Post-processing of the North American multi-model ensemble for monthly forecast of precipitation based on neural network models
Morteza Pakdaman, Yashar Falamarzi,  Iman Babaeian,  Zohreh Javanshiri
 The aim of this paper is to investigate the ability of artificial neural network (ANN) models for post-processing the monthly precipitation forecasts under North American multi-model ensemble (NMME) project and proposing a new multi-model ensemble neural network (MME-NN) model. Monthly precipitation hindcasts of eight models from NMME project are considered in this study. Multi-layer perceptron neural networks are employed for post-processing the output of the models in comparison with PERSIANN-CDR climatology data. Also, utilizing a multi-criteria decision-making approach, NMME models are ranked for each month. The study is implemented over Iran and detailed discussions are provided. The results indicate that the skill of NMME models is different for each month and for each region of the country. Also, it is shown that the neural network outperforms all NMME models for all months. By using the ranking of the models, for each month, the NMME models are ordered based on their skill and a monthly rank is devoted for each model.
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