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2016年GRAPES_TYM改进及对台风预报影响
引用本文:麻素红,张进,沈学顺,王玉清.2016年GRAPES_TYM改进及对台风预报影响[J].应用气象学报,2018,29(3):257-269.
作者姓名:麻素红  张进  沈学顺  王玉清
作者单位:1.国家气象中心, 北京 100081
基金项目:公益性行业(气象)科研专项(GYHY201406006)
摘    要:为了进一步提高国家气象中心区域模式台风数值预报系统(GRAPES_TYM)的预报能力,2016年对模式参考大气廓线以及涡旋初始化方案进行了改进:由模式初始场水平方向平均的一维参考大气代替原来的等温大气,涡旋初始化方案取消了原涡旋重定位并将涡旋强度调整半径由原来的12°减小到4°。对2014—2016年的生命史超过3 d的所有台风进行了回算,路径及近地面最大风速统计误差分析表明:参考大气的改进可以减小模式对台风预报路径预报的系统北偏和平均路径误差,尤其是140°E以东的转向台风。涡旋初始化方案中强度调整半径的减小会进一步减小模式预报路径的北偏趋势,从而进一步减小平均误差。同业务系统预报结果相比,改进后的GRAPES_TYM(包括参考大气和涡旋初始化)可以使平均路径误差分别减小10%(24 h),12%(48 h),16%(72 h),14%(96 h)以及15%(120 h)。同美国NCEP全球模式路径预报相比,GRAPES_TYM在西行、西北行登陆我国的台风路径预报有一定优势。

关 键 词:参考大气    涡旋初始化    强度调整    路径预报    系统偏差
收稿时间:2017/7/31 0:00:00
修稿时间:2018/2/26 0:00:00

The Upgrade of GRAPE_TYM in 2016 and Its Impacts on Tropical Cyclone Prediction
Ma Suhong,Zhang Jin,Shen Xueshun and Wang Yuqing.The Upgrade of GRAPE_TYM in 2016 and Its Impacts on Tropical Cyclone Prediction[J].Quarterly Journal of Applied Meteorology,2018,29(3):257-269.
Authors:Ma Suhong  Zhang Jin  Shen Xueshun and Wang Yuqing
Institution:1.National Meteorological Center, Beijing 1000812.University of Hawaii, Hawaii 96822, USA
Abstract:The model reference profile and the vortex initialization scheme in GRAPES_TYM of China National Meteorological Center are modified in 2016 to improve the ability of tropical cyclone (TC) track and intensity prediction.The reference atmosphere profile is often applied in numerical weather prediction model to guarantee model integration stability and the accuracy. The reference atmosphere profile of isothermal temperature is replaced by a profile based on the horizontal mean of the model initial condition in GRAPES_TYM. It could decrease the amplitude of the perturbation of potential temperature and pressure and increase the accuracy and stability of model integration.The TC vortex initialization is a key factor to TC track and intensity numerical prediction. The TC vortex initialization scheme in GRAPES_TYM includes two parts:Vortex relocation (the vortex in the analyzed field is moved to the location analyzed by forecasters) and intensity correction. The modification of vortex initialization scheme includes two aspects:The relocation of vortex is removed, the radius of correction of the intensity is reduced to 4° from 12° in order to weaken the influence on the outer circulation of TC, which is assumed well analyzed by global model with higher resolution that provides the initial and boundary conditions for regional model.Experiments are carried out twice a day using 2014-2016 main TCs which last more than 72 h. Results show that upgrade of reference atmosphere profile could reduce the northward bias and the mean track errors especially for the mid-turning typhoon around 140°E. The modification of the radius of the intensity correction from 12° to 4° could improve TC track prediction especially for 0-72 h. GRAPES_TYM with the upgrade of reference profile and vortex initialization scheme could reduce the mean track errors by 10%(24 h), 12%(48 h), 16%(72 h), 14(96 h), and 15%(120 h) compared with the operational system.Results from 2014-2016 are also compared with results of NCEP global forecast system (NCEP-GFS). Mean track errors of GRAPES_TYM are larger than those. The track error differences are 9.2 km(24 h), 17.2 km(48 h), 18.4 km(72 h), 41.1 km(96 h) and 60.3 km(120 h), which are generally within 50 km except for 120 h prediction. GRAPES_TYM performs better than NCEP-GFS for TCs moving westward or northwestward and landing at the coast of China. The mean TC intensity errors of GRAPES_TYM within 72 h are smaller than those of NCEP-GFS.It can be found from the above results that the regional model improvements are more important for TC track prediction compared with improvements of model initial conditions which come from the global model, especially when the resolution of global model get higher and more data are assimilated. Therefore, more efforts should be put into the regional model optimization and improvement in the future.
Keywords:reference profile  vortex initialization  intensity correction  track prediction  system bias
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