Zhang Meng, Yu Haipeng, Huang Jianping, et al. Assessment on unsystematic errors of GRAPES_GFS 2.0. J Appl Meteor Sci, 2019, 30(3): 332-344. DOI:  10.11898/1001-7313.20190307.
Citation: Zhang Meng, Yu Haipeng, Huang Jianping, et al. Assessment on unsystematic errors of GRAPES_GFS 2.0. J Appl Meteor Sci, 2019, 30(3): 332-344. DOI:  10.11898/1001-7313.20190307.

Assessment on Unsystematic Errors of GRAPES_GFS 2.0

DOI: 10.11898/1001-7313.20190307
  • Received Date: 2018-11-20
  • Rev Recd Date: 2019-02-26
  • Publish Date: 2019-05-31
  • Unsystematic error is one of main sources of model simulation error, which is mainly induced by initial field error and model defect. Global and Regional Assimilation and Prediction System (GRAPES) global model forecast data and final analysis data made by National Centers for Environmental Prediction (NCEP) during January, April, July, October in 2014 are chosen to be compared and analyzed. In terms of temporal variation, conclusion could be made that peaks of unsystematic errors in both north and south hemispheres occur in their respective winters, and errors show periodical changes. With the increase of forecasting time, the model unsystematic mean square error along with geopotential height field increases over time, first in an exponential function way, and then linear growth. In addition, linear growth in temperature field and zonal wind field are discovered. It shows in the consequence that the large value of model unsystematic mean square error center in mid-latitude and distribute like zonal banded. Large value regions basically do not change along with forecast time. In zonal average geopotential height field and zonal wind field, large value regions are found in tropopause, whereas it is found in boundary layer in temperature field. The cause is that the parameterization scheme does not fully describe differences of these two stratifications in physical process and dynamic framework. It is worth mentioning that the error of the increase in the height of the temperature field at middle and upper levels of the troposphere decreasing. After fitting the error-time line, the error of the initial field, the upper limit of the prediction and the proportion error taken up by the initial field in the south hemisphere are all higher than that in the north hemisphere. Besides, it is found that the initial field gradually decreases in proportion with height increasing. It shows in the above results that the precision of GRAPES_GFS 2.0 for the simulation of mid-latitudes of the south hemisphere and the entire troposphere should be further improved. In order to discover internal physical mechanism of model forecast's defect, and correct error target, it is necessary to research the feature of unsystematic error of GRAPES global model, and analyze the spatial-temporal evolution of the error.
  • Fig. 1  Forecast unsystematic errors of 500 hPa geopotential height field by GRAPES_GFS 2.0 with lead time of 1 d(a), 3 d(b), 5 d(c), 8 d(d) based on the average of selected time(the shaded)

    (solid lines denote mean field by NCEP FNL, unit:gpm)

    Fig. 2  Empirical orthogonal function(EOF) analysis of unsystematic errors in 500 hPa geopotential height field based on the average of selected time

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

    Fig. 3  Mean unsystematic errors(the shaded) of 500 hPa geopotential height field by GRAPES_GFS 2.0 with lead time of 2 days

    (solid lines denote mean field by NCEP FNL, unit:gpm) (a)January 2014, (b)April 2014, (c)July 2014, (d)October 2014

    Fig. 4  Forecast unsystematic errors(the shaded) of 500 hPa temperature field by GRAPES_GFS 2.0 with the lead time of 1 day(a), 3 days(b), 5 days(c), 8 days(d) based on the average of selected time

    (solid lines denote mean field by NCEP FNL, unit:K)

    Fig. 5  Forecast unsystematic errors(the shaded) in zonal average temperature field by GRAPES_GFS 2.0 with lead time of 1 day(a), 3 days(b), 5 days(c), 8 days(d) based on the average of selected time

    (solid lines denote mean field by NCEP FNL, unit:K)

    Fig. 6  Forecast unsystematic errors(the shaded) of 200 hPa zonal wind field by GRAPES_GFS 2.0 with lead time of 1 day(a), 3 days(b), 5 days(c), 8 days(d) based on the average of selected time

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

    Fig. 7  Forecast unsystematic errors(the shaded) of 850 hPa zonal wind field by GRAPES_GFS 2.0 with lead time of 1 day(a), 3 days(b), 5 days(c), 8 days(d) based on the average of selected time

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

    Fig. 8  Forecast unsystematic errors(the shaded) of zonal average wind field by GRAPES_GFS 2.0 with lead time of 1 day(a), 3 days(b), 5 days(c), 8 days(d) based on the average of selected time

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

    Fig. 9  Unsystematic error of geopotential height field(a), temperature field(b), zonal wind field(c) at 500 hPa by GRAPES_GFS 2.0 along with lead time based on the average of selected time

