Vol.29, NO.5, 2018
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Article Navigation >  Journal of Applied Meteorological Science  > 2018  >  29(5): 571-583
Zhang Meng, Yu Haipeng, Huang Jianping, et al. Assessment on systematic errors of GRAPES_GFS 2.0. J Appl Meteor Sci, 2018, 29(5): 571-583. DOI:  10.11898/1001-7313.20180506.
Citation: Zhang Meng, Yu Haipeng, Huang Jianping, et al. Assessment on systematic errors of GRAPES_GFS 2.0. J Appl Meteor Sci, 2018, 29(5): 571-583. DOI:  10.11898/1001-7313.20180506.
shu

Assessment on Systematic Errors of GRAPES_GFS 2.0

DOI: 10.11898/1001-7313.20180506
  • 1.

    College of Atmospheric Sciences and Key Laboratory for Semi-arid Climate change, Lanzhou University, Lanzhou 730000

  • 2.

    Unit 86 of No.93811 PLA, Lanzhou 730020

  • 3.

    Key Laboratory of Arid Climatic Change and Reducing Disaster of Gansu Province, Key Open Laboratory of Arid Climatic Change and Disaster Reduction of CMA, Institute of Arid Meteorology, CMA, Lanzhou 730020

  • 4.

    Numerical Prediction Center of CMA, Beijing 100081

  • 5.

    State Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, Beijing 100081

  • Received Date: 2018-03-20
  • Rev Recd Date: 2018-05-21
  • Publish Date: 2018-09-30
    Abstract
  • The Global and Regional Assimilation and Prediction System(GRAPES) model is set up as a new generation multi-scale universal data assimilation and numerical prediction system in China. The global forecasting system version of GRAPES_GFS 2.0 is formally established in June 2016, and thus a comprehensive assessment on its forecasting capacity is urgently needed. Comparing with NCEP FNL data, the hindcast of a whole year of 2014 and 4 seasonal representative months by GRAPES_GFS 2.0 are analyzed.The systematic error of 500 hPa potential height field is characterized by the obvious gradient and zonal distribution or wave columnar distribution, concentrated in mid and high latitudes. The error shows significant seasonal variation, which is much larger in winter than that in summer of both the north and south hemispheres. Furthermore, compared with the linear growth rate, GRAPES_GFS 2.0 forecast error is lower, and changing trends of errors with height are similar when lead time changes. The distribution of the initial error of 500 hPa temperature field is concentrated in tropics, while along with the growth of the forecast time, the large area of forecast error gradually moves to middle and high latitude areas. Moreover, the zonal average temperature error is mainly negative, while slightly positive near the tropopause of high latitude areas. There is no obvious distribution law of latitudinal wind field error, which is not closely related to latitude, sea land distribution and topography, alternated with west wind error and east wind error. The error of the height field in the tropopause, the temperature field and zonal wind field in the boundary layer and in the tropopause increases rapidly as well.Results above show that the evaluation on the oblique pressure instability of geopotential height field in mid and high latitudes still needs improving. As the low latitude area is dominated by positive pressure structure, the absolute error value with its growth is relatively small. Over-estimated thermal forces in plateau and desert regions result in the large error area of temperature field. The zonal wind field error is similar but may result in meridional wind error. In addition, the performance of the model in boundary layer and tropopause simulation needs improving.
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  • Fig. 1  GRAPES_GFS 2.0 model forecast systematic errors of 500 hPa geopotential height field with lead time of 1 d(a), 3 d(b), 5 d(c), 8 d(d) for the average of Jan, Apr, Jul and Oct in 2014(the shaded)

    (the solid line represent mean field by NCEP FNL, unit:gpm)

    DownLoad: Full-Size Img PowerPoint

    Fig. 2  Empirical orthogonal function analysis of systematic errors of 500 hPa geopotential height field for the average of Jan, Apr, Jul and Oct in 2014

    (a)the first mode, (b)the second mode, (c)the third mode, (d)time coefficient corresponding to the first mode, (e)time coefficient corresponding to the second mode, (f)time coefficient corresponding to the third mode

    DownLoad: Full-Size Img PowerPoint

    Fig. 3  GRAPES_GFS 2.0 model 5-day mean systematic errors of 500 hPa geopotential height field(the shaded)

    (a)Jan 2014, (b)Apr 2014, (c)Jul 2014, (d)Oct 2014
    (solid lines represent mean field by NCEP FNL, unit:gpm)

    DownLoad: Full-Size Img PowerPoint

    Fig. 4  GRAPES_GFS 2.0 model forecast systematic errors of 500 hPa temperature height field with lead time of 1 d(a), 3 d(b), 5 d(c), 8 d(d) for the average of Jan, Apr, Jul and Oct in 2014(the shaded)

    (solid lines represent mean field by NCEP FNL, unit:℃)

    DownLoad: Full-Size Img PowerPoint

    Fig. 5  GRAPES_GFS 2.0 model forecast systematic errors of zonal temperature field with lead time of 1 d(a), 3 d(b), 5 d(c), 8 d(d) for the average of Jan, Apr, Jul and Oct in 2014(the shaded)

