| 集合预报最优ETKF初始扰动方法设计及其在暴雨中的应用 |
| |
| 引用本文： | 闵锦忠,蔡瑾婕,刘畅.集合预报最优ETKF初始扰动方法设计及其在暴雨中的应用[J].气象科学,2018,38(5):565-574. |
| |
| 作者姓名： | 闵锦忠 蔡瑾婕 刘畅 |
| |
| 作者单位： | 南京信息工程大学 气象灾害预报预警与评估协同创新中心, 南京 210044;南京信息工程大学 气象灾害教育部重点实验室, 南京 210044,南京信息工程大学 气象灾害预报预警与评估协同创新中心, 南京 210044;南京信息工程大学 气象灾害教育部重点实验室, 南京 210044,南京信息工程大学 气象灾害预报预警与评估协同创新中心, 南京 210044;南京信息工程大学 气象灾害教育部重点实验室, 南京 210044 |
| |
| 基金项目： | 国家自然科学基金重点资助项目（41430427）；NSFC-广东联合基金（第二期）超级计算科学应用研究专项资助；国家超级计算机广州中心支持 |
| |
| 摘 要： | 为改进集合转换卡尔曼滤波方法(Ensemble Transform Kalman Filter,ETKF)在初始扰动中离散度偏小的问题,考虑引入物理不确定性。使用初始时刻离散度检验两种ETKF初始扰动方案改进的程度,通过动力和水汽条件分析探求改进机制。利用WRF模式构建更新预报系统,选取2014年5月一次暴雨个例进行集合降水预报试验,通过ETKF方法设计两种不同的初始扰动方案。结果表明:在分析循环中引入多物理扰动的初始扰动方案(multi)相比单一物理过程的初始扰动方案(mono)在初始时刻离散度和模拟动力水汽条件以及降水评分上均有较大改进。初始扰动中multi的离散度相比mono整体更优,显然添加了多物理扰动方案的试验对结果有改进作用;在对两种方案的机理分析中,multi对于降水位置的明显改善主要取决于散度及水汽通量散度模拟能力的提高;在离散度分析中,multi方案在强对流区域的改进效果比在整个区域中的更好,而对各变量的离散度和均方根误差之比相当,说明集合预报系统的合理性;对各量级预报结果评分显示,multi方案均呈现较好表现能力。
|
| 关 键 词： | 风暴尺度 集合预报 集合转换卡尔曼滤波 初始扰动 |
| 收稿时间： | 2017/6/1 0:00:00 |
| 修稿时间： | 2017/6/6 0:00:00 |
Optimal design of ETKF in storm-scale ensemble forecast: A case study |
| |
| MIN Jinzhong,CAI Jinjie and LIU Chang.Optimal design of ETKF in storm-scale ensemble forecast: A case study[J].Scientia Meteorologica Sinica,2018,38(5):565-574. |
| |
| Authors: | MIN Jinzhong CAI Jinjie and LIU Chang |
| |
| Institution: | Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science & Technology, Nanjing 210044, China;Key Laboratory of Meteorological Disaster, Ministry of Education, Nanjing University of Information Science & Technology, Nanjing 210044, China,Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science & Technology, Nanjing 210044, China;Key Laboratory of Meteorological Disaster, Ministry of Education, Nanjing University of Information Science & Technology, Nanjing 210044, China and Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science & Technology, Nanjing 210044, China;Key Laboratory of Meteorological Disaster, Ministry of Education, Nanjing University of Information Science & Technology, Nanjing 210044, China |
| |
| Abstract: | In order to improve the low-spread of Ensemble Transform Kalman Filter (ETKF) in initial perturbation, we consider the introduction of physical uncertainty. The relative improvement degree of the two ETKF initial perturbation schemes was tested by using the initial spread. We explored the improvement mechanism by means of dynamic and water vapor condition analysis. The WRF model was used to construct an updated forecast system. One extreme heavy precipitation which happened in May, 2014 was simulated and it was made ensemble forecasting. Two initial perturbation schemes were designed by using ETKF method. One was a single physical scheme; the other was on the basis of scheme one, adding the multi-physics process in the analyzing perturbation, and the samephysical parameterization setting is adopted after the beginning of the forecasting process. The results show that the multi-scheme with multi-physical perturbation in the analysis cycle is much better than mono-ETKF scheme in initial moment spread and simulated dynamic and water vapor conditions as well as precipitation ratings. Compared with mono, the initial moment spread of multi-scheme is obviously better. It is obvious that the experiments with multi-physical perturbation scheme can improve the results, and provide more forecast information including forecast uncertainty to user decision. In the mechanism analysis of the two schemes, the significant improvement of the precipitation scheme for the multi scheme is mainly depends of the making the divergence and the moisture flux divergence better. In the analysis of spread, the improvement effect of multi-scheme in strong convection area is better than that in whole area, and the ratio of spread to root-mean-square error of each variable is roughly equal, and the rationality of ensemble forecasting system is explained. The results show that the multi-physics program shows better performance. |
| |
| Keywords: | Storm-scale Ensemble forecast Ensemble transform kalman filter Initial perturbation |
| 本文献已被 CNKI 等数据库收录！ |
| 点击此处可从《气象科学》浏览原始摘要信息 |
| 点击此处可从《气象科学》下载免费的PDF全文 |
|
