中国地区集合预报产品自适应递减平均偏差订正法的改进研究

Improvement of a self-adaption decaying average bias correction method based on ensemble forecast

基于中国地区T213集合预报产品2 m温度预报数据,采用卡尔曼滤波类型的自适应递减平均法进行偏差订正处理,原方案在剧烈降温天气订正效果表现不理想。通过对递减平均参数w的重新构建得到改进的订正方案w(i,p)(i为站点信息,p为天气过程信息),在此基础上进一步优化对历史信息的有效提取,得到改进的方案w(i,p)相似法和w(i,p)统计法,并进行效果检验。结果表明:改进为包含空间和天气过程信息的函数w(i,p)后方案的订正效果得到不同程度的提高,其中24 h剧烈降温预报各成员预报均方根误差平均减小了0. 15℃;而进一步改进的w(i,p)统计法在当前几种剧烈降温预报中订正效果最优,其集合平均偏差与w(i,p)方案相比减小2. 54℃。

Bias correction for the 2 m air temperature from the T213 ensemble forecast product performed not good on dramatically cooling days using the original self-adaption Kalman Filter-typed decaying average bias correction method.In this study,the bias correction scheme w(i,p ) is improved by redefining the decaying average weight w ,with i representing station information and p representing synoptic process information,and the similarity w(i,p ) method and the statistical w(i,p ) method are further developed through optimizing effective extraction of historical information.The new improved bias correction methods have been evaluated.The result showed that the improved w(i,p ) decaying average bias correction method has a better performance than the original method.The averaged root-mean-square (RMS) error of the 24-h forecast decreases by 0.15 ℃ for each member on dramatically cooling days.The statistical w(i,p ) method has the best performance,with the averaged ensemble mean bias decreases by 2.54 ℃ compared with the w(i,p ) decaying average bias correction method.