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
|
摘要
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 newimproved 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 (RM S) 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.
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 newimproved 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 (RM S) 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.
引文
[1]马清.中尺度集合预报的偏差订正与多模式集成研究[D].南京:南京信息工程大学,2008.
[2]陈静,陈德辉,颜宏.集合数值预报发展与研究进展[J].应用气象学报,2002,13(4):497-507.
[3]Lorenz E N.A study of the predictability of a 28-variable atmospheric model[J].Tellus,1965,17(3):321-333.
[4]杜钧.集合预报的现状和前景[J].应用气象学报,2002,13(1):16-28.
[5]马旭林,时洋,和杰,等.基于卡尔曼滤波递减平均算法的集合预报综合偏差订正[J].气象学报,2015,73(5):952-964.
[6]王太微,陈德辉,吴曼丽.GRAPES-EPS系统的初值生成方法与对比试验研究[J].气象与环境学报,2014,30(2):23-30.
[7]Toth Z,Kalnay E.Ensemble forecasting at NMC:The generation of perturbations[J].Bulletin of the American M eteorological Society,1993,74(12):2317-2330.
[8]范苏丹,盛春岩,肖明静,等.多模式集合对山东省气象要素预报效果检验[J].气象与环境学报,2015,31(6):68-77.
[9]李俊,杜钧,陈超君.降水偏差订正的频率(或面积)匹配方法介绍和分析[J].气象,2014,40(5):580-588.
[10]王焕毅,谭政华,杨萌,等.三种数值模式气温预报产品的检验及误差订正方法研究[J].气象与环境学报,2018,34(1):22-29.
[11]徐姝,尉英华,熊明明,等.频率匹配法在海河流域ECM WF集合预报融合产品中的应用研究[J].气象与环境学报,2018,34(4):11-17.
[12]任宏利,丑纪范.统计-动力相结合的相似误差订正法[J].气象学报,2005,63(6):988-993.
[13]Roulston M S,Smith L A.Combining dynamical and statistical ensembles[J].Tellus A:Dynamic M eteorology and Oceanography,2003,55(1):16-30.
[14]黄嘉佑,谢庄.卡尔曼滤波在天气预报中的应用[J].气象,1993,19(4):3-7.
[15]Cui B,Toth Z,Zhu Y J,et al.Bias correction for global ensemble forecast[J].Weather and Forecasting,2012,27(2):396-410.
[16]李莉,李应林,田华,等.T213全球集合预报系统性误差订正研究[J].气象,2011,37(1):31-38.
[17]吴春英,刘多文,钟博,等.基于数值预报模式的乡镇温度预报方法研究[J].气象与环境学报,2019,35(1):108-112.
[18]佟华,郭品文,朱跃建,等.基于大尺度模式产品的误差订正与统计降尺度气象要素预报技术[J].气象,2014,40(1):66-75.
[19]陈国华.基于统计降尺度的中国地面气温精细化预报方法研究[D].南京:南京信息工程大学,2013.
[20]肖瑶,郭品文.中国地面气温自适应递减平均降尺度方法的改进研究[J].气象科学,2016,36(5):606-613.
[21]李莉,朱跃建.T213降水预报订正系统的建立与研究[J].应用气象学报,2006,17(S1):130-134.
[22]钱维宏,张玮玮.我国近46年来的寒潮时空变化与冬季增暖[J].大气科学,2007,31(6):1266-1278.
[23]佟华,姚明明,王雨,等.T213L31全球中期数值天气预报系统2m温度预报误差源分析[J].气象,2006,32(2):52-57.
[2]陈静,陈德辉,颜宏.集合数值预报发展与研究进展[J].应用气象学报,2002,13(4):497-507.
[3]Lorenz E N.A study of the predictability of a 28-variable atmospheric model[J].Tellus,1965,17(3):321-333.
[4]杜钧.集合预报的现状和前景[J].应用气象学报,2002,13(1):16-28.
[5]马旭林,时洋,和杰,等.基于卡尔曼滤波递减平均算法的集合预报综合偏差订正[J].气象学报,2015,73(5):952-964.
[6]王太微,陈德辉,吴曼丽.GRAPES-EPS系统的初值生成方法与对比试验研究[J].气象与环境学报,2014,30(2):23-30.
[7]Toth Z,Kalnay E.Ensemble forecasting at NMC:The generation of perturbations[J].Bulletin of the American M eteorological Society,1993,74(12):2317-2330.
[8]范苏丹,盛春岩,肖明静,等.多模式集合对山东省气象要素预报效果检验[J].气象与环境学报,2015,31(6):68-77.
[9]李俊,杜钧,陈超君.降水偏差订正的频率(或面积)匹配方法介绍和分析[J].气象,2014,40(5):580-588.
[10]王焕毅,谭政华,杨萌,等.三种数值模式气温预报产品的检验及误差订正方法研究[J].气象与环境学报,2018,34(1):22-29.
[11]徐姝,尉英华,熊明明,等.频率匹配法在海河流域ECM WF集合预报融合产品中的应用研究[J].气象与环境学报,2018,34(4):11-17.
[12]任宏利,丑纪范.统计-动力相结合的相似误差订正法[J].气象学报,2005,63(6):988-993.
[13]Roulston M S,Smith L A.Combining dynamical and statistical ensembles[J].Tellus A:Dynamic M eteorology and Oceanography,2003,55(1):16-30.
[14]黄嘉佑,谢庄.卡尔曼滤波在天气预报中的应用[J].气象,1993,19(4):3-7.
[15]Cui B,Toth Z,Zhu Y J,et al.Bias correction for global ensemble forecast[J].Weather and Forecasting,2012,27(2):396-410.
[16]李莉,李应林,田华,等.T213全球集合预报系统性误差订正研究[J].气象,2011,37(1):31-38.
[17]吴春英,刘多文,钟博,等.基于数值预报模式的乡镇温度预报方法研究[J].气象与环境学报,2019,35(1):108-112.
[18]佟华,郭品文,朱跃建,等.基于大尺度模式产品的误差订正与统计降尺度气象要素预报技术[J].气象,2014,40(1):66-75.
[19]陈国华.基于统计降尺度的中国地面气温精细化预报方法研究[D].南京:南京信息工程大学,2013.
[20]肖瑶,郭品文.中国地面气温自适应递减平均降尺度方法的改进研究[J].气象科学,2016,36(5):606-613.
[21]李莉,朱跃建.T213降水预报订正系统的建立与研究[J].应用气象学报,2006,17(S1):130-134.
[22]钱维宏,张玮玮.我国近46年来的寒潮时空变化与冬季增暖[J].大气科学,2007,31(6):1266-1278.
[23]佟华,姚明明,王雨,等.T213L31全球中期数值天气预报系统2m温度预报误差源分析[J].气象,2006,32(2):52-57.