European Multi Model Ensemble (EMME): A New Approach for Monthly Forecast of Precipitation

Morteza Pakdaman (), Iman Babaeian, Zohreh Javanshiri and Yashar Falamarzi
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Morteza Pakdaman: Climatological Research Institute (CRI)
Iman Babaeian: Climatological Research Institute (CRI)
Zohreh Javanshiri: Climatological Research Institute (CRI)
Yashar Falamarzi: Climatological Research Institute (CRI)

Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), 2022, vol. 36, issue 2, No 11, 623 pages

Abstract: Abstract Regarding the ability of data mining algorithms for post-processing the output of climate models, and on the other hand, the successful application of multi-model ensemble approaches in climate forecasts, in this paper, some important data mining algorithms are evaluated for the monthly forecast of precipitation over Iran. For this purpose, four European climate models, from DWD, ECMWF, CMCC and Meteo-France, with six lead times, are used to be post-processed by applying four different algorithms including artificial neural networks, support vector regression, decision tree and random forests. Based on the proposed approach, 72 different models are provided for 12 months, each month with six lead times. The approach is applied for the monthly forecast of precipitation over Iran. According to the results, the neural network and random forest methods performed better than the decision tree and the support vector machine. This advantage preserved for all months of the year. Also, the proposed multi-model approach outperformed any of the individual European models.

Keywords: Artificial neural networks; Support vector regression; Random forests; Monthly forecast; Post-processing; Persian Gulf (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (2)

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DOI: 10.1007/s11269-021-03042-8

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