高级检索

留言板

尊敬的读者、作者、审稿人, 关于本刊的投稿、审稿、编辑和出版的任何问题, 您可以本页添加留言。我们将尽快给您答复。谢谢您的支持!

姓名
邮箱
手机号码
标题
留言内容
验证码

水平相关模型的性质及其在GRAPES三维变分系统中的应用

庄照荣 李兴良 陈春刚

庄照荣, 李兴良, 陈春刚. 2021. 水平相关模型的性质及其在GRAPES三维变分系统中的应用[J]. 大气科学, 45(1): 229−244 doi: 10.3878/j.issn.1006-9895.2010.20107
引用本文: 庄照荣, 李兴良, 陈春刚. 2021. 水平相关模型的性质及其在GRAPES三维变分系统中的应用[J]. 大气科学, 45(1): 229−244 doi: 10.3878/j.issn.1006-9895.2010.20107
ZHUANG Zhaorong, LI Xingliang, CHEN Chungang. 2021. Properties of Horizontal Correlation Models and Its Application in GRAPES 3DVar System [J]. Chinese Journal of Atmospheric Sciences (in Chinese), 45(1): 229−244 doi: 10.3878/j.issn.1006-9895.2010.20107
Citation: ZHUANG Zhaorong, LI Xingliang, CHEN Chungang. 2021. Properties of Horizontal Correlation Models and Its Application in GRAPES 3DVar System [J]. Chinese Journal of Atmospheric Sciences (in Chinese), 45(1): 229−244 doi: 10.3878/j.issn.1006-9895.2010.20107

水平相关模型的性质及其在GRAPES三维变分系统中的应用

doi: 10.3878/j.issn.1006-9895.2010.20107
基金项目: 国家重点研究发展计划项目2017YFA0603901、2017YFC1501901、2017YFC1502001
详细信息
    作者简介:

    庄照荣,女,1978年出生,高级工程师,主要从事资料同化研究,E-mail: zrzhuang@cma.gov.cn

    通讯作者:

    李兴良,E-mail: lixliang@cma.gov.cn

  • 中图分类号: P435

Properties of Horizontal Correlation Models and Its Application in GRAPES 3DVar System

Funds: National Key Research and Development Program of China (Grants 2017YFA0603901, 2017YFC1501901, 2017YFC1502001)
  • 摘要: 在变分资料同化中背景误差水平相关模型不仅决定着观测信息传播到格点空间的远近,而且影响着频谱空间中不同尺度上的分析增量信息的多少。本文比较高斯(Gauss)、二阶自回归(Soar)以及尺度叠加高斯模型(Supergauss)在时空域随着空间距离和在频谱域随着不同尺度分布的特点,阐述三种相关模型在区域GRAPES三维变分分析(GRAPES-3DVar)中的实施方案,同时通过单点观测试验研究不同相关模型对分析的影响。研究表明Gauss相关模型造成分析中小尺度信息的不足,同时当流函数和非平衡的势函数作为分析变量时,依据动力场变量之间的相关关系,会造成风场观测不合理的较大负相关信息。Soar相关模型能增加分析的中小尺度信息,但在三维变分分析实施中只能采用一阶递归滤波方案,由于计算精度不够会造成风场分析增量异常。当采用Supergauss相关模型时,不仅缓解单一高斯模型造成的不恰当风场观测负相关信息,并可增加分析增量的中小尺度信息,同时在递归滤波实施中可获得合理的分析增量。因而Supergauss相关模型在三种模型中最适合描述背景误差水平相关,对高分辨率3DVar系统的中小尺度分析有益。
  • 图  1  (a)高斯(Gauss)和二阶自回归(Soar)相关模型及其(b)归一化的负拉普拉斯算子。r为两点间的距离,L为固定水平相关尺度

    Figure  1.  (a) Gaussian and second-order autoregressive correlation models and (b) their normalized negative Laplacian. r represents distance of two point, L indicates horizontal correlation length

    图  2  (a)高斯和二阶自回归相关模型的谱响应函数及其(b)拉普拉斯算子的谱响应函数

    Figure  2.  (a) Spectral response functions of Gaussian and second-order autoregressive correlation models and (b) their Laplacian spectral response functions

