives.
  The overall objective of this study is to link the occurence of impact-relevant convective rainfall events to the large scale ciculation, and to explore this link in order to quantify changes in the frequency of such events under past and future climate conditions. The specific research questions following from this objective are: (i) is there a sufficiently stable link between convective heavy rainfall events, as represented by the recently published event catalogue CatRaRE, and indices of the prevailing convective atmosphere? (ii) which set of indices should be chosen? (iii) do conventional methods still stand the challenge to compete with up-to-date deep learning (DL) methods with regard to the previous question? (iv) if we can establish such a link and apply it to simulated atmospheres, how does past and future climate affect the frequency of such impact-relevant convective rainfall events?
  In our view, these research questions are relevant and, to date, not sufficiently addressed in the scientific literature. So while we disagree with the referee‘s notion, we admit that the objectives and research questions could be elaborated more concisely, and we will revise the manuscript accordingly.
  2. The results pertaining to the DL models are inconclusive because their application requires more fine-tuning.
  With regard to DL tuning, we have described that we purposefully used the basic model structure as is from the corresponding image recognition tasks, but fine-tuned settings to achieve convergence in the learning curves. This was successful, and the residual uncertainty is likely a genuine one. This topic is widely discussed in the DL community (cf. https://machinelearningmastery.com/randomness-in-machine-learning) and represents in itself an interesting scientific result for which we have not found any reference in the corresponding literature. Our dealing with randomness is exactly as proposed in the above-linked document. We will discuss the issue more explicitly in the revised version of the manuscript.
  3. The results are not relevant to the reader because the performance of all benchmarked models is very similar.
  First, Fig. 4 clearly shows that the models are not all very similar. Second, we see the comprehensive assessment of a large collection of methods as part of a transparent and objective methodological approach, and also as a service to the scientific community in order to identfy promising model architectures. While it may seem more exciting if a model had emerged as a clear „winner“, we do not see why lacking such an obvious discrimination should make our results less relevant."
  
  Citation: https://doi.org/10.5194/egusphere-2022-1159-AC2
EC1: 'Comment on egusphere-2022-1159', Andreas Hense, 22 Feb 2023

There are now two reviews available: Reviewer 1 suggest major revisions based on an extensive lists of suggestions, Reviewer 2 is more pessimistic and suggests rejection but leaves open the possibility to declare in more details the scientific questions behind the described approaches. As responsible handling editor I do read this as a major major revision, which I would like to be incorporated into a revised version of the manuscript.
Therefore I am looking forward to receive first the authors comments and then a revised version acc to the suggestions by both reviewers.

Citation: https://doi.org/10.5194/egusphere-2022-1159-EC1

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) (08 Mar 2023) by Andreas Hense

AR by Gerd Bürger on behalf of the Authors (18 Apr 2023) Author's response Author's tracked changes Manuscript

ED: Reconsider after major revisions (further review by editor and referees) (27 Apr 2023) by Andreas Hense

ED: Referee Nomination & Report Request started (28 Apr 2023) by Andreas Hense

RR by Anonymous Referee #1 (30 May 2023)

Suggestions for revision or reasons for rejection

I want to thank the authors for their time and effort in revising their manuscript. While the authors adequately addressed most of my previous remarks, there are still some open issues, so I'd like to ask for more clarification. I still have some mixed feelings regarding a recommendation for publishing. Those mixed feelings are primarily due to the "unclear" study motivation (see below), making it hard to distil scientific significance. Jointly with the gap to state-of-the-art models, which become easily accessible through, for example, Hugging Face (https://huggingface.co/) I would recommend a major revision of the study's motivation.
Please note that I don't see the gap to the latest methods as an exclusion criterion for publication, but the motivation and, thus, the significance of the study must be better and more clearly explained.

Study's motivation:
I have some difficulties following your revised motivational paragraphs. I know that answers from the discussion phase might develop further until the revised version is submitted, especially when larger parts are modified or extended. However, in its current state, the focus lies more on what the study is not, instead of -for example - the occurrence-frequency modelled by the different approaches, as you mentioned as an answer in the discussion phase. Moreover, I think that statements about further studies (l. 69) are better suited in the discussion/outlook section rather than as a motivation for your current study.

L70f Even though you present your interpretation of model architecture etc. (lines 194-212), I find the phrasing of the influence misleading, as I would still expect some XAI /sensitivity analysis later on.

L190ff: Did you consider using a SHAPE-value (Lundberg and Lee, 2017) analysis for your (shallow) models?

L176f What are those optimal thresholds? How did you determine them, and do they vary between models?

