梁旭东,王斌. 2010. 基于模式约束三维变分技术的连续循环同化试验研究[J]. 气象学报, 68(2):153-161, doi:10.11676/qxxb2010.016
基于模式约束三维变分技术的连续循环同化试验研究
Data assimilation cycle experiments in typhoon numerical prediction using the model constrained 3DVar
投稿时间:2007-01-31  修订日期:2007-04-06
DOI:10.11676/qxxb2010.016
中文关键词:  变分同化,模式约束,台风
英文关键词:Variational data assimilation, Model constraint, Typhoon
基金项目:国家科技部社会公益专项(2005DIB3J104),国家科技支撑计划课题“灾害天气精细数值预报系统及短期气候集合预测研究”及中国气象局新技术推广项目
作者单位
梁旭东 中国气象局上海台风研究所中国气象局台风预报技术重点开放实验室上海200030 
王斌 LASG, 中国科学院大气物理研究所, 北京, 100029 
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中文摘要:
      由于模式约束三维变分同化技术中考虑了模式的动力和物理过程,因此能保证各物理量间的平衡关系,从而滤除由于观测资料引入导致的高频波动,减小模式与初始场的协调时间。由于能在较短时间调整到稳定状态,采用模式约束三维变分同化进行连续循环同化可用较少的计算量达到同化多时次的多种观测资料的目的。该研究利用模式约束三维变分技术, 针对2006年“桑美”台风个例,进行了连续循环同化卫星云导风、QuikSCAT海面风、Bogus海平面气压的试验。在台风数值预报中往往需要使用经验构造的台风信息(如Bogus海平面气压,Bogus风场等),该研究采用了模式约束三维变分同化技术同化Bogus海平面气压。由于模式约束三维变分同化技术充分考虑了各物理量间的约束,因此通过同化Bogus海平面气压也调整了初始场中相应的高度场、温度场、风场等变量,使得初始场中的台风涡旋具有较强的协调性,提高了对台风的模拟能力。采用AVN模式6小时间隔的分析场作为侧边界,2006年8月8日20时的分析场作为初估场,文中对 8月8日20时到9日05 时“桑美”台风的观测资料进行了连续循环同化。采用连续循环同化后台风路径的模拟精度得到了显著提高,对台风降水结构等的模拟也得到了改善。
英文摘要:
      As a typical initial and boundary-value problem, data assimilation technique plays a very important role in numerical weather prediction (NWP). In the past decades, the 3D and 4D-Var data assimilation techniques were improved quickly not only in the theoretical researches but also in the NWPs of operational centers. However, there are still some problems which limit the wide use of variational data assimilation techniques. One of them is that the general 3-dimensional variational data assimilation (3DVar) technique is short of complicated constraints such as the dynamics and physics in a numerical model, as are used in the 4-dimensional variational data assimilation (4DVar) technique. The other one is that using a numerical model and its adjoint with the 4DVar technique requires a large amount of computer resources, and thus limits its practical applicability. A new 3DVar method (mode-constrained 3DVar, MC-3DVar) is then proposed by incorporating a numerical model constraint. This method minimizes the distance between the observations and model variables and also their time tendency, so that the optimized initial conditions not only fit the observations but also satisfy the constraints of full dynamics and physics of the numerical model. In this study, an assimilation cycle is employed to improve the initial conditions using various data at different time. A case study of Typhoon Saomai (2006) from 20:00 BST 8 to 05:00 BST 9 August is carried out. The assimilated data include the cloud drift wind, QuikSCAT sea level wind, and Bogus sea level pressure. The track forecast of Saomai is improved dramatically after assimilating these data. The simulated structure of the typhoon is also improved using the new initial conditions after the assimilation cycle. These can be attributed to that using MC-3DVar method, after the Bogus sea level pressure field is assimilated, the height, wind and other variables are also adjusted by the constraints of the dynamics and physics of the numerical model. In addition to the Bogus vortex data, the other assimilated observations are also important to improve the initial conditions.
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