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Short-term Climate Forecast Experiment of Winter Temperature in China Using Canonical Correlation Analysis
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摘要: 利用典型相关分析(CCA)方法建立统计气候预测模型，对我国冬季气温进行了预测试验，采用历史资料独立样本检验的方法，对预报技巧给出合理的评定。结果表明，使用CCA方法对我国冬季气温进行短期气候预测，有一定的预报技巧，对于特定地区和特定时期优选的因子场组合，可以取得较为满意的预报效果。大部分地区的季平均预报时效在2个季以内时，最佳预报相关系数在0.5以上。季平均的预报水平明显高于月平均的预报。海温场是所有因子场中最好的预报因子，不仅单独海温场的预报效果较好，而且与其他因子场组合后的预报水平还可以得到进一步提高。
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关键词:
- 典型相关分析(CCA）;
- 预报试验;
- 相关系数
Abstract: By using the mulativariate linear statistical climate forecast model developed from canonical correlation analysis (CCA), a forecast experiment for winter temperature in China is carried out. Forecast skill is assessed using the test method of historical data independent samples. The results show that there is specified statistical predictive skill for short-term climatic prediction of winter temperature in China using CCA method. In most areas the best correlation coefficient between forecasts and observed values at lead times of 0~2 seasons is more than 0.5. The mean seasonal prediction is more effective than mean monthly one in general. The sea surface temperature is the most effective predictor field. The SST as predictor field can get higher skill score than other single one, and SST combined with other predictors would get much higher skill score -
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