GRAPES_Meso背景误差特征及应用
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摘要
基于2015年6月—2016年5月GRAPES_Meso有限区域中尺度数值预报模式产品,采用美国国家气象中心(NMC)方法和高斯函数拟合方案统计中国区域的背景误差和水平相关尺度随纬度、高度和季节的变化特征。结果表明:控制变量的背景误差与水平相关尺度不仅随高度和纬度有明显变化,其中非平衡Exner气压和比湿具有明显的局地性和季节变化特征。非平衡Exner气压的背景误差在青藏高原地区较大,且冬季最大,夏季最小。比湿背景误差在低纬度热带季风区较大,且夏季最大,冬季最小。非平衡Exner气压和比湿的水平相关尺度在冬季最大,夏季最小。同时文中采用随高度变化的水平相关尺度替换GRAPES-3DVar中单一尺度参数,1个月的分析和模式预报试验表明,6 h的位势高度预报在对流层有明显改进;风场分析及其12 h内的预报在平流层改进明显;对24 h不同量级降水的预报有显著正贡献,也显著改善24 h内的小雨、中雨和大雨的空报现象,明显改善12～24 h特大暴雨的漏报现象。
The statistic structure of background covariance is studied by NMC method of USA based on GRAPES regional model forecast data spanning one year from June 2015 to May 2016. The horizontal correlation length scale is estimated with Gauss function linear fitting method. Characteristics of background error and horizontal correlation length scale with the latitude, height and season are investigated. Results show that background error and horizontal correlation characteristic scale obviously change with height and latitude, and the unbalanced non-dimensional pressure and humidity are closely related to the season.Background errors of four control variables are nonhomogeneous,among which background errors of stream function and unbalanced velocity potential mainly change with latitude and height, background errors of unbalanced non-dimensional pressure and humidity show local and seasonal characteristics. The biggest background errors of the unbalanced non-dimensional pressure occur in the Tibetan Plateau, and they are larger in winter while smaller in summer. The biggest background errors of humidity happen in low latitude of tropical monsoon region, and they are larger in summer while smaller in winter. The horizontal correlation length scales of four control variables with Gauss function fitting are reasonable except that correlation coefficients of the unbalanced non-dimensional pressure are overestimated in close distance and underestimated in far distance. Horizontal correlation length scales of steam, unbalanced velocity potential and humidity obviously change with height are largest in tropopause. The length scale of unbalanced non-dimensional pressure obviously changes with latitude and is larger in low latitude of middle tropospheric. The horizontal correlation length scale of the unbalanced non-dimensional pressure and humidity both are larger in winter and smaller in summer. The horizontal correlation length scales changing with height are used in GRAPES-3 DVar system instead of single parameter, and then the analysis and forecast experiment results of one month indicate that, qualities of 6-hour geopotential height forecast in troposphere are improved. Analysis and 12-hour forecast of wind in stratosphere are greatly improved; all levels of 24-hour accumulated precipitation forecast are obviously improved; the false prediction of 24-hour accumulated precipitation of light rain, moderate rain and heavy rain are improved; 12-24-hour accumulated precipitation of extra torrential rain in control test fails to be reported, but the experiment with changing horizontal correlation length scales improves forecasts of positions and values for extra torrential rain.
