| 四维集合变分同化方法在华南冬季暴雨模拟中的应用 |
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| 引用本文： | 杨雨轩,张立凤,张斌,张明阳,谢宾鹏.四维集合变分同化方法在华南冬季暴雨模拟中的应用[J].热带气象学报,2018,34(2):217-227. |
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| 作者姓名： | 杨雨轩 张立凤 张斌 张明阳 谢宾鹏 |
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| 作者单位： | 1.国防科技大学气象海洋学院，江苏 南京 211101 |
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| 基金项目： | 国家自然科学基金资助41375063 |
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| 摘 要： | 利用WRF（Weather Research and Forecasting）模式和基于本征正交分解的四维集合变分同化方法（POD-4DEnVar），对2015年12月9日一次华南暴雨过程进行多普勒雷达资料同化试验，并与三维变分同化试验（WRF-3DVar）进行对比，讨论了POD-4DEnVar方法中局地化半径对模拟效果的敏感性。结果表明，比较不同化雷达资料的控制试验，WRF-3DVar和WRF-POD-4DEnVar试验的降水模拟结果得到明显改善，且WRF-POD-4DEnVar的降水强度更接近实况。两种同化方法通过改变不同的初始要素达到改进降水模拟效果的目的，3DVar方法通过调整初始风场，间接减弱暴雨发生的水汽条件，POD-4DEnVar方法则直接调整湿度场。在降水过程中，同化试验改变了冷空气活动和水汽通量辐合的模拟结果，从而改善降水的模拟效果。POD-4DEnVar方法对局地化半径比较敏感，随局地化半径增大，同化对风场和湿度场的影响范围扩大，当局地化半径取为200 km时，降水模拟的效果最好。
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| 关 键 词： | 四维集合变分 三维变分 雷达资料 同化 局地化半径 |
| 收稿时间： | 2017-01-12 |
NUMERICAL SIMULATION ANALYSIS OF A HEAVY RAINFALL IN SOUTH CHINA WITH FOUR-DIMENSIONAL ENSEMBLE VARIATIONAL ASSIMILATION |
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| Institution: | 1.College of Meteorology and Oceanography, National University of Defense Technology, Nanjing 211101, China3.Unit 61741 of PLA, Beijing 100094, China |
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| Abstract: | The heavy rain event that occurred in South China on 9 December 2015 is studied by using Weather Research and Forecasting (WRF) and the Proper Orthogonal Decomposition-based four-dimensional ensemble variational assimilation (POD-4DEnVar) method to assimilate real Doppler radar observations. A WRF-3DVar contrast experiment is designed and the sensitivities of localization radius to the simulated precipitation are discussed. The contrast experiment results show that the radar data assimilation both using 3DVar and POD-4DEnVar method improves the prediction of precipitation compared with the control experiment, and the effect of POD-4DEnVar method is better than that of 3DVar method. The two assimilation methods are aimed at improving the effect of precipitation simulation by changing different initial factors. The 3DVar method indirectly reduces the water vapor conditions required for the storm by adjusting the initial wind field, and the POD-4DEnVar method directly adjusts the humidity field. During the precipitation, the assimilation experiments change the activity of the cold air and the water vapor flux convergence, which improves the effect of precipitation simulation. The sensitivity experiments show that the POD-4DEnVar assimilation method is sensitive to the localization radius, and the influences of assimilation on the wind and humidity field increase with the increase of localization radius. The localization radius of about 200km can achieve the best prediction of precipitation. |
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