Articles | Volume 20, issue 11
Hydrol. Earth Syst. Sci., 20, 4605–4623, 2016
https://doi.org/10.5194/hess-20-4605-2016

Special issue: Sub-seasonal to seasonal hydrological forecasting

Hydrol. Earth Syst. Sci., 20, 4605–4623, 2016
https://doi.org/10.5194/hess-20-4605-2016

Research article 17 Nov 2016

Research article | 17 Nov 2016

A statistically based seasonal precipitation forecast model with automatic predictor selection and its application to central and south Asia

Lars Gerlitz et al.

Download

Interactive discussion

Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
Printer-friendly Version - Printer-friendly version Supplement - Supplement

Peer-review completion

AR: Author's response | RR: Referee report | ED: Editor decision
ED: Reconsider after major revisions (15 Jun 2016) by Q.J. Wang
AR by Lars Gerlitz on behalf of the Authors (17 Aug 2016)  Author's response    Manuscript
ED: Publish subject to minor revisions (Editor review) (30 Aug 2016) by Q.J. Wang
AR by Lars Gerlitz on behalf of the Authors (09 Sep 2016)  Author's response    Manuscript
ED: Publish as is (23 Sep 2016) by Q.J. Wang
AR by Lars Gerlitz on behalf of the Authors (10 Oct 2016)  Author's response    Manuscript
Download
Short summary
Most statistically based seasonal precipitation forecast models utilize a small set of well-known climate indices as potential predictor variables. However, for many target regions, these indices do not lead to sufficient results and customized predictors are required for an accurate prediction. This study presents a statistically based routine, which automatically identifies suitable predictors from globally gridded SST and climate variables by means of an extensive data mining procedure.