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

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)

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<0437:TNYRP>2.0.CO;2

NCEP/NCAR Reanalysis 1: Summary Earth System Research Laboratory


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.