A statistical approach for rain intensity differentiation using Meteosat Second Generation–Spinning Enhanced Visible and InfraRed Imager observations

Ricciardelli, E.; Cimini, D.; Di Paola, F.; Romano, F.; Viggiano, M.

doi:https://doi.org/10.5194/hess-18-2559-2014

Articles | Volume 18, issue 7

https://doi.org/10.5194/hess-18-2559-2014

© Author(s) 2014. This work is distributed under
the Creative Commons Attribution 3.0 License.

https://doi.org/10.5194/hess-18-2559-2014

© Author(s) 2014. This work is distributed under
the Creative Commons Attribution 3.0 License.

Articles | Volume 18, issue 7

Research article

|

10 Jul 2014

Research article |

| 10 Jul 2014

A statistical approach for rain intensity differentiation using Meteosat Second Generation–Spinning Enhanced Visible and InfraRed Imager observations

E. Ricciardelli, D. Cimini, F. Di Paola, F. Romano, and M. Viggiano

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Final revised paper (published on 10 Jul 2014)
Preprint (discussion started on 12 Nov 2013)

Interactive discussion

Status: closed

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

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- Supplement

RC C6759: 'Referee comments', Anonymous Referee #1, 16 Dec 2013
- AC C7760: 'Response to anonymous referee#1', Elisabetta Ricciardelli, 04 Feb 2014
RC C6794: 'Interactive comment on “A statistical approach for rain class evaluation using Meteosat Second Generation-Spinning Enhanced Visible and InfraRed Imager observations” by E. Ricciardelli et al.', Anonymous Referee #2, 17 Dec 2013
- AC C7761: 'Response to anonymous referee#2', Elisabetta Ricciardelli, 04 Feb 2014
RC C7019: 'reviewer comments', Anonymous Referee #3, 02 Jan 2014
- AC C7762: 'Response to anonymous referee#3', Elisabetta Ricciardelli, 04 Feb 2014

Peer-review completion

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

ED: Reconsider after major revisions (13 Feb 2014) by Carlo De Michele

AR by Elisabetta Ricciardelli on behalf of the Authors (27 Mar 2014) Author's response Manuscript

ED: Referee Nomination & Report Request started (08 Apr 2014) by Carlo De Michele

RR by Anonymous Referee #2 (11 Apr 2014)

RR by Anonymous Referee #1 (21 Apr 2014)

Suggestions for revision or reasons for rejection

I appreciated that the authors took into account all the issues that I remarked, but unfortunately I think that the paper is still not suitable for publication. A great effort has been done to try to improve the paper but it is still lacking in sharpness particularly in the algorithm description.

1. English should be improved.

2. Introduction.
This section was improved by focusing the bibliographic review on geostationary and particularly on SEVIRI applications in the field of rain rate retrieval. Nevertheless the authors still omitted to highlight the strengths of their methodology with respect to other similar products. It is not clear which gaps are filled by the presented technique. If a key-point of this algorithm is that it does not need any real-time ancillary data, while this is not true for other similar products, the authors must specify this concept clearly in the Introduction. The authors also indicate the use of WV channel brightness temperatures in “temporal differences” as another key factor of their algorithm (this concept is also cited in the Conclusions), but in this case the benefits produced by this channel has to be demonstrated.

3. Instruments and data description. Page 5 – line 2.
From EUMETSAT Image newsletter MSG-4 launch is scheduled for February 2015, not 2014.

4. Instruments and data description. Page 6 – lines 4-21.
The references cited to describe Italian Weather Radar Network data (Vulpiani et al., 2008, 2008a, 2012; Rinollo et al., 2013; Puca et al., 2013) were not included in the bibliography.

5. Cloud classification algorithm description. Page 7-9
The authors improved the description of C_MACSP module, by including a citation of the paper Ricciardelli et al., 2008 for the description of the previous version of the module, a very brief (completely non-exhaustive) presentation of the 6.2 – 10.8 BTD test used to identify the presence of thick clouds with high tops, and the description of the training dataset arrangement (see also Appendix 1). Nevertheless at the end of this section readers do not yet know which threshold tests are exploited within the C_MACSP module.
Moreover it is not clear to me in which way the C_MACSP module associates to each SEVIRI pixel a cloud class among the five available classes. Does it use the k-NN method?

6. Features selection and description. Page 10 – line 26.
I think that it is more appropriate to replace “cloud drop distribution” with “cloud effective radius”, which is the parameter that can be retrieved together with cloud phase from SEVIRI channels centred at 1.6 and 3.9 μm.

7. Training procedure
In my opinion the comprehension of this section is still difficult despite of the revised text, in particular the bootstrap sample construction.

8. RainCEIV validation results
Table 8 with the list of the case studies for the validation was never cited in the text. There are two Table 10, the first one is the contingency table (rain/no-rain) for night-time pixels, the second one summarizes the statistical scores for the C1 and C2 classes and it was never cited in the text.

9. Conclusions
The exploitation of VIIRS data is identified as a method to individuate very localized (size smaller than the SEVIRI pixel) extreme rainy events. The authors could add further details about this topic.

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RR by Anonymous Referee #3 (22 Apr 2014)

ED: Publish subject to minor revisions (Editor review) (04 May 2014) by Carlo De Michele

AR by Elisabetta Ricciardelli on behalf of the Authors (09 May 2014) Author's response Manuscript

ED: Publish as is (30 May 2014) by Carlo De Michele

AR by Elisabetta Ricciardelli on behalf of the Authors (04 Jun 2014)