Typhoon initialization in the CMA global forecast system
-
摘要: 在CMA-GFS(CMA Global Forecast System)全球四维变分资料同化系统(4DVar)基础上,参照BDA(Bogus Data Assimilation)方法,建立了一个全球模式台风初始化方案。该方案通过4DVar同化窗口吸收诊断处理后的1 h间隔台风中心定位及中心气压信息,利用模式动力物理约束产生台风环流。同时,考虑到模式对台风的分辨能力,中心气压数据误差采用动态调整技术。2016年西北太平洋22个台风的试验表明,新方案不仅可以促进初始场中台风环流的生成,还可以显著减小CMA-GFS全球预报系统的台风路径和强度预报平均误差,具有业务应用前景。
-
关键词:
- CMA-GFS全球预报系统 /
- 台风初始化 /
- 四维变分资料同化系统(4DVar) /
- 台风预报
Abstract: A global model typhoon initialization scheme is developed based on the global four-dimensional variational data assimilation (4DVar) of CMA global forecast system (CMA-GFS) with reference to the BDA (Bogus Data Assimilation) method. The scheme assimilates 1 h interval information of typhoon center location and central sea level pressure diagnosed by interpolation method, and generates typhoon circulation using the model dynamic physical constraints within the 4DVar assimilation window. At the same time, errors in the central sea surface pressure are dynamically corrected using a technique that takes into account the model's ability to describe typhoon circulation. Experiments of 22 typhoon cases that occurred in the Northwest Pacific in 2016 show that the new scheme not only promotes the generation of typhoon circulation in the initial field, but also significantly reduces errors in the mean typhoon track and intensity predicted by the CMA global forecast system. Therefore, this scheme has a potential for operational application.-
Key words:
- CMA global forecast system /
- Typhoon initialization /
- 4DVar /
- Typhoon prediction.
-
图 3 2016年7月29日12时00分预报48 h后台风Nida 海平面气压场 (a、b,单位:hPa) 和 风场 v 分量 (c、d,单位:m/s) 垂直剖面 (沿台风中心) 分布 (a、c. 对照预报,b、d. 试验预报)
Figure 3. 48 hour forecast of sea level pressure (a,b;unit:hPa) and vertical cross section of v-wind (c,d;unit:m/s) along the typhoon center for typhoon Nida valid at 12:00 UTC 29 July 2016 (a,c. control,b,d. experiment)
图 4 对照预报和试验方案于2016年7月29日12时00分预报的台风Nida移动路径和实况对比 (实线是观测路径,短虚线、点虚线分别是对照预报和试验方案的预报路径;间隔6 h)
Figure 4. TC track forecasts (6 h intervals output) from control (dashed line) and experiment (dot-dashed line) runs initialized at 12:00 UTC 29 July 2016 plotted against best-track observed (solid line) TC track (6-h intervals) for Typhoon Nida
图 5 2016年9月10日03时00分台风Meranti 海平面气压场 (a、b,单位:hPa) 和 (c、d) 风场 u 分量 (c、d,单位:m/s) 垂直剖面 (沿139.8°E) 分布 (a、c. 背景场,b、d. 分析场)
Figure 5. Sea level pressure (a,b;unit:hPa) and vertical cross section of u-wind (c,d;unit:m/s) along 139.8°E for typhoon Meranti valid at 03:00 UTC 10 September 2016 (a,c. background,b,d. analysis)
图 6 应用3 h (三角形)、1 h (正方形) 间隔台风中心气压数据同化后,CMA-GFS全球预报系统预报的台风平均 (a) 移速误差和 (b) 移向误差
Figure 6. Mean forecast errors in typhoon moving speed (a) along-track and direction (b) cross-track predicted by CMA global forecast system with assimilation of 3-hourly (triangles) and 1-hourly (squares) central pressure
-
