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Abstract
The skill of probability density function (PDF) prediction of summer rainfall over East China using
optimal ensemble schemes is evaluated based on the precipitation data from ˉve coupled atmosphere-ocean
general circulation models that participate in the ENSEMBLES project. The optimal ensemble scheme in
each region is the scheme with the highest skill among the four commonly-used ones: the equally-weighted
ensemble (EE), EE for calibrated model-simulations (Cali-EE), the ensemble scheme based on multiple linear
regression analysis (MLR), and the Bayesian ensemble scheme (Bayes). The results show that the optimal
ensemble scheme is the Bayes in the southern part of East China; the Cali-EE in the Yangtze River valley,
the Yangtze-Huaihe River basin, and the central part of northern China; and the MLR in the eastern part
of northern China. Their PDF predictions are well calibrated, and are sharper than or have approximately
equal interval-width to the climatology prediction. In all regions, these optimal ensemble schemes outperform
the climatology prediction, indicating that current commonly-used multi-model ensemble schemes are able
to produce skillful PDF prediction of summer rainfall over East China, even though more information for
other model variables is not derived.
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Citation
LI Fang. 2011: Probabilistic Seasonal Prediction of Summer Rainfall over East China Based on Multi-Model Ensemble Schemes. Journal of Meteorological Research, 25(3): 283-292. DOI: 10.1007/s13351-011-0304-4
LI Fang. 2011: Probabilistic Seasonal Prediction of Summer Rainfall over East China Based on Multi-Model Ensemble Schemes. Journal of Meteorological Research, 25(3): 283-292. DOI: 10.1007/s13351-011-0304-4
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LI Fang. 2011: Probabilistic Seasonal Prediction of Summer Rainfall over East China Based on Multi-Model Ensemble Schemes. Journal of Meteorological Research, 25(3): 283-292. DOI: 10.1007/s13351-011-0304-4
LI Fang. 2011: Probabilistic Seasonal Prediction of Summer Rainfall over East China Based on Multi-Model Ensemble Schemes. Journal of Meteorological Research, 25(3): 283-292. DOI: 10.1007/s13351-011-0304-4
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