【作者】 李新峰；

【导师】 赵坤；

【作者基本信息】 南京大学 ， 气象学， 2012， 硕士

【摘要】 台风结构、强度、路径和降水的准确预报,是数值预报研究领域中的热点和难点问题。近年来,数值模式对台风路径预报准确率持续提高,而对台风强度和风雨预报则进展缓慢,其中一个重要原因是模式初始场不能准确反映台风内核区结构。众所周知,地基多普勒雷达是唯一能监测登陆台风风雨三维精细结构的工具,其获取的高时空分辨率资料有助于改善数值模式初始场精度。本文利用中尺度预报模式ARPS及其三维变分(3DVAR)/云分析系统,以2010年在福建登陆的近海增强台风“莫兰蒂”为例,研究云分辨率尺度下循环同化雷达资料对台风初始场和预报场的影响。首先,研究雷达径向风和反射率因子资料同化对台风分析和预报的作用,其中雷达资料同化窗为6h,同化频率为1h。结果显示,无雷达资料同化,模式初始场和预报的台风强度弱、路径误差大。同化雷达径向速度资料后,初始场台风内核区环流显著增强,台风眼和眼墙位置、地面最大风速与观测接近。随着初始场改进,12h的台风结构、强度、路径和降水预报也显著提高。在此基础上,同化雷达反射率因子可进一步改进降水预报,并准确预报出台风登陆过程中台风内部及其与福建地形交互作用形成的3个强降水区。总体而言,同时同化径向风和反射率因子试验预报效果最好。径向风同化在改进台风强度、路径和结构预报中起主导作用,而反射率因子同化对降水预报起重要作用。这些结果与过去的研究雷达资料同化对强台风短时预报的作用相一致。在雷达资料同化试验基础上,进一步分析循环同化过程中雷达资料的作用以及模式动力和热力响应。结果显示,随着雷达资料同化次数增加,模式的雷达径向速度和反射率因子误差逐渐降低。其中,前期同化使误差降低幅度最大,主要改善涡旋尺度环流结构,而后期同化则主要改善次涡旋尺度结构。径向风同化在台风环流结构分析中起主导作用。在同化窗内模式积分过程中,气压场和温度场逐渐调整,向同化改进的环流场适应。相应的,最低海平面气压(MSLP)误差逐渐降低,台风暖心结构逐渐增强,最终形成动力、热力平衡的台风结构,与观测台风强度一致。最后,设计3组敏感性试验,研究同化配置对台风预报的影响。结果显示同化频率越高、次数越多,台风初始场结构分析和路径强度预报就越好。在雷达资料同化基础上,同化最佳路径集的MSLP资料,可显著改善初始场的强度分析,但这一改进在积分1h内就迅速消失,原因是ARPS的3DVAR是单变量分析,气压场调整时温度场不变,造成模式的温压场不平衡,因此调整的气压场无法维持。同化常规观测和雷达资料可进一步改进台风的环境场,但对台风的结构和强度影响不大。只同化一个能有效观测台风内核区的单雷达资料,也能获取与同化多个雷达接近的分析和预报效果,表明台风内核区结构的雷达观测信息对台风分析和预报起重要作用。增加云分析方案中的湿度场调整频率能提高台风强度预报,但会造成过度降水。更多还原

【Abstract】 Accurate prediction of the track, intensity, structure and precipitation of typhoon has remained a challenging problem in typhoon research area. Over the past decade, typhoon track forecasts have improved steadily, but typhoon intensity and structure forecasts have improved slowly. The lack of accurate initial conditions capturing the internal structure of typhoons has been attributed as one of the main factors. Coastal Doppler radar is the only platform that can observe the three-dimensional structure of typhoons near landfall with sufficiently high temporal and spatial resolutions This study examines the impact of cycling assimilation of radar data from eight coastal ground-based Doppler radars of China, for the analysis and prediction of Typhoon Meranti (2010) making landfall at a cloud-resolving resolution using the ARPS3DVAR and cloud analysis package.Firstly, the impact of radial velocity (Vr) and reflectivity (Z) data individually and in combination are examined. The radar data are assimilated over a6-h period before Meranti landfall through1-hour assimilation cycles, and then twelve-hour predictions are made. All experiments that assimilate radar data produce better structure, intensity, track and precipitation analysis and prediction than the one without radar data assimilation. Vr data lead to a larger improvement to the intensity and track forecast than Z data, while additional Z data further improve the precipitation forecast. Overall, assimilating both Vr and Z data from multiple radars gives the best forecasts. Three local rainfall maxima related to typhoon circulations and their interactions with the complex terrain in the southeast China coastal region are also captured.Secondly, the analysis increments produced by the3DVAR/cloud analysis system, and the dynamic and thermodynamic responses during the forecast step is studied. It is shown that the errors in Vr and Z decrease during the assimilation cycles. The most significant improvement to the model vortex occurs in the first and second cycles, when the background error is larger. In later cycles, the corrections contain mostly subvortex structures. Although the ARPS3DVAR system does not directly update pressure and temperature when analyzing radar data, the pressure and temperature field can responds to the analyzed winds through model adjustments during the assimilation cycles, which leads to the reduction in MSLP and increase in warm core. As a result, a vortex with the balance between the dynamic and thermodynamic fields is established in the final analysis, which is close to the observation.Finally, Sensitivity experiments suggest that the assimilation frequency also affects the analysis and forecast. The experiments with lower assimilation cycles predicted a somewhat weaker typhoon with larger track errors later on than the experiment with hourly cycles, but they still performed much better than the experiment without radar data. Assimilating MSLP from the best track in addition to radar resulted in a vortex whose MSLP is much closer to observed in the analysis, but the benefit was mostly lost within the first hour of free forecast, mainly because of the lack of balance between the pressure and temperature fields analyzed by the3DVAR. Additional assimilation of the conventional observation data helps improve the large-scale environmental conditions in the initial field. Assimilating data from a single Doppler radar with good coverage of the typhoon inner core region is also quite effective, except that it takes one more cycle to establish circulation analyses of a similar quality as the multiple radar case; although, the forecasts using multiple radars are still the best. Increasing frequency of moisture adjustment in the cloud analysis can improve the intensity forecasts, but degrade the track forecast and overpredict precipitation.更多还原