    (solid lines represent mean field by NCEP FNL, unit:℃)

    DownLoad: Full-Size Img PowerPoint

    Fig. 6  GRAPES_GFS 2.0 model forecast systematic errors of 850 hPa zonal wind field with lead time of 1 d(a), 3 d(b), 5 d(c), 8 d(d) for the average of Jan, Apr, Jul and Oct in 2014(the shaded)

    (solid lines represent mean field by NCEP FNL, unit:m·s-1)

    DownLoad: Full-Size Img PowerPoint

    Fig. 7  GRAPES_GFS 2.0 model forecast systematic errors of 200 hPa zonal wind field with lead time of 1 d(a), 3 d(b), 5 d(c), 8 d(d) for the average of Jan, Apr, Jul and Oct in 2014(the shaded)

    (solid lines represent mean field by NCEP FNL, unit:m·s-1)

    DownLoad: Full-Size Img PowerPoint

    Fig. 8  GRAPES_GFS 2.0 model forecast systematic errors of zonal average wind field with lead time of 1 d(a), 3 d(b), 5 d(c), 8 d(d) for the average of Jan, Apr, Jul and Oct in 2014(the shaded)

    (solid lines represent mean field by NCEP FNL, unit:m·s-1)

    DownLoad: Full-Size Img PowerPoint

    Fig. 9  Systematic mean square error along with forecast time for geopotential height field, temperature field, zonal wind field at 500 hPa the average of Jan, Apr, Jul and Oct in 2014

    (shaded bands represent 95% confidence intervals)

    DownLoad: Full-Size Img PowerPoint

    Fig. 10  Systematic mean square error along with height in different lead time for geopotential height field, temperature field, zonal wind field for the average of Jan, Apr, Jul and Oct in 2014