[1] 龚建东,王瑞春,郝民. 2016. 温湿统计平衡约束关系对GRAPES全球湿度分析的作用. 气象学报,74(3):380-396 doi: 10.11676/qxxb2016.035Gong J D,Wang R C,Hao M. 2016. The impact of a balance constraint between temperature and humidity on the global humidity analysis in GRAPES. Acta Meteor Sinica,74(3):380-396 (in Chinese) doi: 10.11676/qxxb2016.035 [2] 龚建东,张林,王金成. 2020. 背景误差水平相关结构对四维变分资料同化的影响研究. 气象学报,78(6):988-1001 doi: 10.11676/qxxb2020.062Gong J D,Zhang L,Wang J C. 2020. An impact study of background error horizontal correlation structure on 4DVar. Acta Meteor Sinica,78(6):988-1001 (in Chinese) doi: 10.11676/qxxb2020.062 [3] 黄伟,梁旭东. 2010. 台风涡旋循环初始化方法及其在GRAPES-TCM中的应用. 气象学报,68(3):365-375 doi: 10.11676/qxxb2010.036Huang W,Liang X D. 2010. A cycling typhoon-like vortex initialization scheme and its application to GRAPES-TCM. Acta Meteor Sinica,68(3):365-375 (in Chinese) doi: 10.11676/qxxb2010.036 [4] 刘艳,薛纪善. 2019. GRAPES的新初始化方案. 气象学报,77(2):165-179Liu Y,Xue J S. 2019. The new initialization scheme of the GRAPES. Acta Meteor Sinica,77(2):165-179 (in Chinese) [5] 刘永柱,龚建东,张林等. 2019. 线性化物理过程对GRAPES 4DVar同化的影响. 气象学报,77(2):196-209 doi: 10.11676/qxxb2019.013Liu Y Z,Gong J D,Zhang L,et al. 2019. Influence of linearized physical processes on the GRAPES 4DVar. Acta Meteor Sinica,77(2):196-209 (in Chinese) doi: 10.11676/qxxb2019.013 [6] 瞿安祥,麻素红,Liu Q F等. 2009a. 全球数值模式中的台风初始化Ⅰ:方案设计. 气象学报,67(5):716-726Qu A X,Ma S H,Liu Q F,et al. 2009a. The initialization of tropical cyclones in the NMC global model Part Ⅰ:Scheme design. Acta Meteor Sinica,67(5):716-726 (in Chinese) [7] 瞿安祥,麻素红,李娟等. 2009b. 全球数值模式中的台风初始化Ⅱ:业务应用. 气象学报,67(5):727-735Qu A X,Ma S H,Li J,et al. 2009b. The initialization of tropical cyclones in the NMC global model Part Ⅱ:Implementation. Acta Meteor Sinica,67(5):727-735 (in Chinese) [8] 瞿安祥,麻素红,张进. 2016. T639全球模式的台风初始化方案升级试验. 气象,42(6):664-673 doi: 10.7519/j.issn.1000-0526.2016.06.002Qu A X,Ma S H,Zhang J. 2016. Updated experiments of tropical cyclone initialization in global model T639. Meteor Mon,42(6):664-673 (in Chinese) doi: 10.7519/j.issn.1000-0526.2016.06.002 [9] 苏勇,沈学顺,陈子通等. 2018. GRAPES_GFS中三维参考大气的研究:理论设计和理想试验. 气象学报,76(2):241-254 doi: 10.11676/qxxb2017.097Su Y,Shen X S,Chen Z T,et al. 2018. A study on the three-dimensional reference atmosphere in GRAPES_GFS:Theoretical design and ideal test. Acta Meteor Sinica,76(2):241-254 (in Chinese) doi: 10.11676/qxxb2017.097 [10] 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. 气象学报,72(1):62-78 doi: 10.11676/qxxb2014.008Wang J C,Zhuang Z R,Han W,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. Acta Meteor Sinica,72(1):62-78 (in Chinese) doi: 10.11676/qxxb2014.008 [11] 王金成,龚建东,王瑞春. 2016. GRAPES全球三维变分同化中卫星微波温度计亮温的背景误差及在质量控制中的应用. 气象学报,74(3):397-409 doi: 10.11676/qxxb2016.026Wang J C,Gong J D,Wang R C. 2016. Estimation of background error for brightness temperature in GRAPES 3DVar and its application in radiance data background quality control. Acta Meteor Sinica,74(3):397-409 (in Chinese) doi: 10.11676/qxxb2016.026 [12] 张林,刘永柱. 2017. GRAPES全球四维变分同化系统极小化算法预调节. 