The statistic structure of background covariance is studied by NMC method of USA based on GRAPES regional model forecast data spanning one year from June 2015 to May 2016. The horizontal correlation length scale is estimated with Gauss function linear fitting method. Characteristics of background error and horizontal correlation length scale with the latitude, height and season are investigated. Results show that background error and horizontal correlation characteristic scale obviously change with height and latitude, and the unbalanced non-dimensional pressure and humidity are closely related to the season.Background errors of four control variables are nonhomogeneous,among which background errors of stream function and unbalanced velocity potential mainly change with latitude and height, background errors of unbalanced non-dimensional pressure and humidity show local and seasonal characteristics. The biggest background errors of the unbalanced non-dimensional pressure occur in the Tibetan Plateau, and they are larger in winter while smaller in summer. The biggest background errors of humidity happen in low latitude of tropical monsoon region, and they are larger in summer while smaller in winter. The horizontal correlation length scales of four control variables with Gauss function fitting are reasonable except that correlation coefficients of the unbalanced non-dimensional pressure are overestimated in close distance and underestimated in far distance. Horizontal correlation length scales of steam, unbalanced velocity potential and humidity obviously change with height are largest in tropopause. The length scale of unbalanced non-dimensional pressure obviously changes with latitude and is larger in low latitude of middle tropospheric. The horizontal correlation length scale of the unbalanced non-dimensional pressure and humidity both are larger in winter and smaller in summer. The horizontal correlation length scales changing with height are used in GRAPES-3 DVar system instead of single parameter, and then the analysis and forecast experiment results of one month indicate that, qualities of 6-hour geopotential height forecast in troposphere are improved. Analysis and 12-hour forecast of wind in stratosphere are greatly improved; all levels of 24-hour accumulated precipitation forecast are obviously improved; the false prediction of 24-hour accumulated precipitation of light rain, moderate rain and heavy rain are improved; 12-24-hour accumulated precipitation of extra torrential rain in control test fails to be reported, but the experiment with changing horizontal correlation length scales improves forecasts of positions and values for extra torrential rain.
引文
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[2] Bannister R N. A review of forecast error covariance statistics in atmospheric variational data assimilation. I:Characteristics and measurements of forecast error covariances. Q J R Meteorol Soc,2008,134:1951-1970.
[3] Hollingsworth A,Lonnberg P. The statistical structure of shortrange forecast errors as determined from radiosonde data. PartⅠ:The wind field. Tellus, 1986,38A:111-136.
[4] Lonnberg P, Hollingsworth A. The statistical structure of short-range forecast errors as determined from radiosonde data. PartⅡ:The covariance of height and wind errors. Tellus,1986,38A:137-161.
[5] Franke R. Three-dimensional covariance functions for NOGAPS data. Mon Wea Rev,1999,127:2293-2308.
[6] Xu Qin,Li Wei, Andrew V T,et al. Estimation of three-dimensional error covariances. Part I:Analysis of height innovation vectors. Mon Wea Rev,2001,129:2126-2135.
[7]庄照荣,薛纪善,庄世宇,等.资料同化中背景场位势高度误差统计分析的研究.大气科学,2006,30(3):533-544.
[8] Parrish D,Derber J. The national meteorological center spectral statistical interpolation analysis. Mon Wea Rev,1992,120:1747-1763.
[9] Dee D,Gaspari G. Development of Anisotropic Correlation Models for Atmospheric Data Assimilation. The 11th Conference on Numerical Weather Prediction. American Meteorological Society,1996:249-251.
[10] Ingleby B N. The statistical structure of forecast errors and its representation in The Met Office Global 3-D Variational Data Assimilation Scheme. Q J R Meteorol Soc, 2001,127:209-231.
[11] Wu Wanshu,Purser R J. Three-dimensional variational analysis with spatially inhomogeneous covariances. Mon Wea Rev,2002,130:2905-2916.
[12]王金成,庄照荣,韩威,等.GRAPES全球变分同化背景误差协方差的改进及对分析预报的影响:背景误差协方差三维结构的估计.气象学报,2014,72(1):62-78.
[13]王亚华,杨晓帆,曾勇虎,等.GRAPES全球模式背景误差协方差水平结构特征分析.干旱气象,2017,35(1):57-63.
[14] Evensen G. The ensemble Kalman filter:Theoretical formulation and practical implementation. Ocean Dynamics,2003,53:343-367.
[15]庄照荣,薛纪善,李兴良.GRAPES集合卡尔曼滤波资料同化系统I:系统设计及初步试验.气象学报,2011,69(4):620-630.
[16]庄照荣,薛纪善,李兴良.GRAPES集合卡尔曼滤波资料同化系统II:区域分析及集合预报.气象学报,2011,69(5):860-871.
[17] Bishop C H,Etherton B J,Majumdar S J. Adaptive sampling with the ensemble transform Kalman filter. PartⅠ:Theoreti-cal aspects. Mon WeaRev, 2001,129:420-436.