    图  3  (a)不同尺度的高斯相关模型和尺度叠加的高斯模型及其(b)归一化的负拉普拉斯算子。G350G500G850分别表示相关尺度为350 km、500 km和850 km的高斯相关模型;Supergauss表示以上三种尺度叠加的高斯模型

    Figure  3.  (a) Gaussian correlation models of different scales, Supergauss correlation model and (b) their normalized negative Laplacians. G350, G500, and G850 indicate Gaussian correlation models with horizontal correlation length of 350 km, 500 km and 850 km, respectively; Supergauss represents superposition of Gaussian components with the three horizontal correlation lengths

    图  4  (a)不同尺度的高斯相关模型和尺度叠加高斯模型的谱响应函数及其(b)拉普拉斯算子的谱响应函数

    Figure  4.  (a) Spectral response functions of Gaussian correlation models with different scales, Supergauss correlation model and (b) their Laplacian spectral response functions

    图  5  单点u风场和湿度观测分析试验的分析增量:Gauss(左);Soar(中);Supergauss(右)。第1~4行分别为无量纲气压(×10−6)、u风场(单位:m s−1)、v风场(单位:m s−1)、比湿(单位:10−4 kg kg−1

    Figure  5.  Analysis increments for single u-component wind and humidity observation: Gauss (left); Soar (middle); Supergauss (right). The row 1–4 indicate nondimensional pressure (×10−6), u-component (units: m s−1), v–component (units: m s−1), specific humidity (units: 10−4 kg kg−1), respectively

    图  6  110°E处(a)u风场和(b)比湿归一化的分析增量

    Figure  6.  Normalized analysis increment for (a) u-component and (b) specific humidity at 110°E

    图  7  图5,但为单点v风场观测分析试验的分析增量

    Figure  7.  As in Fig. 5, but for analysis increments for single v-component wind observation

    图  8  图5,但为单点气压观测分析试验的分析增量

    Figure  8.  As in Fig. 5, but for analysis increments for single pressure observation

    图  9  单点u风场和湿度观测分析试验的分析增量功率谱(单位:m3 s−2):(a)无量纲气压;(b)比湿;(c)u风场;(d)v风场

    Figure  9.  Power spectrum (units: m3 s−2) of analysis increments for single u-component wind and humidity observation: (a) Nondimensional pressure; (b) specific humidity; (c) u-component; (d) v-component

    图  10  单点气压观测分析试验的分析增量功率谱(单位:m3 s−2):(a)无量纲气压;(b)u风场;(c)v风场

    Figure  10.  Power spectrum (units: m3 s−2) of analysis increments for single pressure observation: (a) Nondimensional pressure; (b) u-component; (c) v-component