    DownLoad: Full-Size Img PowerPoint
  • [1]
    黄丽萍, 伍湘君, 金之雁.GRAPES模式标准初始化方案设计与实现.应用气象学报, 2005, 16(3):374-384. doi:  10.3969/j.issn.1001-7313.2005.03.011
    [2]
    伍湘君, 金之雁, 黄丽萍, 等.GRAPES模式软件框架与实现.应用气象学报, 2005, 16(4):539-546. doi:  10.3969/j.issn.1001-7313.2005.04.015
    [3]
    徐国强, 陈德辉, 薛纪善, 等.GRAPES物理过程的优化试验及程序结构设计.科学通报, 2008, 53(20):2428-2434. doi:  10.3321/j.issn:0023-074X.2008.20.006
    [4]
    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. doi:  10.3969/j.issn.1001-7313.2006.06.014
    [5]
    张人禾, 沈学顺.中国国家级新一代业务数值预报系统GRAPES的发展.科学通报, 2008, 53(20):2393-2395. doi:  10.3321/j.issn:0023-074X.2008.20.001
    [6]
    薛纪善, 庄世宇, 朱国富, 等.GRAPES新一代全球/区域变分同化系统研究.科学通报, 2008, 53(20):2408-2417. doi:  10.3321/j.issn:0023-074X.2008.20.003
    [7]
    李耀辉, 赵建华, 薛纪善, 等.基于GRAPES的西北地区沙尘暴数值预报模式及其应用研究.地球科学进展, 2005, 20(9):999-1011. doi:  10.3321/j.issn:1001-8166.2005.09.010
    [8]
    伍红雨, 陈德辉.应用GRAPES模式对贵州暴雨过程的模拟试验.气象, 2006, 32(12):29-35. http://d.old.wanfangdata.com.cn/Periodical/qx200612005
    [9]
    宋煜, 叶成志, 黄振, 等.GRAPES模式对2005年登陆强台风预报检验分析.热带气象学报, 2008, 24(6):694-699. doi:  10.3969/j.issn.1004-4965.2008.06.015
    [10]
    王莉莉, 陈德辉, 赵琳娜.GRAPES气象-水文模式在一次洪水预报中的应用.应用气象学报, 2012, 23(3):274-284. doi:  10.3969/j.issn.1001-7313.2012.03.003
    [11]
    沈学顺, 苏勇, 胡江林, 等.GRAPES_GFS全球中期预报系统的研发和业务化.应用气象学报, 2017, 28(1):1-10. http://qikan.camscma.cn/jams/ch/reader/view_abstract.aspx?file_no=20170101&flag=1
    [12]
    Houtekamer P L, Lefaivre L, Derome J, et al.A system simulation approach to ensemble prediction. Mon Wea Rev, 1996, 124(6):1225-1242. doi:  10.1175/1520-0493(1996)124<1225:ASSATE>2.0.CO;2
    [13]
    Lorenz E N.A study of the predictability of a 28-variable atmospheric model. Tellus, 1965, 17:321-333. doi:  10.1111-j.2153-3490.1965.tb01424.x/
    [14]
    Lorenz E N.Atmospheric predictability experiments with a large numerical model. Tellus, 1982, 34:505-513. doi:  10.1111-j.2153-3490.1982.tb01839.x/
    [15]
    Dalcher A, Kalnay E.Error growth and predictability in operational ECMWF forecasts. Tellus, 1987, 39(A):474-491. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=JJ0215927961
    [16]
    邵爱梅, 希爽, 邱崇践.修正数值天气预报的非系统性误差的变分方法.中国科学(地球科学), 2009, 39(2):235-244.
    [17]
    于海鹏.利用历史资料订正数值模式预报误差研究.兰州:兰州大学, 2016.
    [18]
    Jung T.Systematic errors of the atmospheric circulation in the ECMWF forecasting system. Q J R Meteorol Soc, 2005, 131:1045-1073. doi:  10.1256/qj.04.93
    [19]
    郑志海, 任宏利, 黄建平.基于季节气候可预报分量的相似误差订正方法和数值实验.物理学报, 2009, 58(10):7359-7367. http://d.old.wanfangdata.com.cn/Periodical/wlxb200910114
    [20]
    郑志海, 封国林, 黄建平, 等.基于延伸期可预报性的集合预报方法和数值试验.物理学报, 2012, 61(19):1-8. http://d.old.wanfangdata.com.cn/Periodical/wlxb201219079
    [21]
    郑志海, 黄建平, 封国林, 等.延伸期可预报分量的预报方案和策略.中国科学(地球科学), 2013, 43(4):594-605. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=QKC20132013052100038625
    [22]
    钟剑, 黄思训, 费建芳, 等.模式误差动力特征:模式参数误差和物理过程描绘缺失误差.大气科学, 2011, 35(6):1169-1176. doi:  10.3878/j.issn.1006-9895.2011.06.15
    [23]
    王皓, 郑志海, 于海鹏, 等.国家气候中心大气环流模式冬季模式误差特征分析.物理学报, 2014, 63(9):099202. http://d.old.wanfangdata.com.cn/Periodical/wlxb201409059
    [24]
    于海鹏, 黄建平, 李维京, 等.数值预报误差订正技术中相似-动力方法的发展.气象学报, 2014, 72(5):1012-1022. http://d.old.wanfangdata.com.cn/Periodical/qxxb201405015
    [25]
    Yu H P, Huang J P, Chou J F.Improvement of medium-range forecasts using the analog-dynamical method. Mon Wea Rev, 2014, 142(4):1570-1587. doi:  10.1175/MWR-D-13-00250.1
    [26]
    薛纪善.新世纪初我国数值天气预报的科技创新研究.应用气象学报, 2006, 17(5):602-610. doi:  10.3969/j.issn.1001-7313.2006.05.010
    [27]
    Mlawer E J, Clough S A.Shortwave Clear-sky Model Measurement Intercomparison Using RRTM//Proceedings of the 8th Atmospheric Radiation Measurement (ARM) Science Team Meeting.Tucson, Arizona, USA, 1998: 513-516.
    [28]
    Iacono M J, Mlawer E J, Clough S A, et al.Impact of an improved longwave radiation model, RRTM, on the energy budget and thermodynamic properties of the NCAR community climate model, CCM3. J Geophys Res, 2000, 105(14):14873-14890. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=JJ0210452025
    [29]
    Hong S Y, Pan H L.Nonlocal boundary layer vertical diffusion in a medium-range forecast model. Mon Wea Rev, 1996, 124(10):2322-2339. doi:  10.1175/1520-0493(1996)124<2322:NBLVDI>2.0.CO;2
    [30]
    Dai Y J, and Coauthors.The common land model. Bull Amer Meteor Soc, 2003, 84(8):1013-1023. doi:  10.1175/BAMS-84-8-1013
    [31]
    Hong S Y, Dudhia J, Chen S H.A revised approach to ice microphysical processes for the bulk parameterization of clouds and precipitation. Mon Wea Rev, 2004, 132(1):103-120. doi:  10.1175/1520-0493(2004)132<0103:ARATIM>2.0.CO;2
    [32]
    Lott F, Miller M J.A new subgrid-scale orographic drag parametrization:Its formulation and testing. Q J R Meteorol Soc, 1997, 123(537):101-127. doi:  10.1002/(ISSN)1477-870X
    [33]
    施晓晖, 徐祥德, 谢立安.NCEP/NCAR再分析风速、表面气温距平在中国区域气候变化研究中的可信度分析.气象学报, 2006, 64(6):709-722. doi:  10.3321/j.issn:0577-6619.2006.06.004
    [34]
    周青, 赵凤生, 高文华.NCEP/NCAR逐时分析与中国实测地表温度和地面气温对比分析.气象, 2008, 34(2):83-91. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=qx200802012
    [35]
    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. http://qikan.camscma.cn/jams/ch/reader/view_abstract.aspx?file_no=20170102&flag=1
    [36]
    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. http://qikan.camscma.cn/jams/ch/reader/view_abstract.aspx?file_no=20160101&flag=1
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    • Received : 2018-03-20
    • Accepted : 2018-05-21
    • Published : 2018-09-30
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