应用气象学报,28(2):168-176 doi: 10.11898/1001-7313.20170204Zhang L,Liu Y Z. 2017. The preconditioning of minimization algorithm in GRAPES global four-dimensional variational data assimilation system. J Appl Meteor Sci,28(2):168-176 (in Chinese) doi: 10.11898/1001-7313.20170204 [13] Aberson S D. 2008. Large forecast degradations due to synoptic surveillance during the 2004 and 2005 hurricane seasons. Mon Wea Rev,136(8):3138-3150 doi: 10.1175/2007MWR2192.1 [14] Barnes S L. 1964. A technique for maximizing details in numerical weather map analysis. J Appl Meteor Climatol,3(4):396-409 doi: 10.1175/1520-0450(1964)003<0396:ATFMDI>2.0.CO;2 [15] Dvorak V F. 1975. Tropical cyclone intensity analysis and forecasting from satellite imagery. Mon Wea Rev,103(5):420-430 doi: 10.1175/1520-0493(1975)103<0420:TCIAAF>2.0.CO;2 [16] Heming J T. 2009. Evaluation of and improvements to the Met Office tropical cyclone initialisation scheme. Meteor Appl,16(3):339-351 doi: 10.1002/met.129 [17] Heming J T. 2016. Met Office unified model tropical cyclone performance following major changes to the initialization scheme and a model upgrade. Wea Forecasting,31(5):1433-1449 doi: 10.1175/WAF-D-16-0040.1 [18] Kleist D T. 2011. Assimilation of tropical cyclone advisory minimum sea level pressure in the NCEP global data assimilation system. Wea Forecasting,26(6):1085-1091 doi: 10.1175/WAF-D-11-00045.1 [19] Knaff J A,Zehr R M. 2007. Reexamination of tropical cyclone wind-pressure relationships. Wea Forecasting,22(1):71-88 doi: 10.1175/WAF965.1 [20] Marchok T. 2021. Important factors in the tracking of tropical cyclones in operational models. J Appl Meteor Climatol,60(9):1265-1284 [21] Wang D L,Liang X D,Zhao Y,et al. 2008. A comparison of two tropical cyclone bogussing schemes. Wea Forecasting,23(1):194-204 doi: 10.1175/2007WAF2006094.1 [22] Xiao Q N,Kuo Y H,Zhang Y,et al. 2006. A tropical cyclone bogus data assimilation scheme in the MM5 3D-Var system and numerical experiments with typhoon Rusa (2002) near landfall. J Meteor Soc Japan,84(4):671-689 doi: 10.2151/jmsj.84.671 [23] Zhang X Y,Wang B,Ji Z Z,et al. 2003. Initialization and simulation of a typhoon using 4-dimensional variational data assimilation:Research on typhoon Herb (1996). Adv Atmos Sci,20(4):612-622 doi: 10.1007/BF02915504 [24] Zhao Y,Wang B,Ji Z Z,et al. 2005. Improved track forecasting of a typhoon reaching landfall from four-dimensional variational data assimilation of AMSU-A retrieved data. J Geophys Res Atmos,110(D14):D14101 [25] Zhao Y,Wang B,Wang Y. 2007. Initialization and simulation of a landfalling typhoon using a variational bogus mapped data assimilation (BMDA). Meteor Atmos Phys,98(3):269-282 [26] Zou X L,Xiao Q N. 2000. Studies on the initialization and simulation of a mature hurricane using a variational bogus data assimilation scheme. J Atmos Sci,57(6):836-860 doi: 10.1175/1520-0469(2000)057<0836:SOTIAS>2.0.CO;2 -