  • [1] Bannister R N. 2017. A review of operational methods of variational and ensemble-variational data assimilation [J]. Quart. J. Roy. Meteor. Soc., 143: 607−633. doi: 10.1002/qj.2982
    [2] Bédard J, Caron J F, Buehner M, et al. 2020. Hybrid background error covariances for a limited-area deterministic weather prediction system [J]. Wea. Forecasting, 35: 1051−1066. doi: 10.1175/WAF-D-19-0069.1
    [3] Bishop C H, Hodyss D. 2011. Adaptive ensemble covariance localization in ensemble 4D-VAR state estimation [J]. Mon. Wea. Rev., 139: 1241−1255. doi: 10.1175/2010MWR3403.1
    [4] Chen Lianglü, Chen Jing, Xue Jishan, et al. 2015. Development and testing of the GRAPES regional ensemble-3DVar hybrid data assimilation system [J]. Journal of Meteorological Research, 29: 981−996. doi: 10.1007/s13351-015-5021-y
    [5] Clayton A M, Lorenc A C, Barker D M. 2013. Operational implementation of a hybrid ensemble/4D-Var global data assimilation system at the Met Office [J]. Quart. J. Roy. Meteor. Soc., 139: 1445−1461. doi: 10.1002/qj.2054
    [6] Daley R. 1985. The analysis of synoptic scale divergence by a statistical interpolation procedure [J]. Mon. Wea. Rev., 113: 1066−1080. doi:10.1175/1520-0493(1985)113<1066:TAOSSD>2.0.CO;2
    [7] Daley R. 1991. Atmospheric Data Analysis [M]. Cambridge: Cambridge University Press, 107–118.
    [8] Dee D, Gaspari G. 1996. Development of anisotropic correlation models for atmospheric data assimilation [C]//Proceedings of the 11th Conference on Numerical Weather Prediction. Norfolk: American Meteorological Society, 249–251.
    [9] Denis B, Côté J, Laprise R. 2002. Spectral decomposition of two-dimensional atmospheric fields on limited-area domains using the Discrete Cosine Transform (DCT) [J]. Mon. Wea. Rev., 130: 1812−1829. doi:10.1175/1520-0493(2002)130<1812:SDOTDA>2.0.CO;2
    [10] Evensen G. 1994. Sequential data assimilation with a nonlinear quasi-geostrophic model using Monte Carlo methods to forecast error statistics [J]. J. Geophys. Res., 99: 10143−10162. doi: 10.1029/94JC00572
    [11] Evensen G. 2003. The ensemble Kalman filter: Theoretical formulation and practical implementation [J]. Ocean Dyn., 53: 343−367. doi: 10.1007/s10236-003-0036-9
    [12] Evensen G, van Leeuwen P J. 2000. An ensemble Kalman smoother for nonlinear dynamics [J]. Mon. Wea. Rev., 128: 1852−1867. doi:10.1175/1520-0493(2000)128<1852:AEKSFN>2.0.CO;2
    [13] Franke R. 1999. Three-dimensional covariance functions for NOGAPS data [J]. Mon. Wea. Rev., 127: 2293−2308. doi:10.1175/1520-0493(1999)127<2293:TDCFFN>2.0.CO;2
    [14] 龚建东. 2007. 资料同化中二维特征长度随模式分辨率变化的分析研究 [J]. 大气科学, 31(3): 459−467. doi: 10.3878/j.issn.1006-9895.2007.03.09

    Gong Jiandong. 2007. The analysis on variation of horizontal de-correlation length with model resolution in data assimilation system [J]. Chinese Journal of Atmospheric Sciences (in Chinese), 31(3): 459−467. doi: 10.3878/j.issn.1006-9895.2007.03.09
    [15] Gustafsson N, Bojarova J. 2014. Four-dimensional ensemble variational (4D-En-Var) data assimilation for the HIgh Resolution Limited Area Model (HIRLAM) [J]. Nonlinear Processes in Geophysics, 21: 745−762. doi: 10.5194/npg-21-745-2014
    [16] Hamill T M, Snyder C. 2000. A hybrid ensemble Kalman filter-3D variational analysis scheme [J]. Mon. Wea. Rev., 128: 2905−2919. doi:10.1175/1520-0493(2000)128<2905:AHEKFV>2.0.CO;2
    [17] 何光鑫. 2009. 递归滤波方案在GRAPES-3DVar中的运用研究 [D]. 南京信息工程大学硕士学位论文, 30−36.

    He Guangxin. 2009. The application of recursive filters to GRAPES three-dimensional variational data assimilation system [D]. M. S. thesis (in Chinese), Nanjing University of Information Science & Technology, 30−36.
    [18] 何光鑫, 李刚, 张华. 2011. GRAPES-3DVar高阶递归滤波方案及其初步试验 [J]. 气象学报, 69(6): 1001−1008. doi: 10.11676/qxxb2011.087

