资料同化在空气质量预报中的应用  

Applications of Data Assimilation in Air Quality Prediction

作　　者：白晓平[1] 李红[1] 方栋[1] 胡非[2] Francesca Costabile 王芬娟[1] 

机构地区：[1]清华大学核能与新能源技术研究院,北京100084 [2]中国科学院大气物理研究所大气边界层物理和大气化学国家重点实验室,北京100029 [3]Institute for Atmospheric Pollution Italian National Research Council,Rome Italy

出　　处：《地球科学进展》2007年第1期66-73,共8页Advances in Earth Science

摘　　要：随着数值模式的不断完善和观测技术的不断提高,资料同化逐渐成为能够进一步提高数值预报水平的一种有效方法。20世纪70年代,资料同化开始引入空气质量预报领域,成为当前大气环境科学研究的一个新方向。简要介绍了资料同化的含义,较详细地介绍了Kalman滤波法、四维变分同化法、牛顿松弛法的基本思想和优缺点,重点阐述了国内外资料同化在空气质量预报中的研究应用情况,最后指出资料同化应用于空气质量预报时存在的问题和今后的研究方向。Along with the improvement of numerical models and the enhancement of observation technology, data assimilation has become an efficient method that can further improve numerical prediction level. Since the seventies of last century, data assimilation has been used in air quality prediction, and has become a new direction in the research on the atmospheric environmental science. The meaning of data assimilation is briefly described, and the basic principle, advantages and drawbacks of Kalman filter, four-dimensional variational assimilation and nudging methods are introduced in detail. The research progress of data assimilation in air quality prediction is mainly reviewed. The existing problems and further research directions of data assimilation applied in air quality prediction are discussed.

关 键 词：数值预报 资料同化 空气质量预报 

分 类 号：X823[环境科学与工程—环境工程]