Post-processing of the North American multi-model ensemble for monthly forecast of precipitation based on neural network models
| Autor: | Z. Javanshiri, Yashar Falamarzi, Morteza Pakdaman, Iman Babaeian |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Atmospheric Science
010504 meteorology & atmospheric sciences Artificial neural network Computer science business.industry Rank (computer programming) 0207 environmental engineering 02 engineering and technology Machine learning computer.software_genre Perceptron 01 natural sciences Ranking Artificial intelligence Precipitation 020701 environmental engineering business computer 0105 earth and related environmental sciences |
| Zdroj: | Theoretical and Applied Climatology. 141:405-417 |
| ISSN: | 1434-4483 0177-798X |
| DOI: | 10.1007/s00704-020-03211-6 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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