| 多普勒雷达资料循环同化在台风“鲇鱼”预报中的应用 |
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| 引用本文： | 李新峰,赵坤,王明筠,明杰.多普勒雷达资料循环同化在台风“鲇鱼”预报中的应用[J].气象科学,2013,33(3):255-263. |
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| 作者姓名： | 李新峰 赵坤 王明筠 明杰 |
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| 作者单位： | 中尺度灾害性天气教育部重点实验室,南京大学大气科学学院,南京210093 |
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| 基金项目： | 公益性行业(气象)科研专项(GYHY201006007);国家自然基金项目(40975011;41105035);国家重点基础研究发展计划项目(2009CB421502) |
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| 摘 要： | 高分辨率的中尺度预报模式ARPS及其3DVAR/云分析系统,针对2010年登陆福建的超强台风“鲇鱼”,研究对流可分辨尺度下,每1h循环同化沿海新一代多普勒雷达网资料分析、研究对台风初始场和预报场的改进作用.结果表明:单独同化雷达资料可显著改善初始场中的台风内核区动力和热力结构,以及台风强度和位置,进而提高18h台风强度、路径和降水预报,但预报路径和降水分布与实况仍存在差异.在雷达资料同化基础上加入常规观测资料,对初始场中台风内核区结构改进不大.但在显著调整大尺度背景场,从而进一步减少台风路径预报误差,能准确预报出福建沿海两个强降水区域的位置和强度.总体而言,雷达资料同化主要提高台风结构分析,而常规观测资料同化主要改善环境场分析,两者有效结合使得预报结果和实况最为接近.
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| 关 键 词： | 台风 ARPS 3DVAR/云分析 循环同化 多普勒雷达 |
| 收稿时间： | 2012/5/16 0:00:00 |
| 修稿时间： | 2012/5/16 0:00:00 |
Short-term forecasting of super typhoon Megi at landfall through cycling assimilation of China coastal radar data |
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| LI Xinfeng,ZHAO Kun,WANG Mingyun and MING Jie.Short-term forecasting of super typhoon Megi at landfall through cycling assimilation of China coastal radar data[J].Scientia Meteorologica Sinica,2013,33(3):255-263. |
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| Authors: | LI Xinfeng ZHAO Kun WANG Mingyun and MING Jie |
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| Institution: | Key Laboratory for Mesoscale of Severe Weather/MOE, School of Atmosphere Sciences, Nanjing University, Nanjing 210093, China;Key Laboratory for Mesoscale of Severe Weather/MOE, School of Atmosphere Sciences, Nanjing University, Nanjing 210093, China;Key Laboratory for Mesoscale of Severe Weather/MOE, School of Atmosphere Sciences, Nanjing University, Nanjing 210093, China;Key Laboratory for Mesoscale of Severe Weather/MOE, School of Atmosphere Sciences, Nanjing University, Nanjing 210093, China |
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| Abstract: | The impact of cycling assimilation of radar data on the initial and prediction field of landfalling super typhoon Megi (2010) by cloud-resolving resolution is examined by the ARPS 3DVAR/cloud analysis. Results show that assimilating radar reflectivity and radial velocity data alone can significantly improve the inner-core kinematic and thermodynamic structure, typhoon position and intensity of initial field, then improve the subsequent 18 h forecast of typhoon intensity, track and precipitation, but there still existed some errors between predicted track and precipitation distribution and the observation. Assimilating additional conventional observation data can help further improve the large-scale environmental conditions in the initial field, thus improve the 18 h track and precipitation forecast. In this case, two rainfall maxima along the coast of Fujian province are predicted quite well. Overall, radar data assimilation mainly improves the typhoon structure analysis, while the conventional observation data improve the large-scale environmental conditions most. Best forecasts are obtained if both radar and conventional data can be combined. |
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| Keywords: | Typhoon ARPS 3DVAR/cloud analysis Cycling assimilation Doppler radar |
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