大气科学2018，Vol.42Issue(1)：164-177,14.DOI:10.3878/j.issn.1006-9895.1704.16281
基于卫星资料梯度信息的新型变分同化方法对于台风数值模拟的研究
A New Variational Assimilation Method for Numerical Typhoon Simulation Based on Gradient Information of Satellite Data
摘要
Abstract
Satellite data has been widely used as a non-conventional meteorological data in numerical weather prediction due to its global coverage and continuous,high-frequency observations.However,there are a variety of observation errors in satellite observations,including the random errors caused by the contingency of observations and systematic errors brought by the instrument itself and introduced by the Community Radiative Transfer Model,which influence the quality of satellite data to a large extent.A method of gradient information assimilation to correct the systematic errors of satellite data is proposed in this paper,which uses a gradient operator to make gradient transformation between the model variables and observation variables and realize the objective to eliminate the systematic errors.A numerical simulation of typhoon Kompasu is then conducted using the WRF (Weather Research Forecast) and WRFDA (WRF Data Assimilation system) model as well as the AIRS (Atmospheric Infrared Sounder) data.The results indicate that the method of gradient information assimilation can greatly improve the simulation of Kompasu's track,and the method can also be applied to process data of low reliability.In addition,through the assimilation diagnosis analysis,it is found that the systematic deviation of the satellite data has a great impact on numerical simulation of typhoons,and the gradient information assimilation method proposed in this paper can solve this kind of problem.关键词
梯度信息同化/系统性偏差/WRFDA (WRF Data Assimilation system)模式Key words
Gradient information assimilation/Systematic error/WRFDA (WRF Data Assimilation system) model分类
天文与地球科学引用本文复制引用
钟波,王云峰,马刚,马新园..基于卫星资料梯度信息的新型变分同化方法对于台风数值模拟的研究[J].大气科学,2018,42(1):164-177,14.基金项目
国家自然科学基金项目41375106、11271195、41230421 National Natural Science Foundation of China (Grants 41375106,11271195,41230421) （Grants 41375106,11271195,41230421）
