四维变分方法反演低层风场能力研究  

The Capability Research on Retrieving Low-Level Wind field with 4D-VAR Assimilation Technique

作　　者：牟容[1] 刘黎平[1] 许小永[1] 庄薇[1] 

机构地区：[1]中国气象科学研究院灾害天气国家重点试验室,北京100081

出　　处：《气象》2007年第1期11-18,I0001,共9页Meteorological Monthly

基　　金：国家自然科学基金项目(40375008);敏视达公司项目"低层风场反演系统和边界层辐合线风切变识别系统研制";国家973项目(2004CB418305)资助

摘　　要：从新一代天气雷达径向速度资料中反演出可靠的三维风场对提高新一代天气雷达的应用水平有重要的作用，将雷达直接观测的径向速度转换成台站预报员更为熟悉的风场结构，对识别中小尺度信息有很大帮助。为此该文对4DVAR同化技术在风场业务反演中应用的可能性进行了探讨，利用广州、济南新一代多普勒天气雷达观测资料，从是否加入云模式湿过程以及迭代次数、计算时间、背景场、初始场、分辨率和反演区域等方面对干模式的4DVAR系统进行了讨论，并从风场结构、均方根差别等方面对反演结果进行分析。多种试验表明，干模式的4DVAR系统与湿的云模式反演结果差异不大。模式的初始场和背景场对反演结果具有较高的敏感性，利用前一时次的反演结果作为背景场迭代15～20次的干模式结果可以很好地在业务上试运行，对台站预报员提高中小尺度天气预报的准确率有着很重要的作用。It is very effective to improve new generation weather radar application that reliable 3D wind fields are retrieved from real time radar radial velocities. The retrieved wind field can help forecaster to identify the mesoscale structures. The potential usage of 4DVAR assimulation technique with pure dynamical process to retrieve wind field in real time is examined by using the Doppler radar data in Guangzhou and Jinan. It is argued whether the wet process needs to be input into cloud model, and what should iteration number, retrieval area, background and initial fields be, etc. In addition, the retrieval results are analyzed from different aspects including wind field structure, computer time, mean square deviation etc. Tests show that there is little difference between retrieved results from the dry and wet 4D- VAR systems. Given a background field, the basic characteristics in low-level wind fields can be presented from dry model by 15-20 iterations, and model results are high sensitive to initial and background fields. Under the condition of background field, dry results iterated 15-20 times can be effectively operated, which are beneficial to improve the accuracy of meso- , micro-scale weather system forecast.

关 键 词：多普勒雷达 4DVAR 低层风场 准业务试验 

分 类 号：P426.62[天文地球—大气科学及气象学]