    He Guangxin, Li Gang, Zhang Hua. 2011. The scheme of high-order recursive filter for the GRAPES-3DVar with its initial experiments [J]. Acta Meteor. Sinica (in Chinese), 69(6): 1001−1008. doi: 10.11676/qxxb2011.087
    [19] Hollingsworth A, Lönnberg P. 1986. The statistical structure of short-range forecast errors as determined from radiosonde data. Part I: The wind field [J]. Tellus, 38A: 111−136. doi: 10.1111/j.1600-0870.1986.tb00460.x
    [20] Houtekamer P L, Mitchell H L. 1998. Data assimilation using an ensemble Kalman filter technique [J]. Mon. Wea. Rev., 126: 796−811. doi:10.1175/1520-0493(1998)126<0796:DAUAEK>2.0.CO;2
    [21] Houtekamer P L, Mitchell H L. 2001. A sequential ensemble Kalman filter for atmospheric data assimilation [J]. Mon. Wea. Rev., 129: 123−137. doi:10.1175/1520-0493(2001)129<0123:ASEKFF>2.0.CO;2
    [22] Houtekamer P L, Zhang F Q. 2016. Review of the ensemble Kalman filter for atmospheric data assimilation [J]. Mon. Wea. Rev., 144: 4489−4532. doi: 10.1175/MWR-D-15-0440.1
    [23] Ingleby N B. 2001. The statistical structure of forecast errors and its representation in the Met. Office global 3-D variational data assimilation scheme [J]. Quart. J. Roy. Meteor. Soc., 127: 209−231. doi: 10.1002/qj.49712757112
    [24] Kleist D T, Ide K. 2015. An OSSE-based evaluation of hybrid variational-ensemble data assimilation for the NCEP GFS. Part Ⅱ: 4DEnVar and hybrid variants [J]. Mon. Wea. Rev., 143: 452−470. doi: 10.1175/MWR-D-13-00350.1
    [25] Liu Chengsi, Xiao Qingnong. 2013. An ensemble-based four-dimensional variational data assimilation scheme. Part Ⅲ: Antarctic applications with advanced research WRF using real data [J]. Mon. Wea. Rev., 141: 2721−2739. doi: 10.1175/MWR-D-12-00130.1
    [26] Liu Chengsi, Xiao Qingnong, Wang Bin. 2008. An ensemble-based four-dimensional variational data assimilation scheme. Part I: Technical formulation and preliminary test [J]. Mon. Wea. Rev., 136: 3363−3373. doi: 10.1175/2008MWR2312.1
    [27] Liu Chengsi, Xiao Qingnong, Wang Bin. 2009. An ensemble-based four-dimensional variational data assimilation scheme. Part Ⅱ: Observing system simulation experiments with advanced research WRF (ARW) [J]. Mon. Wea. Rev., 137: 1687−1704. doi: 10.1175/2008MWR2699.1
    [28] Lönnberg P, Hollingsworth A. 1986. The statistical structure of short-range forecast errors as determined from radiosonde data. Part Ⅱ: The covariance of height and wind errors [J]. Tellus, 38A: 137−161. doi: 10.3402/tellusa.v38i2.11708
    [29] 马旭林, 陆续, 于月明, 等. 2014. 数值天气预报中集合—变分混合资料同化及其研究进展 [J]. 热带气象学报, 30(6): 1188−1195. doi: 10.3969/j.issn.1004-4965.2014.06.020

    Ma Xulin, Lu Xu, Yu Yueming, et al. 2014. Progress on hybrid ensemble-variational data assimilation in numerical weather prediction [J]. Journal of Tropical Meteorology (in Chinese), 30(6): 1188−1195. doi: 10.3969/j.issn.1004-4965.2014.06.020
    [30] 马旭林, 李琳琳, 周勃旸, 等. 2015. 台风预报误差的流依赖特征及混合资料同化中最优耦合系数 [J]. 大气科学学报, 38(6): 766−775. doi: 10.13878/j.cnki.dqkxxb.20141224001

