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FY-2G云导风资料同化在台风“天鸽”数值预报中的应用
作者:许冬梅1 2 3  束艾青1  李泓4  吴海英5  何志新6  沈菲菲1 2 4  庄雨馨1 
单位:1. 南京信息工程大学气象灾害教育部重点实验室/气候与环境变化国际合作联合实验室/气象灾害预报预警与评估协同创新中心, 江苏 南京 210044;
2. 高原与盆地暴雨旱涝灾害四川省重点实验室, 四川 成都 610225;
3. 中国气象局沈阳大气环境研究所, 辽宁 沈阳 110000;
4.
关键词:FY-2G云导风 WRF模式 3DVAR 数值预报 
分类号:P457.8
出版年·卷·期(页码):2022·39·第一期(56-66)
摘要:
以2017年8月登陆我国的13号台风"天鸽"为个例,采用美国全球预报系统资料作为背景场,利用WRF中尺度数值模式及天气研究和预报模式同化系统中的三维变分模块,探究了新一代静止卫星FY-2G云导风资料同化对台风预报的影响。研究结果表明:云导风资料同化模拟的台风路径、强度和最大风速与实况更加接近。与控制试验相比,云导风资料同化能够为背景场提供丰富的风矢量信息,增强台风周围对流云及其引导气流的强度,从而较好地模拟台风的内部结构,对影响其发展和维持的水汽条件与动力条件进行改进。
In order to explore the influence of FY-2G AMVs data assimilation on typhoon forecast, FY-2G AMVs data assimilation and numerical simulation experiments are conducted in this paper by taking typhoon Hato (No.1713) as an example that landed in China in August 2017. The Global Forecasting System data are used as background, and the mesoscale numerical model and data assimilation method are the WRF and 3DVAR data assimilation module of the Weather Research and Forecasting model Data Assimilation system, respectively. The results show that the typhoon track, intensity, and maximum wind speed simulated with AMVs data assimilation match better with the observation. Compared with the control experiment, AMVs data assimilation can provide abundant wind vector information in the background field and enhance the strength of convective clouds around the typhoon and its steering flow. Therefore, the internal structure of typhoon can be well simulated, and the water vapor condition and dynamic condition that affect the development and maintenance of the typhoon can be improved.
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