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Huang Liping, Chen Dehui, Deng Liantang, et al. Main technical improvements of GRAPES_Meso V4.0 and verification. J Appl Meteor Sci, 2017, 28(1): 25-37. DOI: 10.11898/1001-7313.20170103.
Citation: Huang Liping, Chen Dehui, Deng Liantang, et al. Main technical improvements of GRAPES_Meso V4.0 and verification. J Appl Meteor Sci, 2017, 28(1): 25-37. DOI: 10.11898/1001-7313.20170103.
shu

Main Technical Improvements of GRAPES_Meso V4.0 and Verification

  • Numerical Weather Prediction Center of CMA, Beijing 100081

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    Abstract
  • After operational implementation of GRAPES_Meso V3.0 in March 2013, some problems are found, which include over-prediction of precipitation, integration instability, large 2 m temperature forecast errors, insufficient observations assimilated, and coarser resolution. To deal with these problems, a lot of changes are made, mainly including introducing variational quality control scheme, applying the bias correction for sounding humidity observation, assimilating GPS/PW data, FY-2E cloud drift wind and radio occultation observation, increasing resolution of the model, using the fourth horizontal diffusion scheme, adjusting the coupling scheme between dynamic core and WSM6 microphysics parameterization, optimizing land surface model, and improving diagnostic algorithm of composite radar reflectivity. GRAPES_Meso is also upgraded from Version 3.0 to Version 4.0 by integrating all of the progresses mentioned above. One month hindcast experiments are implemented and results show that, compared with GRAPES_Meso V3.0, ETS scores of precipitation forecasts for GRAPES_Meso V4.0 are obviously higher for all five thresholds of 24 h accumulated precipitation, and the bias is largely decreased for light, moderate and heavy rainfall thresholds. The monthly mean precipitation pattern and intensity are both closer to observation, and the detail precipitation distribution can be reproduced better. Daily time evolutions of root mean square errors for 2 m temperature forecasts are very similar, while the amount of V4.0 is much less than that of V3.0. Monthly mean errors are reduced about 1-2℃ over most region of China and even 3-5℃ over some region for 24 h forecast. It is apparent that GRAPES_Meso V4.0 performs better for height, temperature and wind fields, as anomaly correlation coefficients of these fields at 500 hPa are larger and root mean square errors of these fields at 850 hPa are less than those by GRAPES_Meso V3.0. The forecast skill of GRAPES_Meso is largely improved from Version 3.0 to Version 4.0. Also, the unified process control has been implemented for GRAPES_Meso and GRAPES_RAFS (Rapid Analysis and Forecast System), which can reduce the system maintenance and management costs significantly. GRAPES_Meso V4.0 is transitioned into operational run at China National Meteorological Center with horizontal resolution of 0.1°×0.1° and vertical resolution of 50 levels from July 2014 and the whole system running is stable.
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  • Fig  1.   Equitable threat score for 24 h accumulated precipitation forecast with and without GPS/PW data assimilated from 20 Jun to 20 Jul in 2013 (a)0-24 h, (b)24-48 h

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    Fig  2.   Equitable threat score for 24 h accumulated precipitation forecast with and without FY-2E atmospheric wind vector data assimilated from 20 Jun to 20 Jul in 2013 (a)0-24 h, (b)24-48 h

    DownLoad: Full-Size Img PowerPoint

    Fig  3.   Comparison of different GRAPES_Meso level schemes (a) all levels, (b) below 3000 m

    DownLoad: Full-Size Img PowerPoint

    Fig  4.   Correlation coefficients of 24 h and 48 h forecast with and without the 4th horizontal diffusion in Jul 2013 (a)500 hPa height, (b)500 hPa temperature

    DownLoad: Full-Size Img PowerPoint

    Fig  5.   Time evolution of total water vapor (a) and cloud water (b) by GRAPES_Meso V3.0 on 15 Jul 2008

    DownLoad: Full-Size Img PowerPoint

    Fig  6.   Observed and simulated 24-hour accumulated precipitation from 0000 UTC 16 Jul to 0000 UTC 17 Jul in 2008 (a) observation, (b)18-42 h forecast by GRAPES_Meso V3.0, (c)18-42 h forecast after adjusting moisture flux scheme and couple between WSM6 and dynamical core

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    Fig  7.   The root mean square error for 2 m temperature of 12 h and 36 h forecast before and after the improvement of GRAPES_Meso surface energy balance equation

