TY - JOUR
T1 - Multischeme ensemble forecasting of surface temperature using neural network over Turkey
AU - Cakir, Sedef
AU - Kadioglu, Mikdat
AU - Cubukcu, Nihat
PY - 2013/2
Y1 - 2013/2
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84873114930&partnerID=8YFLogxK
U2 - 10.1007/s00704-012-0703-1
DO - 10.1007/s00704-012-0703-1
M3 - Article
AN - SCOPUS:84873114930
SN - 0177-798X
VL - 111
SP - 703
EP - 711
JO - Theoretical and Applied Climatology
JF - Theoretical and Applied Climatology
IS - 3-4
ER -