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

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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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Data Availability

Data can be directly downloaded from https://cds.climate.copernicus.eu/.

Abbreviations

ANN:

Artificial Neural Network (or NN for short)

CMCC:

Euro-Mediterranean Center on Climate Change

DT:

Decision Tree

DWD:

Deutscher Wetterdienst

ECMWF:

European Center for Medium-Range Weather Forecasts

EMME:

European Multi-Model Ensemble

MLP:

Multi-Layer Perceptron

MME:

Multi-Model Ensemble

NMME:

North American Multi-Model Ensemble

NS:

Nash−Sutcliffe efficiency

R:

Correlation coefficient

RF:

Random Forest

RMSE:

Root Mean Square Error

SVR:

Support Vector Regression

Met. Fr.:

Meteo France

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Acknowledgements

The authors would like to thank the anonymous reviewers who reviewed the manuscript constructively and Editors for their comments. The authors declare that there is no conflict of interest.

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Authors and Affiliations

  1. Disasters and Climate Change Group, Atmospheric Science and Meteorological Research Center (ASMERC), Climatological Research Institute (CRI), Mashhad, Iran

    Morteza Pakdaman

  2. Applied Climatology Group, Atmospheric Science and Meteorological Research Center (ASMERC), Climatological Research Institute (CRI), Mashhad, Iran

    Zohreh Javanshiri

  3. Climate Modeling and Early warning Group, Atmospheric Science and Meteorological Research Center (ASMERC), Climatological Research Institute (CRI), Mashhad, Iran

    Iman Babaeian & Yashar Falamarzi

Authors
  1. Morteza Pakdaman
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  2. Iman Babaeian
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  3. Zohreh Javanshiri
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  4. Yashar Falamarzi
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Contributions

M. Pakdaman and I. Babaeian contributed to the study conception and design. Z. Javanshiri contributed to statistical concepts and Y. Falamarzi contributed to preparing the maps. The first draft of the manuscript was written by M. Pakdaman and all authors commented on previous versions of the manuscript All authors read and approved the final manuscript.

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Correspondence to Morteza Pakdaman.

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Pakdaman, M., Babaeian, I., Javanshiri, Z. et al. European Multi Model Ensemble (EMME): A New Approach for Monthly Forecast of Precipitation. Water Resour Manage 36, 611–623 (2022). https://doi.org/10.1007/s11269-021-03042-8

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  • DOI: https://doi.org/10.1007/s11269-021-03042-8

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