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Water Resources Management

Published in:

17-01-2022

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

Authors: Morteza Pakdaman, Iman Babaeian, Zohreh Javanshiri, Yashar Falamarzi

Published in: Water Resources Management | Issue 2/2022

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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.

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Title: European Multi Model Ensemble (EMME): A New Approach for Monthly Forecast of Precipitation
Authors: Morteza Pakdaman
Iman Babaeian
Zohreh Javanshiri
Yashar Falamarzi
Publication date: 17-01-2022
Publisher: Springer Netherlands
Published in: Water Resources Management / Issue 2/2022
Print ISSN: 0920-4741
Electronic ISSN: 1573-1650
DOI: https://doi.org/10.1007/s11269-021-03042-8

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