Similarity-based multi-model ensemble approach for 1–15-day advance prediction of monsoon rainfall over India
The southwest (SW) monsoon season (June, July, August and September) is the major period of rainfall over the Indian region. The present study focuses on the development of a new multi-model ensemble approach based on the similarity criterion (SMME) for the prediction of SW monsoon rainfall in the extended range. This approach is based on the assumption that training with the similar type of conditions may provide the better forecasts in spite of the sequential training which is being used in the conventional MME approaches. In this approach, the training dataset has been selected by matching the present day condition to the archived dataset and days with the most similar conditions were identified and used for training the model. The coefficients thus generated were used for the rainfall prediction. The precipitation forecasts from four general circulation models (GCMs), viz. European Centre for Medium-Range Weather Forecasts (ECMWF), United Kingdom Meteorological Office (UKMO), National Centre for Environment Prediction (NCEP) and China Meteorological Administration (CMA) have been used for developing the SMME forecasts. The forecasts of 1–5, 6–10 and 11–15 days were generated using the newly developed approach for each pentad of June–September during the years 2008–2013 and the skill of the model was analysed using verification scores, viz. equitable skill score (ETS), mean absolute error (MAE), Pearson’s correlation coefficient and Nash–Sutcliffe model efficiency index. Statistical analysis of SMME forecasts shows superior forecast skill compared to the conventional MME and the individual models for all the pentads, viz. 1–5, 6–10 and 11–15 days.
KeywordsMulti-model Southwest monsoon TIGGE Similarity Prediction
Acknowledgement goes to the THORPEX Interactive Grand Global Ensemble (TIGGE) (http://tigge-portal.ecmwf.int) for providing the model output, and Indian Meteorological Department for providing rainfall data that have been used in the present study. Authors acknowledge the anonymous reviewers and the editor of the journal for their valuable comments and suggestions for improving the manuscript.
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