| EnKF局地化算法对雷达资料同化的影响研究 |
| |
| 引用本文： | 高士博,闵锦忠,黄丹莲.EnKF局地化算法对雷达资料同化的影响研究[J].大气科学学报,2016,39(5):633-642. |
| |
| 作者姓名： | 高士博 闵锦忠 黄丹莲 |
| |
| 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, 江苏 南京 210044;南京信息工程大学 气象灾害教育部重点实验室, 江苏 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, 江苏 南京 210044;南京信息工程大学 气象灾害教育部重点实验室, 江苏 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, 江苏 南京 210044;南京信息工程大学 气象灾害教育部重点实验室, 江苏 南京 210044 |
| |
| 基金项目： | 国家重点基础研究发展计划（973计划）项目（2013CB43013）；江苏省普通高校研究生科研创新计划项目（KYLX_0829；KYLX_0844）；国家自然科学基金重点资助项目（41430427）；江苏省高校自然科学重大基础研究项目（11KJA170001）；江苏省气象科学研究所北极阁基金（BJG201510） |
| |
| 摘 要： | 分级集合滤波(Hierarchical Ensemble Filter,HEF)和采样误差修正(Sampling Error Correction,SEC)局地化算法能够使采样误差取得极小值,且不需要给出距离的定义。为了检验其理论优势,基于集合卡尔曼滤波(Ensemble Kalman Filter,En KF)方法同化模拟雷达资料,通过与Gaspari-Cohn(GC)局地化算法对比,分析不同局地化算法对En KF同化效果的影响。结果表明,HEF和SEC局地化算法的雷达回波在水平和垂直方向上均强于GC局地化算法。HEF局地化算法各个变量的离散度最高,均方根误差最低;SEC局地化算法离散度略低,均方根误差略高;GC局地化算法离散度最低,均方根误差最高。相比于GC局地化算法,HEF和SEC局地化算法的冷池强度减弱,面积减小,下沉气流的速度和范围增大,雹霰混合比的大小和覆盖面积增大。通过模拟发现,HEF局地化算法模拟的北侧对流中心最强,SEC局地化算法模拟的南侧对流中心最强,且模拟出(40 km,60 km)处的强对流中心。HEF局地化算法模拟的冷池强度最强,HEF和SEC局地化算法基本上模拟出北侧的雹霰混合比高值区。这表明HEF局地化算法有效地改进了基于GC局地化算法的En KF雷达资料同化效果,SEC局地化算法减小了计算量,是HEF局地化算法较好的近似。
|
| 关 键 词： | 集合卡尔曼滤波 雷达资料同化 HEF局地化算法 SEC局地化算法 GC局地化算法 |
| 收稿时间： | 2015/5/29 0:00:00 |
| 修稿时间： | 2015/10/8 0:00:00 |
Research on the impact of localization methods on radar data assimilation using the ensemble Kalman filter |
| |
| GAO Shibo,MIN Jinzhong and HUANG Danlian.Research on the impact of localization methods on radar data assimilation using the ensemble Kalman filter[J].大气科学学报,2016,39(5):633-642. |
| |
| Authors: | GAO Shibo MIN Jinzhong and HUANG Danlian |
| |
| Institution: | Collaborative Innovation Center on the Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science & Technology, Nanjing 210044, China;Key Laboratory of Meteorological Disaster of the Ministry of Education, Nanjing University of Information Science & Technology, Nanjing 210044, China;Collaborative Innovation Center on the Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science & Technology, Nanjing 210044, China;Key Laboratory of Meteorological Disaster of the Ministry of Education, Nanjing University of Information Science & Technology, Nanjing 210044, China;Collaborative Innovation Center on the Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science & Technology, Nanjing 210044, China;Key Laboratory of Meteorological Disaster of the Ministry of Education, Nanjing University of Information Science & Technology, Nanjing 210044, China |
| |
| Abstract: | The hierarchical ensemble filter(HEF) and sampling error correction(SEC) localization methods can minimize sampling error without giving definition of physical distance.To examine the advantages of the two methods and the possibility of applying them to storm-scale assimilation,experiments involving assimilating radar data are conducted using the ensemble Kalman filter(EnKF).Compared with the Gaspari-Cohn(GC) experiment,the influence of the localization methods on the assimilation effect is investigated.Results show that the analysis reflectivity coverage of all the experiments is smaller than the true reflectivity.The analysis reflectivity of the HEF experiment is bigger than that of the GC experiment in both the horizontal and vertical directions.The analysis error of most model variables decreases with time and becomes lower after analysis.This indicates that radar data assimilation can help to improve the quality of the forecast field.The RMSE of the HEF experiment is the smallest and the analysis error of the SEC experiment is smaller than the GC experiment.Compared with the GC experiment,the analysis error of the U,V and W of the HEF experiment decreases more sharply than the microphysical variables,including QR(cloud-water mixing ratio),QC(rainwater mixing ratio),QI(ice mixing ratio),QS(snow mixing ratio) and QG(graupel mixing ratio).For U,V and W,the analysis error decreases by 25% and,for microphysical variables,it decreases by 17%.The spread of the HEF experiment is largest and the spread of the SEC experiment is larger than that of the GC experiment.Compared with the GC experiment,the spread of the U,V and W of the HEF experiment increases by 45%,while that of the microphysical variables increases by 42%.In the convective region,the temperature is colder than the environment,which is called the cold pool.This is caused by the evaporation of the rainwater in the convective system.The strength and coverage of the cold pool of the GC experiment are stronger than the true field,while the HEF and SEC experiments are weaker and their areas are smaller.From 60 km to 120 km in the south-north direction,and from 0 km to 12 km in the vertical direction,the areas of vertical wind and Graupel mixing ratio are bigger,while their values are larger.So,they are closer to the wind and Graupel mixing ratio of the true field,respectively.Through simulation of the analysis fields,it is found that the northern branch of the convective system of the HEF experiment is stronger than that of the SEC and GC experiments,especially at 80 km in the south-north direction.The true field and HEF forecast result can reach about 50 dBz,which corresponds well with the assimilation results.The southern branch of the convective system of the SEC experiment is stronger than that of the HEF and GC experiments.The SEC experiment can simulate the new convective cell at(40 km,60 km).The cold pool of the HEF experiment is coldest,reaching as low as 299 K.Both the HEF and SEC experiments can simulate the center of the graupel mixing ratio.These results prove that the HEF and SEC localization methods can improve the performance of the EnKF based on GC localization method.The SEC localization method is inferior to the HEF method,but it can reduce the computational expense of the HEF method and its effect is better than the GC method.So,it could be a good choice when the NWP model is complicated. |
| |
| Keywords: | EnKF radar data assimilation HEF localization SEC localization GC localization |
| 本文献已被 CNKI 等数据库收录！ |
| 点击此处可从《大气科学学报》浏览原始摘要信息 |
| 点击此处可从《大气科学学报》下载免费的PDF全文 |
|
