Does objective cluster analysis serve as a useful precursor to seasonal precipitation prediction at local scale? Application  to western Ethiopia

Zhang, Ying; Moges, Semu; Block, Paul

doi:https://doi.org/10.5194/hess-22-143-2018

Articles | Volume 22, issue 1

https://doi.org/10.5194/hess-22-143-2018

© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 3.0 License.

Special issue:

Sub-seasonal to seasonal hydrological forecasting

https://doi.org/10.5194/hess-22-143-2018

© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 3.0 License.

Articles | Volume 22, issue 1

Research article

|

09 Jan 2018

Research article |

| 09 Jan 2018

Does objective cluster analysis serve as a useful precursor to seasonal precipitation prediction at local scale? Application to western Ethiopia

Ying Zhang, Semu Moges, and Paul Block

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Final revised paper (published on 09 Jan 2018)
Preprint (discussion started on 21 Feb 2017)

Interactive discussion

Status: closed

AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment

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- Supplement

RC1: 'Review: "Does objective cluster analysis serve as a useful precursor to seasonal precipitation prediction at local scale? Application to western Ethiopia"', Anonymous Referee #1, 01 Mar 2017
- AC2: 'Responses to Comment', Ying Zhang, 17 May 2017
RC2: 'Interactive comment on “Does objective cluster analysis serve as a useful precursor to seasonal precipitation prediction at local scale? Application to western Ethiopia” by Ying Zhang et al.', Anonymous Referee #2, 09 Mar 2017
- AC3: 'Responses to Comment', Ying Zhang, 17 May 2017
EC1: 'Please reply to the referee comments', Q.J. Wang, 08 May 2017
- AC1: 'We are preparing the responses. Thank you.', Ying Zhang, 08 May 2017
- SC1: 'Responses to Comments Submitted', Ying Zhang, 17 May 2017

Peer-review completion

AR: Author's response | RR: Referee report | ED: Editor decision

ED: Reconsider after major revisions (further review by Editor and Referees) (02 Jun 2017) by Q.J. Wang

AR by Anna Wenzel on behalf of the Authors (17 Jul 2017) Author's response

ED: Referee Nomination & Report Request started (20 Jul 2017) by Q.J. Wang

RR by Anonymous Referee #2 (28 Jul 2017)

RR by Anonymous Referee #1 (04 Aug 2017)

Suggestions for revision or reasons for rejection

The revised version of the manuscript by Zhang et al. definitively improved from a methodological point of view. The predictor selection is now based on a procedure, which automatically identifies pixels of large scale climate variables, which significantly correlate with summer precipitation anomalies. In a next step, the pixel anomalies in specific regions are averaged and different variables are combined by means of a pca analysis. PCA timeseries (explaining 95% of the overall variance), are eventually utilized as predictors for a linear regression based prediction. The procedure is now fully cross-validated which results in poor to moderate prediction results.
While the application of the method is solid (beside of some smaller issues, see specific remarks), the results of the statistical forecast model are worse compared with some dynamical models (and sometimes even worse compared with climatology). Beside of two clusters all correlations between observations and cross-validated forecast are below 0.2, which is not statistically significant for a period of 30 years. Thus in my opinion the study is not yet ready for publication in HESS. However, I see quite a big potential of the study from a hydro-climatological point of view. The major question for me is, why there is such a big difference in the skill of the forecast models, although the clusters are very close to each other. This could either be a statistical artefact or it could be explained by large/regional scale climate mechanisms. I believe that the results are robust and physically interpretable (all clusters with moderate forecast skill are windward of the East-African monsoon), but this should be somehow investigated. In the revised manuscript the authors give some more information on the general climate characteristics and also mention, that different cluster regions are correlated with different climate modes and sst patterns. I still feel that this insufficient for the interpretation of the results. I suggest to give a short literature overview of climatic mechanisms in the introduction. Which anomalies (pressure patterns, moisture fluxes etc) trigger precipitation surplus and deficits? How are these patterns related to ENSO and other potential predictors? There is quite a lot literature on the monsoon in general.
What kind of predictors are important for which cluster? Maybe this could eventually explain the different model skills and support the robustness of your models. Further it would definitively justify the clustering procedure. My impression from the gridded analysis is that it doesn’t make a difference?