[18]马旭林,薛纪善,陆维松.GRAPES全球集合预报的集合卡尔曼变换初始扰动方案初步研究.气象学报,2008,66(4):526-536.
[19] Polavarapu S,Ren S,Rochon Y,et al. Data assimilation with the Canadian middle atmosphere model. Atmos Ocean, 2005,43:77-100.
[20] Bannister R N. A review of forecast error covariance statistics in atmospheric variational data assimilation. II:Modelling the forecast error covariance statistics. Q J R Meteorol Soc, 2008,134:1971-1996.
[21]王瑞春,龚建东,张林.GRAPES变分同化系统中动力平衡约束的统计求解.应用气象学报,2012,23(2):129-138.
[22]薛纪善,陈德辉.数值预报系统GRAPES的科学设计与应用.北京:科学出版社,2008.
[23]薛纪善,刘艳,张林,等.GRAPES全球三维变分同化系统模式变量分析版(文档试用本).2012.
[24]朱宗申,朱国富.有限区域风速场求解流函数和速度势场的有效方案.应用气象学报,2008,19(1):10-18.
[25]薛纪善.我国西北高原地区数值预报的几个科学问题.干旱气象,2005,23(1):68-71.
[26]陈耀登,赵幸,闵锦忠,等.青藏高原和华东地区背景误差协方差特征的对比研究.大气科学学报,2015,38(5):650-657.
[27]央美,达瓦泽仁,贡觉顿珠,等.数值预报产品在藏北高原可预报性检验.安徽农业科学,2013,41(17):7620-7623;7675.
[28]陈德辉,沈学顺.新一代数值预报系统GRAPES研究进展.应用气象学报,2006,17(6):773-777.
[29]黄丽萍,陈德辉,邓莲堂,等.GRAPES_Meso V4. 0主要技术改进和预报效果检验.应用气象学报,2017,28(1):25-37.
[30]庄照荣,薛纪善,韩威,等.探空观测黑名单检查在变分同化系统中的应用.应用气象学报,2014,25(3):274-283.
[31]郝民,龚建东,田伟红,等.L波段探空仪湿度资料偏差订正及同化试验.应用气象学报,2018,29(5):559-570.
[32]刘艳,薛纪善,张林,等.GRAPES全球三维变分同化系统的检验与诊断.应用气象学报,2016,27(1):1-15.
[33]王金成,陆慧娟,韩威,等.GRAPES全球三维变分同化业务系统性能.应用气象学报,2017,28(1):11-24.
[34]朱立娟,龚建东,黄丽萍,等.GRAPES三维云初始场形成及在短临预报中的应用.应用气象学报,2017,28(1):38-51.
[2] Bannister R N. A review of forecast error covariance statistics in atmospheric variational data assimilation. I:Characteristics and measurements of forecast error covariances. Q J R Meteorol Soc,2008,134:1951-1970.
[3] Hollingsworth A,Lonnberg P. The statistical structure of shortrange forecast errors as determined from radiosonde data. PartⅠ:The wind field. Tellus, 1986,38A:111-136.
[4] Lonnberg P, Hollingsworth A. The statistical structure of short-range forecast errors as determined from radiosonde data. PartⅡ:The covariance of height and wind errors. Tellus,1986,38A:137-161.
[5] Franke R. Three-dimensional covariance functions for NOGAPS data. Mon Wea Rev,1999,127:2293-2308.
[6] Xu Qin,Li Wei, Andrew V T,et al. Estimation of three-dimensional error covariances. Part I:Analysis of height innovation vectors. Mon Wea Rev,2001,129:2126-2135.
[7]庄照荣,薛纪善,庄世宇,等.资料同化中背景场位势高度误差统计分析的研究.大气科学,2006,30(3):533-544.
[8] Parrish D,Derber J. The national meteorological center spectral statistical interpolation analysis. Mon Wea Rev,1992,120:1747-1763.
[9] Dee D,Gaspari G. Development of Anisotropic Correlation Models for Atmospheric Data Assimilation. The 11th Conference on Numerical Weather Prediction. American Meteorological Society,1996:249-251.