    Ma Xulin, Li Linlin, Zhou Boyang, et al. 2015. Flow-dependent characteristics of typhoon forecasting errors and optimal coupling coefficient in hybrid data assimilation [J]. Transactions of Atmospheric Sciences (in Chinese), 38(6): 766−775. doi: 10.13878/j.cnki.dqkxxb.20141224001
    [31] Parrish D F, Derber J C. 1992. The national meteorological center’s spectral statistical-interpolation analysis system [J]. Mon. Wea. Rev., 120: 1747−1763. doi:10.1175/1520-0493(1992)120<1747:TNMCSS>2.0.CO;2
    [32] Purser R J, Wu Wanshu, Parrish D F, et al. 2003a. Numerical aspects of the application of recursive filters to variational statistical analysis. Part I: Spatially homogeneous and isotropic Gaussian covariances [J]. Mon. Wea. Rev., 131: 1524−1535. doi:10.1175//1520-0493(2003)131<1524:NAOTAO>2.0.CO;2
    [33] Purser R J, Wu Wanshu, Parrish D F, et al. 2003b. Numerical aspects of the application of recursive filters to variational statistical analysis. Part Ⅱ: Spatially inhomogeneous and anisotropic general covariances [J]. Mon. Wea. Rev., 131: 1536−1548. doi: 10.1175//2543.1
    [34] Vandenberghe F, Kuo Y H. 1999. Introduction to the MM5 3D-VAR data assimilation system: Theoretical basis [R]. NCAR Technical note 917.
    [35] Wang Xuguang, Lei Ting. 2014. GSI-based four-dimensional ensemble-variational (4DEnsVar) data assimilation: Formulation and single-resolution experiments with real data for NCEP global forecast system [J]. Mon. Wea. Rev., 142: 3303−3325. doi: 10.1175/MWR-D-13-00303.1
    [36] Wang Xuguang, Barker D M, Snyder C, et al. 2008a. A hybrid ETKF-3DVar data assimilation scheme for the WRF model. Part I: Observing system simulation experiment [J]. Mon. Wea. Rev., 136: 5116−5131. doi: 10.1175/2008MWR2444.1
    [37] Wang Xuguang, Barker D M, Snyder C, et al. 2008b. A hybrid ETKF-3DVar data assimilation scheme for the WRF model. Part Ⅱ: Real observation experiments [J]. Mon. Wea. Rev., 136: 5132−5147. doi: 10.1175/2008MWR2445.1
    [38] Wang Xuguang, Parrish D, Kleist D, et al. 2013. GSI 3DVar-based ensemble-variational hybrid data assimilation for NCEP global forecast system: Single-resolution experiments [J]. Mon. Wea. Rev., 141: 4098−4117. doi: 10.1175/MWR-D-12-00141.1
    [39] 王金成, 庄照荣, 韩威, 等. 2014. 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 [J]. 气象学报, 72(1): 62−78. doi: 10.11676/qxxb2014.008

    Wang Jincheng, Zhuang Zhaorong, Han Wei, et al. 2014. An improvement of background error covariance in the global GRAPES variational data assimilation and its impact on the analysis and prediction: Statistics of the three-dimensional structure of background error covariance [J]. Acta Meteor. Sinica (in Chinese), 72(1): 62−78. doi: 10.11676/qxxb2014.008
    [40] 王亚华, 杨晓帆, 曾勇虎, 等. 2017. GRAPES全球模式背景误差协方差水平结构特征分析 [J]. 干旱气象, 35(1): 57−63.

    Wang Yahua, Yang Xiaofan, Zeng Yonghu, et al. 2017. Horizontal structural characteristics analysis of global GRAPES model background error covariance [J]. Journal of Arid Meteorology (in Chinese), 35(1): 57−63.
    [41] 王玉柱, 姜金荣, 迟学斌, 等. 2014. 并行准高斯高阶递归滤波算法研究 [J]. 计算数学, 36(2): 179−194.

    Wang Yuzhu, Jiang Jinrong, Chi Xuebin, et al. 2014. Research on parallel algorithm of quasi-gaussian high-order recursive filter [J]. Mathematica Numerica Sinica (in Chinese), 36(2): 179−194.
    [42] 王瑞春, 龚建东, 张林, 等. 2015a. 热带风压场平衡特征及其对GRAPES系统中同化预报的影响研究. I: 平衡特征分析 [J]. 大气科学, 39(5): 953−966. doi: 10.3878/j.issn.1006-9895.1412.14233