    Fig. 10  Unsystematic error of geopotential height field(a), temperature field(b), zonal wind field(c) by GRAPES_GFS 2.0 along with height based on the average of selected time

  • [1]
    陈德辉, 薛纪善.数值天气预报业务模式现状与展望.气象学报, 2004, 62(5):623-633. http://d.old.wanfangdata.com.cn/Periodical/qxxb200405009
    [2]
    潘留杰, 张宏芳, 王建鹏.数值天气预报检验方法研究进展.地球科学进展, 2014, 29(3):327-335. http://d.old.wanfangdata.com.cn/Periodical/dqkxjz201403003
    [3]
    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/
    [4]
    丑纪范, 郑志海, 孙树鹏.10~30 d延伸期数值天气预报的策略思考——直面混沌.气象科学, 2010, 30(5):569-573. doi:  10.3969/j.issn.1009-0827.2010.05.001
    [5]
    陈静, 陈德辉.集合数值预报发展与研究进展.应用气象学报, 2002, 13(4):497-507. http://qikan.camscma.cn/jams/ch/reader/view_abstract.aspx?file_no=20020465&flag=1
    [6]
    达朝究, 穆帅, 马德山.基于Lorenz系统的数值天气转折期预报理论探索.物理学报, 2014, 63(2):029201. http://d.old.wanfangdata.com.cn/Periodical/wlxb201402058
    [7]
    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
    [8]
    Dalcher A, Kalnay E.Error growth and predictability in operational ECMWF forecasts.Tellus A, 1987, 39:474-491. doi:  10.3402/tellusa.v39i5.11774
    [9]
    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(D辑), 2009, 39(2):235-244. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=QK200900199808
    [10]
    Danforth C M, Kalnay E, Miyoshi T.Estimating and correcting global weather model error.Mon Wea Rev, 2007, 135:281-299. doi:  10.1175/MWR3289.1
    [11]
    Hu S J, Qiu C Y, Zhang L Y et al.An approach to estimating and extrapolating model error based on inverse problem methods:Towards accurate numerical weather prediction.Chin Phys B, 2014, 23(8):089201. doi:  10.1088/1674-1056/23/8/089201
    [12]
    丑纪范, 任宏利.数值天气预报——另类途径的必要性和可行性.应用气象学报, 2006, 17(2):240-244. http://qikan.camscma.cn/jams/ch/reader/view_abstract.aspx?file_no=20060240&flag=1
    [13]
    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
    [14]
    Reynolds C A, Webster P J, Kalnay E.Random error growth in NMC's global forecasts.Mon Wea Rev, 1994, 122:1281-1305. doi:  10.1175/1520-0493(1994)122<1281:REGING>2.0.CO;2
    [15]
    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. http://qikan.camscma.cn/jams/ch/reader/view_abstract.aspx?file_no=200606125&flag=1
    [16]
    胡江林, 沈学顺, 张红亮, 等.GRAPES模式动力框架的长期积分特征.应用气象学报, 2007, 18(3):276-284. http://qikan.camscma.cn/jams/ch/reader/view_abstract.aspx?file_no=20070349&flag=1
    [17]
    张萌, 于海鹏, 黄建平, 等.GRAPES_GFS 2.0模式系统误差评估.应用气象学报, 2018, 29(5):571-583. http://qikan.camscma.cn/jams/ch/reader/view_abstract.aspx?file_no=20180506&flag=1
    [18]
    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
    [19]
    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
    [20]
    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
    [21]
    Dai Y J, Dickinson R E, Wang Y P.A two-big-leaf model for canopy temperature, photosynthesis, and stomatal conductance.J Climate, 2004, 17(12):2281-2299. doi:  10.1175/1520-0442(2004)017<2281:ATMFCT>2.0.CO;2
    [22]
    Han J, Pan H L.Revision of convection and vertical diffusion schemes in the NCEP global forecast system.Wea Forecasting, 2011, 26(4):520-533. doi:  10.1175/WAF-D-10-05038.1
    [23]
    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
    [24]
    McFarlane N A.The effects of orographically excited gravity waves on the general circulation of the lower stratosphere and troposphere.J Atmos Sci, 1987, 44:1775-1800. doi:  10.1175/1520-0469(1987)044<1775:TEOOEG>2.0.CO;2
    [25]
    Palmer T, Shutts G J, Swinbank R.Alleviation of a systematic westerly bias in general circulation and numerical weather prediction models through an orographic gravity wave drag parameterization.Q J R Meteorol Soc, 1986, 112:1001-1039. doi:  10.1002/(ISSN)1477-870X
    [26]
    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
    [27]
    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
  • 加载中
  • -->

Catalog

    Figures(10)

    Article views (4247) PDF downloads(126) Cited by()
    • Received : 2018-11-20
    • Accepted : 2019-02-26
    • Published : 2019-05-31

    /

    DownLoad:  Full-Size Img  PowerPoint