An improved ARIMA model for precipitation simulations

Wang, H. R.; Wang, C.; Lin, X.; Kang, J.

doi:https://doi.org/10.5194/npg-21-1159-2014

Articles | Volume 21, issue 6

https://doi.org/10.5194/npg-21-1159-2014

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

https://doi.org/10.5194/npg-21-1159-2014

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

Articles | Volume 21, issue 6

Research article

|

01 Dec 2014

Research article |

| 01 Dec 2014

An improved ARIMA model for precipitation simulations

H. R. Wang, C. Wang, X. Lin, and J. Kang

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Final revised paper (published on 01 Dec 2014)
Preprint (discussion started on 30 Apr 2014)

Interactive discussion

Status: closed

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

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- Supplement

RC C317: 'Interesting work, but probably the wrong journal', Reik Donner, 21 Jul 2014
- AC C531: 'Itemized Replies to Dr. R. V. Donner’s comments', Cheng Wang, 27 Sep 2014
  - AC C541: 'Revised manuscript', Cheng Wang, 03 Oct 2014
RC C367: 'Review report Journal: Nonlin. Processes Geophys. Discuss. Manuscript Number: 1, 841-876, 2014 Title: An improved ARIMA model for hydrological simulations Reviewer: Date: 2014-04-05', Fadhilah Yusof, 06 Aug 2014
- AC C533: 'Itemized Replies to Dr. F. Yusof’s Comments', Cheng Wang, 27 Sep 2014

Peer-review completion

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

AR by Cheng Wang on behalf of the Authors (06 Oct 2014) Author's response Manuscript

ED: Publish as is (13 Oct 2014) by Douglas Maraun

AR by Cheng Wang on behalf of the Authors (14 Oct 2014) Manuscript

Short summary

This paper presents an improvement on the conventional ARIMA model for precipitation time-series forecast. The precipitation time series of 12 months is first classified into several clusters. The maxima, minima, and truncation means of each cluster are then predicted using the improved ARIMA models, which are further used to predict the monthly precipitation through a set of regression models. A case study demonstrates that the present approach could increase the forecast accuracy by 21%.