A Hybrid Dynamical-Statistical Approach for Predicting Winter Precipitation over Eastern China |
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| Authors: | Xianmei Lang |
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| Institution: | International Center for Climate and Environment Sciences, Institute of Atmospheric Physics,Chinese Academy of Sciences, Beijing 100029 |
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| Abstract: | Correlation analysis revealed that winter precipitation in six regions of eastern China is closely related not only to preceding
climate signals but also to synchronous atmospheric general circulation fields. It is therefore necessary to use a method
that combines both dynamical and statistical predictions of winter precipitation over eastern China (hereinafter called the
hybrid approach). In this connection, seasonal real-time prediction models for winter precipitation were established for the
six regions. The models use both the preceding observations and synchronous numerical predictions through a multivariate linear
regression analysis. To improve the prediction accuracy, the systematic error between the original regression model result
and the corresponding observation was corrected. Cross-validation analysis and real-time prediction experiments indicate that
the prediction models using the hybrid approach can reliably predict the trend, sign, and interannual variation of regionally
averaged winter precipitation in the six regions of concern. Averaged over the six target regions, the anomaly correlation
coefficient and the rate with the same sign of anomaly between the cross-validation analysis and observation during 1982–2008
are 0.69 and 78%, respectively. This indicates that the hybrid prediction approach adopted in this study is applicable in
operational practice. |
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| Keywords: | winter precipitation dynamical and statistical predictions multivariate linear regression analysis seasonal prediction model hybrid approach |
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