首页 | 本学科首页   官方微博 | 高级检索  
     检索      

ANALYSIS OF THE EFFECT OF 3DVAR AND ENSRF DIRECT ASSIMILATION OF RADAR DATA ON THE FORECAST OF A HEAVY RAINFALL EVENT
作者姓名:刘 寅  何光鑫  刘佳伟  赵 虹  燕成玉
作者单位:1. Jiangsu Meteorological Observation Center, Nanjing 210009 China; 2. Jiangsu Institute of Meteorological Sciences, Nanjing 210009 China
摘    要:The present study designs experiments on the direct assimilation of radial velocity and reflectivity data collected by an S-band Doppler weather radar (CINRAD WSR-98D) at the Hefei Station and the reanalysis data produced by the United States National Centers for Environmental Prediction using the Weather Research and Forecasting (WRF) model, the WRF model with a three-dimensional variational (3DVAR) data assimilation system and the WRF model with an ensemble square root filter (EnSRF) data assimilation system. In addition, the present study analyzes a Meiyu front heavy rainfall process that occurred in the Yangtze -Huaihe River Basin from July 4 to July 5, 2003, through numerical simulation. The results show the following. (1) The assimilation of the radar radial velocity data can increase the perturbations in the low-altitude atmosphere over the heavy rainfall region, enhance the convective activities and reduce excessive simulated precipitation. (2) The 3DVAR assimilation method significantly adjusts the horizontal wind field. The assimilation of the reflectivity data improves the microphysical quantities and dynamic fields in the model. In addition, the assimilation of the radial velocity and reflectivity data can better adjust the wind fields and improve the intensity and location of the simulated radar echo bands. (3) The EnSRF assimilation method can assimilate more small-scale wind field information into the model. The assimilation of the reflectivity data alone can relatively accurately forecast the rainfall centers. In addition, the assimilation of the radial velocity and reflectivity data can improve the location of the simulated radar echo bands. (4) The use of the 3DVAR and EnSRF assimilation methods to assimilate the radar radial velocity and reflectivity data can improve the forecast of precipitation, rain-band areal coverage and the center location and intensity of precipitation.

关 键 词:assimilation    radar  data    heavy  rainfall  forecast    numerical  simulation
修稿时间:2016/6/29 0:00:00

ANALYSIS OF THE EFFECT OF 3DVAR AND ENSRF DIRECT ASSIMILATION OF RADAR DATA ON THE FORECAST OF A HEAVY RAINFALL EVENT
LIU Yin,HE Guang-xin,LIU Jia-wei,ZHAO Hong and YAN Cheng-yu.ANALYSIS OF THE EFFECT OF 3DVAR AND ENSRF DIRECT ASSIMILATION OF RADAR DATA ON THE FORECAST OF A HEAVY RAINFALL EVENT[J].Journal of Tropical Meteorology,2016,22(3):413-425.
Authors:LIU Yin  HE Guang-xin  LIU Jia-wei  ZHAO Hong and YAN Cheng-yu
Institution:1. Jiangsu Meteorological Observation Center, Nanjing 210009 China; 2. Jiangsu Institute of Meteorological Sciences, Nanjing 210009 China
Abstract:The present study designs experiments on the direct assimilation of radial velocity and reflectivity data collected by an S-band Doppler weather radar (CINRAD WSR-98D) at the Hefei Station and the reanalysis data produced by the United States National Centers for Environmental Prediction using the Weather Research and Forecasting (WRF) model, the WRF model with a three-dimensional variational (3DVAR) data assimilation system and the WRF model with an ensemble square root filter (EnSRF) data assimilation system. In addition, the present study analyzes a Meiyu front heavy rainfall process that occurred in the Yangtze -Huaihe River Basin from July 4 to July 5, 2003, through numerical simulation. The results show the following. (1) The assimilation of the radar radial velocity data can increase the perturbations in the low-altitude atmosphere over the heavy rainfall region, enhance the convective activities and reduce excessive simulated precipitation. (2) The 3DVAR assimilation method significantly adjusts the horizontal wind field. The assimilation of the reflectivity data improves the microphysical quantities and dynamic fields in the model. In addition, the assimilation of the radial velocity and reflectivity data can better adjust the wind fields and improve the intensity and location of the simulated radar echo bands. (3) The EnSRF assimilation method can assimilate more small-scale wind field information into the model. The assimilation of the reflectivity data alone can relatively accurately forecast the rainfall centers. In addition, the assimilation of the radial velocity and reflectivity data can improve the location of the simulated radar echo bands. (4) The use of the 3DVAR and EnSRF assimilation methods to assimilate the radar radial velocity and reflectivity data can improve the forecast of precipitation, rain-band areal coverage and the center location and intensity of precipitation.
Keywords:assimilation  radar data  heavy rainfall forecast  numerical simulation
本文献已被 CNKI 等数据库收录!
点击此处可从《热带气象学报(英文版)》浏览原始摘要信息
点击此处可从《热带气象学报(英文版)》下载免费的PDF全文
设为首页 | 免责声明 | 关于勤云 | 加入收藏

Copyright©北京勤云科技发展有限公司  京ICP备09084417号