L176f Did you also consider using the Brier Score, which directly accesses forecasted probabilities?
Fig. 1.: I can not fully follow your line of argumentation, so I am trying to clarify: I fully agree that the German border should not have any influence on the classification itself (especially the CNN-based classification models are in search of whether a specific pattern (extreme event) is present or not. However, my point of concern was related to the initial labelling. You are using CatRaRe events within Germany for labelling the selected ERA5 domain that also covers parts outside of Germany. So as far as I understand the labelling procedure, an extreme event occurring in - say, in the western parts of the Czech Republic would not be present in CatRaRe, and thus it would be labelled as "not extreme". As you are aiming to solve a classification task and not an object detection task (creating something like a bounding box, see, for example, YOLO (https://arxiv.org/pdf/1506.02640v5.pdf), the same or at least very similar ERA5 patterns (independent of their geographic position) might be labelled differently, which in turn, might negatively affect the classification in the next step. Have you looked closely into events where your models predicted an extreme that wasn't observed?

L242 I need clarification on which additional information is conveyed by Fig. 7, especially as the mentioned (see above) optimal probability thresholds are not shown. Is the intention showing the predicted high probability on the 29th of July? But what happens if I select the 25th(?) of July (forecasted probability ~0.5) and the 7th(?) of August (forecasted probability ~0.6)? Given the identical probability threshold, one event would be misclassified in that example. Did you intend to somehow cross-check for detecting the "most famous" convective extreme event(s)? If so, please clarify. Otherwise, one might get the impression of cherry-picking.

References:

Lundberg, S. M. and Lee, S.-I.: A Unified Approach to Interpreting Model Predictions, in: Advances in Neural Information Processing Systems 30 (NeurIPS 2017 proceedings), edited by: Guyon, I., Luxburg, U. V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., and Garnett, R., 4765–4774, Long Beach, CA, USA, http://papers.nips.cc/paper/7062-a-unified-approach-to-interpreting-model-predictions.pdf, 2017

Hide

RR by Anonymous Referee #3 (15 Jun 2023)

ED: Publish subject to minor revisions (review by editor) (19 Jun 2023) by Andreas Hense

ED: Reconsider after major revisions (further review by editor and referees) (20 Jun 2023) by Andreas Hense

AR by Gerd Bürger on behalf of the Authors (26 Jul 2023) Author's response Author's tracked changes Manuscript

ED: Publish subject to technical corrections (13 Aug 2023) by Andreas Hense

AR by Gerd Bürger on behalf of the Authors (14 Aug 2023) Manuscript

Journal article(s) based on this preprint

18 Sep 2023

Shallow and deep learning of extreme rainfall events from convective atmospheres

Gerd Bürger and Maik Heistermann

Nat. Hazards Earth Syst. Sci., 23, 3065–3077, https://doi.org/10.5194/nhess-23-3065-2023,https://doi.org/10.5194/nhess-23-3065-2023, 2023

Short summary

Gerd Bürger and Maik Heistermann

Supplement

https://doi.org/10.5194/egusphere-2022-1159-supplement

Gerd Bürger and Maik Heistermann

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Total: 630
Supplement: 51
BibTeX: 4
EndNote: 6

Views and downloads (calculated since 07 Nov 2022)

Month	HTML	PDF	XML	Total
Nov 2022	154	52	7	213
Dec 2022	43	11	0	54
Jan 2023	50	17	2	69
Feb 2023	58	20	4	82
Mar 2023	37	13	2	52
Apr 2023	29	23	1	53
May 2023	15	8	0	23
Jun 2023	14	8	1	23
Jul 2023	18	12	3	33
Aug 2023	7	5	0	12
Sep 2023	7	9	0	16
Oct 2023	0
Nov 2023	0
Dec 2023	0

Cumulative views and downloads (calculated since 07 Nov 2022)

Month	HTML	PDF	XML	Total
Nov 2022	154	52	7	213
Dec 2022	43	11	0	54
Jan 2023	50	17	2	69
Feb 2023	58	20	4	82
Mar 2023	37	13	2	52
Apr 2023	29	23	1	53
May 2023	15	8	0	23
Jun 2023	14	8	1	23
Jul 2023	18	12	3	33
Aug 2023	7	5	0	12
Sep 2023	7	9	0	16
Oct 2023	0
Nov 2023	0
Dec 2023	0

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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.

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Short summary

Our subject is a new Catalogue of radar-based heavy Rainfall Events (CatRaRE) over Germany, and how it relates to the concurrent atmospheric circulation. We classify daily atmospheric ERA5 fields of convective indices according to CatRaRE, using conventional statistical and more recent machine learning algorithms, and apply them to present and future atmospheres. Increasing trends are predicted for the probability of CatRaRE-type events, from ERA5 as well as from the CORDEX fields.


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