[10] Ingleby B N. The statistical structure of forecast errors and its representation in The Met Office Global 3-D Variational Data Assimilation Scheme. Q J R Meteorol Soc, 2001,127:209-231.
[11] Wu Wanshu,Purser R J. Three-dimensional variational analysis with spatially inhomogeneous covariances. Mon Wea Rev,2002,130:2905-2916.
[12]王金成,庄照荣,韩威,等.GRAPES全球变分同化背景误差协方差的改进及对分析预报的影响:背景误差协方差三维结构的估计.气象学报,2014,72(1):62-78.
[13]王亚华,杨晓帆,曾勇虎,等.GRAPES全球模式背景误差协方差水平结构特征分析.干旱气象,2017,35(1):57-63.
[14] Evensen G. The ensemble Kalman filter:Theoretical formulation and practical implementation. Ocean Dynamics,2003,53:343-367.
[15]庄照荣,薛纪善,李兴良.GRAPES集合卡尔曼滤波资料同化系统I:系统设计及初步试验.气象学报,2011,69(4):620-630.
[16]庄照荣,薛纪善,李兴良.GRAPES集合卡尔曼滤波资料同化系统II:区域分析及集合预报.气象学报,2011,69(5):860-871.
[17] Bishop C H,Etherton B J,Majumdar S J. Adaptive sampling with the ensemble transform Kalman filter. PartⅠ:Theoreti-cal aspects. Mon WeaRev, 2001,129:420-436.
[18]马旭林,薛纪善,陆维松.GRAPES全球集合预报的集合卡尔曼变换初始扰动方案初步研究.气象学报,2008,66(4):526-536.
[19] Polavarapu S,Ren S,Rochon Y,et al. Data assimilation with the Canadian middle atmosphere model. Atmos Ocean, 2005,43:77-100.
[20] Bannister R N. A review of forecast error covariance statistics in atmospheric variational data assimilation. II:Modelling the forecast error covariance statistics. Q J R Meteorol Soc, 2008,134:1971-1996.
[21]王瑞春,龚建东,张林.GRAPES变分同化系统中动力平衡约束的统计求解.应用气象学报,2012,23(2):129-138.
[22]薛纪善,陈德辉.数值预报系统GRAPES的科学设计与应用.北京:科学出版社,2008.
[23]薛纪善,刘艳,张林,等.GRAPES全球三维变分同化系统模式变量分析版(文档试用本).2012.
[24]朱宗申,朱国富.有限区域风速场求解流函数和速度势场的有效方案.应用气象学报,2008,19(1):10-18.
[25]薛纪善.我国西北高原地区数值预报的几个科学问题.干旱气象,2005,23(1):68-71.
[26]陈耀登,赵幸,闵锦忠,等.青藏高原和华东地区背景误差协方差特征的对比研究.大气科学学报,2015,38(5):650-657.
[27]央美,达瓦泽仁,贡觉顿珠,等.数值预报产品在藏北高原可预报性检验.安徽农业科学,2013,41(17):7620-7623;7675.
[28]陈德辉,沈学顺.新一代数值预报系统GRAPES研究进展.应用气象学报,2006,17(6):773-777.
[29]黄丽萍,陈德辉,邓莲堂,等.GRAPES_Meso V4. 0主要技术改进和预报效果检验.应用气象学报,2017,28(1):25-37.
[30]庄照荣,薛纪善,韩威,等.探空观测黑名单检查在变分同化系统中的应用.应用气象学报,2014,25(3):274-283.
[31]郝民,龚建东,田伟红,等.L波段探空仪湿度资料偏差订正及同化试验.应用气象学报,2018,29(5):559-570.
[32]刘艳,薛纪善,张林,等.GRAPES全球三维变分同化系统的检验与诊断.应用气象学报,2016,27(1):1-15.
[33]王金成,陆慧娟,韩威,等.GRAPES全球三维变分同化业务系统性能.应用气象学报,2017,28(1):11-24.
[34]朱立娟,龚建东,黄丽萍,等.GRAPES三维云初始场形成及在短临预报中的应用.应用气象学报,2017,28(1):38-51.
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