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Proceedings Paper

Construction of land data assimilation system based on EnKF technology and community land model
Author(s): Qifeng Lu; Wei Gao; Zhiqiang Gao; Wanli Wu; Chaohua Dong; Zhongdong Yang; Peng Zhang; Bingyu Du; James Slusser
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Paper Abstract

For the land products retrieved from the remotely sensed datasets better using in the land surface model and weather/climate model, Land Data Assimilation Systems (LDAS) based on EnKF Technology and Community Land Model, has been developed at NSMC/CMA. In the context of numerical weather prediction applications, LDAS can provide optimal estimates of land surface state initial conditions by integrating with an ensemble of land surface models, the available atmospheric forcing data, remotely sensed observations of precipitation, radiation and some land surface parameters such as land cover and leaf area index. The validation from Yucheng comprehensive experiment site indicates that the preliminary results obtained are still inspiring. There are still many detailed work to do for the routine operation of LDAS, such as how to get dynamic P in 3dvar, how to select the spacing interpolation algorithm, etc.

Paper Details

Date Published: 22 October 2007
PDF: 7 pages
Proc. SPIE 6679, Remote Sensing and Modeling of Ecosystems for Sustainability IV, 667912 (22 October 2007); doi: 10.1117/12.727868
Show Author Affiliations
Qifeng Lu, China Meteorological Administration (China)
Wei Gao, Colorado State Univ. (United States)
International Ctr. for Desert Affairs (China)
Zhiqiang Gao, Colorado State Univ. (United States)
Institute of Geographic Sciences and Natural Resources Research (China)
International Ctr. for Desert Affairs (China)
Wanli Wu, National Ctr. for Atmospheric Research (United States)
Chaohua Dong, China Meteorological Administration (China)
Zhongdong Yang, China Meteorological Administration (China)
Peng Zhang, China Meteorological Administration (China)
Bingyu Du, Nanjing Univ. of Information Science and Technology (China)
James Slusser, Colorado State Univ. (United States)


Published in SPIE Proceedings Vol. 6679:
Remote Sensing and Modeling of Ecosystems for Sustainability IV
Wei Gao; Susan L. Ustin, Editor(s)

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