【摘要】 随着全球变暖极端事件越来越频繁,开展极端事件预测变得非常重要。基于我国700多个地面台站的逐日最低温度观测资料和国家气候中心第一代海气耦合模式的动力预测结果数据,采用逐步回归的统计降尺度方法,建立了一个针对我国冬季极端低温日数的动力-统计降尺度预测方法。结果表明,该预测方法所预测的1983-2010年历史回报结果与实况资料的相关在我国大部均超过了95%的显著性水平。用该预测方法还对2011/2012年冬季极端低温日数进行了实时预测,事实证明该预测方法对2011/2012年冬季极端低温日数的预测趋势基本正确,可以推广到预测业务中应用。更多还原

【Abstract】 Extreme events become more and more frequent under the global warming. It is very important to provide an extreme events prediction. Based on daily minimum temperature data for more than 700 observation stations over China and 1983-2010 winter hindcasts of the first generation atmospheric-ocean coupled model in Beijing Climate Center,a newprediction of extreme winter cold days( EWCD) over China is developed by using the stepwise regression statistical downscaling model( SRSDM). Results showthat the correlation coefficient of EWCD between predictions using SRSDMand observations for 1983-2010 years exceed the 95% significant level in most of China. Moreover,inter-annual variability of EWCD predicted by SRSDMis well agreed with observations during 1983-2010. The realtime prediction of EWCD in 2011 / 2012 using the SRSDMwas carried out. It is very well that the prediction is successfully and the prediction of EWCD by SRSDMin 2011 / 2012 is basically in accordance with the observation. Above all proved the method to predict EWCD over China by SRSDMcan be employed in operational application.更多还原