    Wang Ruichun, Gong Jiandong, Zhang Lin, et al. 2015a. Tropical balance characteristics between mass and wind fields and their impact on analyses and forecasts in GRAPES system. Part I: Balance characteristics [J]. Chinese Journal of Atmospheric Sciences (in Chinese), 39(5): 953−966. doi: 10.3878/j.issn.1006-9895.1412.14233
    [43] 王瑞春, 龚建东, 张林, 等. 2015b. 热带风压场平衡特征及其对GRAPES系统中同化预报的影响研究. Ⅱ: 动力与统计混合平衡约束方案的应用 [J]. 大气科学, 39(6): 1225−1236. doi: 10.3878/j.issn.1006-9895.1412.14234

    Wang Ruichun, Gong Jiandong, Zhang Lin, et al. 2015b. Tropical Balance Characteristics between mass and wind fields and their impact on analyses and forecasts in GRAPES system. Part Ⅱ: Application of linear balance equation-regression hybrid constraint scheme [J]. Chinese Journal of Atmospheric Sciences (in Chinese), 39(6): 1225−1236. doi: 10.3878/j.issn.1006-9895.1412.14234
    [44] Whitaker J S, Hamill T M. 2002. Ensemble data assimilation without perturbed observations [J]. Mon. Wea. Rev., 130: 1913−1924. doi:10.1175/1520-0493(2002)130<1913:EDAWPO>2.0.CO;2
    [45] 吴洋, 徐枝芳, 王瑞春, 等. 2018. 基于多尺度混合滤波的GRAPES_3Dvar及其在实际暴雨预报中的应用分析 [J]. 气象, 44(5): 621−633. doi: 10.7519/j.issn.1000-0526.2018.05.003

    Wu Yang, Xu Zhifang, Wang Ruichun, et al. 2018. Improvement of GRAPES_3Dvar with a new multi-scale filtering and its application in heavy rain forecasting [J]. Meteorological Monthly (in Chinese), 44(5): 621−633. doi: 10.7519/j.issn.1000-0526.2018.05.003
    [46] Xu Qin, Li Wei, van Andrew T, et al. 2001. Estimation of three-dimensional error covariances. Part I: Analysis of height innovation vectors [J]. Mon. Wea. Rev., 129: 2126−2135. doi:10.1175/1520-0493(2001)129<2126:EOTDEC>2.0.CO;2
    [47] 薛纪善, 陈德辉. 2008. 数值预报系统GRAPES的科学设计与应用 [M]. 北京: 科学出版社, 6–10.

    Xue Jishan, Chen Dehui. 2008. Scientific Design and Application of GRAPES (in Chinese) [M]. Beijing: Science Press, 6–10.
    [48] 薛纪善, 刘艳, 张林, 等. 2012. GRAPES全球三维变分同化系统模式变量分析版科学文档 [R]. 北京: 中国气象局, 1–11.

    Xue Jishan, Liu Yan, Zhang Lin, et al. 2012. Scientific documentation of GRAPES-3DVar version for global model (in Chinese) [R]. Beijing: China Meteorological Administration, 1–11.
    [49] 张华, 薛纪善, 庄世宇, 等. 2004. GRAPeS三维变分同化系统的理想试验 [J]. 气象学报, 62(1): 31−41. doi: 10.3321/j.issn:0577-6619.2004.01.004

    Zhang Hua, Xue Jishan, Zhuang Shiyu, et al. 2004. Idea experiments of GRAPeS three-dimensional variational data assimilation system [J]. Acta Meteor. Sinica (in Chinese), 62(1): 31−41. doi: 10.3321/j.issn:0577-6619.2004.01.004
    [50] 郑永骏, 金之雁, 陈德辉. 2008. 半隐式半拉格朗日动力框架的动能谱分析 [J]. 气象学报, 66(2): 143−157. doi: 10.3321/j.issn:0577-6619.2008.02.002

    Zheng Yongjun, Jin Zhiyan, Chen Dehui. 2008. Kinetic energy spectrum analysis in a semi-implicit semi-Lagrangian dynamical framework [J]. Acta Meteor. Sinica (in Chinese), 66(2): 143−157. doi: 10.3321/j.issn:0577-6619.2008.02.002
    [51] 庄世宇, 薛纪善, 朱国富, 等. 2005. GRAPES全球三维变分同化系统——基本设计方案与理想试验 [J]. 大气科学, 29(6): 872−884. doi: 10.3878/j.issn.1006-9895.2005.06.04

