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Title of Journal: Theor Appl Climatol

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Abbravation: Theoretical and Applied Climatology

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Springer Vienna

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DOI

10.1001/jamafacial.2016.0710

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1434-4483

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Multischeme ensemble forecasting of surface temper

Authors: Sedef Cakir Mikdat Kadioglu Nihat Cubukcu
Publish Date: 2012/07/12
Volume: 111, Issue: 3-4, Pages: 703-711
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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 biascorrected 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 forecastsThis 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 wwwweatherpredictcom The authors would like to thank Zerefsan Kaymaz Altug Aksoy and Samuel Thomas Miller for their valuable review and comments

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