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    Fig  8.   Scores for 24 h accumulated precipitation by GRAPES_Meso V3.0 and V4.0 from 20 Jun to 20 Jul in 2013 (a) ETS of 0-24 h forecast, (b) ETS of 24-48 h forecast, (c) Bias of 0-24 h forecast, (d) Bias of 24-48 h forecast

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    Fig  9.   Monthly mean 24 h accumulated precipitation distribution from 20 Jun to 20 Jul in 2013 (a) observation, (b)24 h forecast of V4.0, (c)24 h forecast of V3.0, (d)48 h forecast of V4.0, (e)48 h forecast of V3.0

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    Fig  10.   Monthly mean error of 2 m temperature of GRAPES_Meso 24 h forecast from 20 Jun to 20 Jul in 2013 (a) V3.0, (b) V4.0

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    Fig  11.   The anomaly correlation coefficient and root mean square error (RMSE) of GRAPES_Meso 24 h and 48 h forecast from 20 Jun to 20 Jul 2013 (a) correlation coefficient of 500 hPa height, (b) RMSE of 850 hPa height, (c) correlation coefficient of 500 hPa temperature, (d) RMSE of 850 hPa temperature, (e) correlation coefficient of 500 hPa zonal wind, (f) RMSE of 850 hPa zonal wind

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    Table  1   Differences between GRAPES_Meso V3.0 and V4.0

