摘要
本文采用基于WRFDA的集合-变分混合同化系统(En3DVAR)在云尺度分辨率下同化了雷达观测资料考察其对登陆台风"桑美"的影响。高时空分辨率的雷达径向风资料在台风登陆前的3h同化窗内以每30min的频率同化进WRF模式(Weather Research and Forecasting)。研究结果表明:En3DVAR试验在3h同化窗内的均方根误差相比3DVAR试验改进显著,这可能得益于混合同化系统中提供的"流依赖"的集合协方差信息。系统性的诊断分析表明En3DVAR试验在台风内核区产生了较为明显正温度增量,对台风内核区的热力和动力结构均有较好调整,而3DVAR则在台风内核区产生了负温度增量;相比3DAVR试验,En3DVAR在采用了"流依赖"的集合协方差信息后还可以对背景场上的台风的位置进行系统性的偏差订正。总体而言,En3DVAR试验预报的台风路径和强度相比3DVAR改进显著,其正效果主要来源于混合背景误差协方差中的"流依赖"集合协方差信息。
The impacts of assimilation of radar radial velocity data（Vr）using ensemble-variational（En3 DVAR）data assimilation system based on the Weather Research and Forecasting model（WRF）data assimilation system（WRFDA）for the application of analyses and forecasts for Typhoon Saomai（2006）are investigated.The Vr data at 30-min intervals are assimilated into the WRF model at a cloud-resolving scale using the three-dimensional variational data assimilation（3 DVAR）and En3 DVAR respectively,over a 3 hour before its landfall.The root-meansquare errors of the Vr data by the En3 DVAR were smaller than those by the 3 DVAR for Typhoon Saomai.Experiments showed that such improvements were due to the use of the flow-dependent ensemble covariance provided by En3 DVAR system.Positive temperature increments are found in Hybrid-En3 DVAR experiments,indicating a warming of the inner core with a more realistic thermal structure throughout the depth of the hurricane.In contrast,3 DVAR experiment produces much weaker and smoother increments with negative values at the vortex center at lower levels.In additional,it was found that the En3 DVAR,using the flow-dependent covariance that gave the hurricane-specific error covariance estimates,was able to systematically adjust the position of the hurricane during the assimilation whereas the 3 DVAR was not.Overall,the analysis and forecasts of the En3 DVAR scheme are superior to the 3 DVAR scheme assimilating the same Vr Observations.
作者
沈菲菲
许冬梅
闵锦忠
张冰
李超
Shen Feifei;Xu Dongmei;Min Jinzhong;Zhang Bing;Li Chao(Key Laboratory of Meteorological Disaster, Ministry of Education （ KLME ） / Joint International Research Laboratory of Climate and Environment Change （ILCEC）/Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters （ CIC-FEMD）, Nanjing University of Information Science ＆ Technology , Nanjing 210044, China;Jiangsu Research Institute of Meteorological Science, Nanjing 210009, China;Nantong Meteorological Bureau, Jiangsu Province, Nantong 226018, China)
出处
《海洋学报》
CAS
CSCD
北大核心
2018年第5期48-61,共14页
基金
江苏省气象局北极阁基金项目(BJG201510
BJG201604)
江苏省自然科学基金项目(BK20170940
BK20160954)
国家重点研发计划(2017YFC1502102
2017YFC1502103)
南京信息工程大学人才启动基金项目(2016r043
2016r27)
