干旱气候变化与可持续发展

数据同化在气候模拟中的应用——对中国西部区域气候研究的展望

  • 蒲朝霞 ,
展开
  • 1. 美国犹他大学气象系,美国 犹他;2. 甘肃省干旱气候变化与健在重点实验室,甘肃 兰州 730020
蒲朝霞(1968-),女,博士,美国犹他大学气象系教授,主要从事数值模拟和气象资料的四维同化方面的研究.E-mail:Zhaoxia.Pu@utah.edu

收稿日期: 2007-08-20

  修回日期: 2007-10-20

  网络出版日期: 2007-11-10

Applications of Data Assimilation in Climate Modeling:A Perspective from Regional Climate Studies Over Western China

Expand
  • 1.Department of Meteorology, University of Utah, USA; 2.Key Open Laboratory of Arid Climate Change and Disaster Reduction of Gansu Province, Lanzhou 730000,China

Received date: 2007-08-20

  Revised date: 2007-10-20

  Online published: 2007-11-10

摘要

现代数值模拟技术是一种把数值模型与观测资料结合起来对地球系统状态进行理想化评估的方法。除了在数值天气预报和气候分析中发挥重要作用外,数据同化技术也被应用于气候研究的许多方面,如模式初始化、确认及最优化。主要通过几个与中国西部区域气候研究紧密相关的议题,讨论了数据同化在气候模拟中的应用。并且阐述了其将面对的挑战、潜在方法学、最新研究成果和未来发展。

本文引用格式

蒲朝霞 , . 数据同化在气候模拟中的应用——对中国西部区域气候研究的展望[J]. 地球科学进展, 2007 , 22(11) : 1177 -1184 . DOI: 10.11867/j.issn.1001-8166.2007.11.1177

Abstract

Modern  data  assimilation  techniques  represent  a  way  to  combine  the  numerical  model and observations together for an optimal estimation of the state of the earth system.  In addition to their vital  role  in  numerical weather prediction and climate  reanalysis,  data  assimilation techniques can  also  be  applied  in  many  aspects of  climate  study,  such  as model  initialization, validation and optimization.  The  paper  gives  a  brief  discussion on the  applications  of  data assimilation in climate modeling with emphases on a few key issues that are closely associated with  the  regional  climate  study  over  the  western  China.  Challenges,  potential  methodologies, recent results and future development are presented.

参考文献

[1]Kalnay E, Atmospheric Modeling, Data Assimilation and Predictability [M]. Cambridge: Cambridge University Press, 2003.
[2]Talagrand O. Assimilation of observations, an introduction[J].Journal of Meteorological Society of Japan,75:191-209.
[3]Daley R.Linear non-divergent mass-wind laws on the sphere[J].Tellus, 1983, 35A:17-27.
[4]Evensen G. Data assimilation, the ensemble Kalman filter [M].  USA New york: Springer Verlag, 2006.
[5]Kopken C, Kelly G , Thepaut J N. Assimilation of meteosat radiances data within 4DVAR system at ECMWF: Assimilation experiences and forecast impact[J].Quartery Journal of Royal Meteorological Society,2004,130:2 277-2 292.
[6]LaMashall J, Uccellini L, Einaudi F, et al. The joint center for satellite data assimilation[J].Bulletin of the American Meteorological Society,2007,88(3):329-340
[7]Reichler T,  Roads J O. Time-space distribution of long-range atmospheric predictability[J].Journal of Atmospheric Science,2004, 61:249-263.
[8]Kistler R, Kalnay E, Collins W, et al. The NCEP-NCAR 50-year reanalysis: Monthly means CD-ROM and documentation[J].Bulletin of the American Meteorological Society,2001,82:247-267.
[9]Uppala S, et al.The ERA-40 reanalysis[J].Quarterly Journal of the Royal Meteorological Society,2005,131:2 961-3 012. 
[10]Mesinger F, DiMego G, Kalnay E. North American regional reanalysis[J]. Bulletin of the American Meteorological Society,2006, 87(3):343-360.
[11]Parrish D F, Derber J D. The National meteorological center spectral statistical interpolation analysis system[J].Monthly Weather Review,120:1 747-1 763.
[12]Rabier F, McNally A, Anderson E,et al.The ECMWF implementation of three- dimensional variational data assimilation (3D-Var).II: Structure function[J].Quarterly Journal of the Royal Meteorological Society,1998,124:1 809-1 830.
[13]Bailey D A, Lynch A H. Development of an Antarctic Regional Climate System Model. Part II: Station Validation and Surface Energy Balance[J]. Journal of Climate, 2000,13(8):1 351-1 361.
[14]King J C, Connolley W M. Validation of the Surface Energy Balance over the Antarctic Ice Sheets in the U.K. Meteorological Office Unified Climate Model[J].Journal of Climate,1997,10(6):1 273-1 287.
[15]Pu Z, Xu L, Salomonson V.  MODIS/Terra observed seasonal variations of snow cover over the Tibet Plateau[J].Geophysical Research Letter,34:101 029-101 034.
[16]Reichle H R, Koster R D, Dong J. Global soil moisture from satellite observations, land surface models, and ground data: Implications for data assimilation[J].Journal of Hydrometeorology,2004, 5(3):430-442.
[17]Ji M, Leetmaa A. Impact of data assimilation on ocean initialization and El Nino prediction[J]. Monthly Weather Review, 1997,125(5):742-753.

文章导航

/