中国科学院机构知识库网格
Chinese Academy of Sciences Institutional Repositories Grid
Comparison of integrating LAS/MODIS data into a land surface model for improved estimation of surface variables through data assimilation

文献类型:SCI/SSCI论文

作者Wang K. ; Tang R. L. ; Li Z. L.
发表日期2013
关键词large-aperture scintillometer remotely-sensed data kalman filter sensing data heat-flux temperature system latent
英文摘要In this article, land surface temperature (LST) and sensible heat flux (H) data assimilation schemes were developed separately using the ensemble Kalman filter (EnKF) and the common land model (CoLM). Surface measurements of ground temperature, H, and latent heat flux (LE) collected at the Yucheng (longitude: 116 degrees 36 E; latitude: 36 degrees 57 N) and Arou (longitude: 100 degrees 27 E; latitude: 38 degrees 02 N) experimental stations were compared with the predictions by assimilating different observation sources into the CoLM. The results showed that both LST and H data assimilation schemes could improve the estimation of ground temperature and H. The root mean square error (RMSE) compared between the predictions and in situ measurements decreased more significantly with the assimilation of values of H measured by a large aperture scintillometer (LAS). Assimilating Moderate Resolution Imaging Spectroradiometer (MODIS) LST only slightly improved the predictions of H and ground temperature. Daytime to night-time comparison results using both assimilation schemes also indicated that accurately quantifying model, prediction, and observation error would improve the efficiency of the assimilation systems. The newly developed land data assimilation schemes have proved to be a feasible and practical method to improve the predictions of heat fluxes and ground temperature from CoLM. Moreover, integrating multisource data (LAS and MODIS LST) simultaneously into the land surface model is believed to result in an efficient and robust way to improve the accuracy of model predictions from a theoretical point of view.
出处International Journal of Remote Sensing
34
9-10
3193-3207
收录类别SCI
语种英语
ISSN号0143-1161
源URL[http://ir.igsnrr.ac.cn/handle/311030/30203]  
专题地理科学与资源研究所_历年回溯文献
推荐引用方式
GB/T 7714
Wang K.,Tang R. L.,Li Z. L.. Comparison of integrating LAS/MODIS data into a land surface model for improved estimation of surface variables through data assimilation. 2013.

入库方式: OAI收割

来源:地理科学与资源研究所

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