    Zhuang Shiyu, Xue Jishan, Zhu Guofu, et al. 2005. GRAPES global 3D-Var system—Basic scheme design and single observation test [J]. Chinese Journal of Atmospheric Sciences (in Chinese), 29(6): 872−884. doi: 10.3878/j.issn.1006-9895.2005.06.04
    [52] 庄照荣, 薛纪善, 朱宗申, 等. 2006a. 非线性平衡方案在三维变分同化系统中的应用 [J]. 气象学报, 64(2): 137−148. doi: 10.3321/j.issn:0577-6619.2006.02.002

    Zhuang Zhaorong, Xue Jishan, Zhu Zongshen, et al. 2006a. Application of nonlinear balance scheme in three-dimensional variational data assimilation [J]. Acta Meteor. Sinica (in Chinese), 64(2): 137−148. doi: 10.3321/j.issn:0577-6619.2006.02.002
    [53] 庄照荣, 薛纪善, 庄世宇, 等. 2006b. 资料同化中背景场位势高度误差统计分析的研究 [J]. 大气科学, 30(3): 533−544.

    Zhuang Zhaorong, Xue Jishan, Zhuang Shiyu, et al. 2006b. A study of the statistical analysis of the geopotential height background errors in the data assimilation [J]. Chinese Journal of Atmospheric Sciences (in Chinese), 30(3): 533−544.
    [54] 庄照荣, 薛纪善, 李兴良. 2011a. GRAPES集合卡尔曼滤波资料同化系统. I: 系统设计及初步试验 [J]. 气象学报, 69(4): 620−630. doi: 10.11676/qxxb2011.054

    Zhuang Zhaorong, Xue Jishan, Li Xingliang. 2011a. The GRAPES ensemble Kalman filter data assimilation system. Part I: Design and its tentative experiment [J]. Acta Meteor. Sinica (in Chinese), 69(4): 620−630. doi: 10.11676/qxxb2011.054
    [55] 庄照荣, 薛纪善, 李兴良. 2011b. GRAPES集合卡尔曼滤波资料同化系统. Ⅱ: 区域分析及集合预报 [J]. 气象学报, 69(5): 860−871. doi: 10.11676/qxxb2011.075

    Zhuang Zhaorong, Xue Jishan, Li Xingliang. 2011b. The GRAPES ensemble Kalman filter data assimilation system. Part Ⅱ: Regional analysis and ensemble prediction [J]. Acta Meteor. Sinica (in Chinese), 69(5): 860−871. doi: 10.11676/qxxb2011.075
    [56] 庄照荣, 陈静, 黄丽萍, 等. 2018. 全球和区域分析的混合方案对区域预报的影响试验 [J]. 气象, 44(12): 1509−1517. doi: 10.7519/j.issn.1000-0526.2018.12.001

    Zhuang Zhaorong, Chen Jing, Huang Liping, et al. 2018. Impact experiments for regional forecast using blending method of global and regional analyses [J]. Meteorological Monthly (in Chinese), 44(12): 1509−1517. doi: 10.7519/j.issn.1000-0526.2018.12.001
    [57] 庄照荣, 王瑞春, 王金成, 等. 2019. GRAPES_Meso背景误差特征及应用 [J]. 应用气象学报, 30(3): 316−331. doi: 10.11898/1001-7313.20190306

    Zhuang Zhaorong, Wang Ruichun, Wang Jincheng, et al. 2019. Characteristics and application of background errors in GRAPES_Meso [J]. Journal of Applied Meteorological Science (in Chinese), 30(3): 316−331. doi: 10.11898/1001-7313.20190306
  • 加载中
图(10)
计量
  • 文章访问数:  30
  • HTML全文浏览量:  10
  • PDF下载量:  9
  • 被引次数: 0
出版历程
  • 收稿日期:  2020-01-20
  • 录用日期:  2020-10-23
  • 网络出版日期:  2020-09-26
  • 刊出日期:  2021-01-19

目录

    /

    返回文章
    返回