Minor remarks:
- Fig. 1: Please show boarders and rivers to make orientation easier. The seasonal precipitation amounts might be rather shown as a barplot.
- P3L24 (and others): for me the term “scenario” stands for future climate change assessments. Do you mean similar large scale conditions? Likewise in Sec. 3 the term is misleading.
- There’s a bit more information on the clustering procedure now. However the selection of k is still not clear. WSS is smallest for the largest possible k, right? I see that the authors refer to their previous publication, but very general information should be provided.
- I was a bit lost in the section on the statistical modelling approach. Would it be possible to structure it into predictor selection, model calibration and evaluation?
- Predictor regions: Why are those regions chosen (Maybe a broader literature review could justify the selection). Please also name the regions in the map.
- Predictor selection approach: In step 3, the “best” grid cells are spatially averaged. This is not clear to me. If there are positive and negative correlations in the region, this might average out the predictive skill. For example in the southern Indian Ocean, both positive and negative correlations are detected in Fig. 5. Likewise the North Atlantic SLP domain, which contains not only the Azores (high) but also Iceland (low), might be problematic. Wouldn’t it be straight forward, to use all gridcells directly for the PCA analysis?
- PCA: No information is given on standardization of predictor variables. If different variables are not standardized, the final PCA might be much more affected by SLP (values around 1000hpa) then SST (around 25°C).
- Predictor selection: How many PCA-predictors are used in the linear models at the moment? Are all of them correlated with the predictant? (Theoretically, the stepwise procedure, which the authors tested for the original predictors (author response), could also be used for the PCA based predictors. The predictors used in the linear regression (most likely very few) might then be easier interpretable. Further it might reduce overfitting, which is judged by the cross-validation.)
- P10L18: Please only use the term significant if you conducted a test (otherwise remarkable, great etc)
- Dynamical Models: I am not sure if it’s really necessary to do that comparison and I still feel that it is poorly integrated. The heart of the study is the statistical approach – and I think one can easily argue, that statistical predictions are good for operational use (without comparing with complex statistical models?)
- Cluster Results (Fig. 7) : How is the standardization conducted? The y-axis should be limited to -2/2 to better present the results.
- Precipitation trends (Fig. 7): The two clusters with skillfull models (5 and 7) seem to have positive precipitation trends. This might lead to an overestimation of skill, since every variable with a similar trend could be used as a predictor. Thus I suggest to test the model for detrended time-series.
- P23: The argument that the statistical models are of higher resolution than dynamical models is misleading. One could downscale the results the same way, as it is done with the cluster results. Further (as I mentioned during the first review round), the high resolution (indirect) forecast has the same temporal variability as the cluster forecast (due to the univariate linear relationship) and thus does not contain much additional information. The analysis of different statistical relationships at the cluster-level seem to be more relevant (in my point of view).

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ED: Reconsider after major revisions (further review by Editor and Referees) (09 Aug 2017) by Q.J. Wang

AR by Svenja Lange on behalf of the Authors (20 Sep 2017) Author's response

ED: Referee Nomination & Report Request started (24 Sep 2017) by Q.J. Wang

RR by Anonymous Referee #1 (02 Oct 2017)

Suggestions for revision or reasons for rejection

I had a look at the revised manuscript and I think, that the authors responded professionally to my comments. I only have a few suggestions, but in general I recommend to publish the manuscript in HESS.

Minor comments:

1) I appreciate that the authors give a general overview about the atmospheric mechanisms related with precipitation variability in the target region. For me the link with the southwest Asian monsoon is not trivial. Since it is an important point, as the autors argue in the conclusion, I think a bit more information in the introduction might be helpful.

2) The link with the Monsoon is investigated by means of the correlation of seasonal precipitation amounts with SLP near Bombay. Is this correlation simultanous or does it consider a lead time? If a lead time is considered, is the monsoonal circulation robust, i.e. stable during subsequent months?

3) structure of the introduction and methods section: I would give the climatic overview (atmospheric mechanisms) in the introduction.
In the methods section it would be appropriate, to describe the PCA techniques for the predictor generation first and the calibration of the LRM afterwards.

4) I still assume that that trends in cluster 5 and 7 might lead to better forecast results. The PCAs are usualkly orthogonal, which does not mean that the predictors do not have any trends, Particularly for sst I assume strong trends during recent decades. I understand the point, that 29 years are to short for detrending (since decadal variations such as PDO are not captured otherwise), but I think this should be better communicated in the conclusions.

Technical corrections: (pages and lines are taken from the track change version)
p22, 1st sentence: I do not understand that sentence
p22, l. 36: corrects? Do you mean correlates?
p23,l2: what do you mean by gradient?

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RR by Anonymous Referee #2 (19 Oct 2017)

ED: Publish subject to minor revisions (review by editor) (25 Oct 2017) by Q.J. Wang

AR by Svenja Lange on behalf of the Authors (02 Nov 2017) Author's response

ED: Publish as is (12 Nov 2017) by Q.J. Wang

Short summary

The study proposes advancing local-level seasonal rainfall predictions by first conditioning on regional-level predictions, as defined through cluster analysis. This statistical approach is applied to western Ethiopia, where lives and livelihoods are vulnerable to its high spatial–temporal rainfall variability, particularly given the high reliance on rain-fed agriculture. The statistical model improves in skills versus the non-clustered case or dynamical models for some critical regions.