    项目 GRAPES_Meso V3.0 GRAPES_Meso V4.0
    观测资料 AOB AOB，GPS/PW，FY-2E
    水平分辨率 0.15° 0.1°
    垂直层次数 L33 L50
    分析系统 无变分质量控制 增加变分质量控制
    无探空湿度偏差订正 增加探空湿度偏差订正
    微物理参数化 WSM6 改进耦合方案的WSM6
    陆面参数化 NOAH 改进地表辐射平衡的NOAH
    辐射参数化 RRTM RRTM (新)
    积云参数化 BMJ KF
    DownLoad: CSV
  • 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
    Xue J S, Zhuang S Y, Zhu G F, et al.Scientific design and preliminary results of three-dimensional variational data assimilation system of GRAPES.Chin Sci Bull, 2008, 53(22):3446-3457. http://www.oalib.com/paper/1304990
    Chen D H, Xue J S, Yang X S, et al.New generation of multi-scale NWP system (GRAPES):General scientific design.Chin Sci Bull, 2008, 53(22):3433-3445. http://www.cnki.com.cn/Article/CJFDTOTAL-JXTW200822003.htm
    Xu G Q, Chen D H, Xue J S, et al.The program structure designing and optimizing tests of GRAPES physics.Chin Sci Bull, 2008, 53(22):3470-3476. https://www.researchgate.net/profile/Xueshun_Shen2/publication/225550173_The_program_structure_designing_and_optimizing_tests_of_GRAPES_physics/links/556edfe208aefcb861dbb247.pdf
    叶成志, 欧阳里程, 李象玉, 等.GRAPES中尺度模式对2005年长江流域重大灾害性降水天气过程预报性能的检验分析.热带气象学报, 2006, 26(4):393-399. http://www.cnki.com.cn/Article/CJFDTOTAL-RDQX200604011.htm
    徐双柱, 张兵, 谌伟.GRAPES模式对长江流域天气预报的检验分析.气象, 2006, 33(11):393-399. http://cpfd.cnki.com.cn/Article/CPFDTOTAL-ZGQX200811010009.htm
    Zhang R H, Shen X S.On the development of the GRAPES-A new generation of the national operational NWP system in China.Chin Sci Bull, 2008, 53(22):3429-3432. https://www.researchgate.net/publication/225916647_On_the_development_of_the_GRAPES-A_new_generation_of_the_national_operational_NWP_system_in_China
    王光辉, 陈峰峰, 沈学顺, 等.数值模式中地形滤波处理及水平扩散对降雨预报的影响.地球物理学报, 2008, 51(6):1642-1650. http://www.cnki.com.cn/Article/CJFDTOTAL-DQWX200806004.htm
    马旭林, 庄照荣, 薛纪善, 等.GRAPES非静力数值预报模式的三维变分资料同化系统的发展.气象学报, 2009, 67(1):50-60. http://www.cnki.com.cn/Article/CJFDTOTAL-QXXB200901007.htm
    沈学顺, 王明欢, 肖锋.GRAPES模式中高精度正定保形物质平流方案的研究Ⅰ:理论方案设计与理想试验.气象学报, 2011, 69(1):1-15. http://www.cnki.com.cn/Article/CJFDTOTAL-QXXB201101001.htm
    沈元芳, 胡江林.GRAPES模式中的坡地辐射方案及其对短期天气过程模拟的影响.大气科学, 2006, 30(6):1129-1137. http://www.cnki.com.cn/Article/CJFDTOTAL-DQXK200606006.htm
    王雨, 李莉.GRAPES_Meso V3.0模式预报效果检验.应用气象学报, 2010, 21(5):393-399. http://qikan.camscma.cn/jams/ch/reader/view_abstract.aspx?file_no=20100502&flag=1
    熊秋芬.GRAPES_Meso模式的降水格点检验和站点检验分析.气象, 2011, 37(2):185-193. http://www.cnki.com.cn/Article/CJFDTotal-QXXX201102009.htm
    陈超君, 王东海, 李国平, 等.冬季高海拔复杂地形下GRAPES-Meso要素预报的检验评估.气象, 2012, 38(6):657-668. http://cpfd.cnki.com.cn/Article/CPFDTOTAL-ZGQX201310002165.htm
    庄照荣, 薛纪善.云迹风资料的三维变分同化及对台风预报的影响试验.热带气象学报, 2004, 20(3):225-236. http://www.cnki.com.cn/Article/CJFDTOTAL-RDQX200403000.htm
    薛湛彬, 龚建东, 何财福, 等.静止卫星云导风的质量控制及在同化中的应用.应用气象学报, 2013, 24(3):356-364. http://qikan.camscma.cn/jams/ch/reader/view_abstract.aspx?file_no=20130312&flag=1
    薛湛彬, 龚建东, 薛纪善, 等.FY-2E卫星云导风定高误差及在同化中的应用.应用气象学报, 2011, 22(6):681-690. http://qikan.camscma.cn/jams/ch/reader/view_abstract.aspx?file_no=20110605&flag=1
    王金成, 龚建东, 邓莲堂.GNSS反演资料在GRAPES_Meso三维变分中的应用.应用气象学报, 2014, 25(6):654-668. http://qikan.camscma.cn/jams/ch/reader/view_abstract.aspx?file_no=20140602&flag=1
    郝民, 张华, 陶士伟, 等.变分质量控制在区域GRAPES-3DVAR中的应用研究.高原气象, 2013, 32(1):122-132. http://www.cnki.com.cn/Article/CJFDTOTAL-GYQX201301014.htm
    郝民, 龚建东, 王瑞文, 等.中国L波段探空湿度观测的质量评估及偏差订正.气象学报, 2015, 73(1):187-199. http://www.cnki.com.cn/Article/CJFDTOTAL-QXXB201501014.htm
    盛春岩, 薛德强, 雷霆, 等.雷达资料同化与提高模式水平分辨率对短时预报影响的数值对比试验.气象学报, 2006, 64(3):293-308. http://www.cnki.com.cn/Article/CJFDTOTAL-QXXB200603003.htm
    Bernadet L R, Grasso L D, Nachamkin J E, et al.Simulating convective events using a high-resolution mesoscale model.J Geophys Res, 2000, 105:14963-14982. DOI: 10.1029/2000JD900100
    Lauritzen P H, Mirin A A, Truesdale J, et al.Implementation of new diffusion/filtering operators in the CAM-FV dynamical core.Int J High Perform Comput Appl, 2012, 26(1):63-73. DOI: 10.1177/1094342011410088
    Chen F, Dudhia J.Coupling an advanced land-surface/hydrology model with the Penn State/NCAR MM5 modeling system.Part Ⅰ:Model description and implementation.Mon Wea Rev, 2001, 129:569-585.
    苏勇, 沈学顺, 张倩, 等.应用样条插值提高GRAPES模式物理过程反馈精度.应用气象学报, 2014, 25(2):202-211. http://qikan.camscma.cn/jams/ch/reader/view_abstract.aspx?file_no=20140210&flag=1
    盛裴轩, 毛节泰, 李建国, 等.大气物理学.北京:北京大学出版社, 2003.
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    • Received : 2016-03-21
    • Accepted : 2016-10-11
    • Published : 2017-01-30
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