Multischeme ensemble forecasting of surface temperature using neural network over Turkey

Sedef Cakir*, Mikdat Kadioglu, Nihat Cubukcu

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

9 Citations (Scopus)

Abstract

The ensemble method has long been used to reduce the errors that are caused by initial conditions and/or parameterizations of models in forecasting problems. In this study, neural network (NN) simulations are applied to ensemble weather forecasting. Temperature forecasts averaged over 2 weeks from four different forecasts are used to develop the NN model. Additionally, an ensemble mean of bias-corrected data is used as the control experiment. Overall, ensemble forecasts weighted by NN with feed forward backpropagation algorithm gave better root mean square error, mean absolute error, and same sign percent skills compared to those of the control experiment in most stations and produced more accurate weather forecasts.

Original languageEnglish
Pages (from-to)703-711
Number of pages9
JournalTheoretical and Applied Climatology
Volume111
Issue number3-4
DOIs
Publication statusPublished - Feb 2013

Funding

This research was supported by The Science and Research Council of Turkey (TUBITAK). The FSUGSM data and compiled NCEP Reanalysis were provided by Weather Predict Consulting Inc ( www.weatherpredict.com ). The authors would like to thank Zerefsan Kaymaz, Altug Aksoy, and Samuel Thomas Miller for their valuable review and comments.

FundersFunder number
TUBITAK
The Science and Research Council of Turkey

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