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

Gerlitz, Lars; Vorogushyn, Sergiy; Apel, Heiko; Gafurov, Abror; Unger-Shayesteh, Katy; Merz, Bruno

doi:https://doi.org/10.5194/hess-20-4605-2016

Articles | Volume 20, issue 11

https://doi.org/10.5194/hess-20-4605-2016

© Author(s) 2016. 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-20-4605-2016

© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.

Articles | Volume 20, issue 11

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, Sergiy Vorogushyn, Heiko Apel, Abror Gafurov, Katy Unger-Shayesteh, and Bruno Merz

Supplement

https://doi.org/10.5194/hess-20-4605-2016-supplement

Data sets

T. M. Smith, R. W. Reynolds, T. C. Peterson, and J. Lawrimore Improvements to NOAA’s Historical Merged Land–Ocean Surface Temperature Analysis (1880–2006) https://doi.org/10.1175/2007JCLI2100.1

Kalnay, M. Kanamitsu, R. Kistler, W. Collins, D. Deaven, L. Gandin, M. Iredell, S. Saha, G. White, J. Y. Woollen, A. Leetmaa, R. Reynolds, M. Chelliah, W. Ebisuzaki, W. Higgins, J. Janowiak, K. C. Mo, C. Ropelewski, J. Wang, and R. Jenne The NCEP/NCAR 40-Year Reanalysis Project https://doi.org/10.1175/1520-0477(1996)077<0437:TNYRP>2.0.CO;2

NCEP/NCAR Reanalysis 1: Summary Earth System Research Laboratory http://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanalysis.html

STANDARDIZED NORTHERN HEMISPHERE TELECONNECTION INDICES (1981-2010 Clim) NOAA ftp://ftp.cpc.ncep.noaa.gov/wd52dg/data/indices/tele_index.nh

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.