Characterization,controlling, and reduction of uncertainties in the modeling and observation of land-surface systems

作     者：LI Xin 

作者机构：Cold and Arid Regions Environmental and Engineering Research Institute Chinese Academy of Sciences 

出 版 物：《Science China Earth Sciences》 (中国科学（地球科学英文版）)

年 卷 期：2014年第57卷第11期

页      面：80-87页

核心收录：

学科分类：081603[工学-地图制图学与地理信息工程] 08[工学] 0708[理学-地球物理学] 0816[工学-测绘科学与技术] 0704[理学-天文学] 

基　　金：supported by the National Natural Science Fundation of China for Distinguished Young Scientists(Grant No.40925004) the Chinese Academy of Sciences Action Plan for West Development Program Project(Grant No.KZCX2-XB3-15) the National Natural Science Foundation of China(Grant No.91125001) 

主　　题：uncertainty data assimilation scale observability predictability model remote sensing 

摘      要：Uncertainty is one of the greatest challenges in the quantitative understanding of land-surface *** paper discusses the sources of uncertainty in land-surface systems and the possible means to reduce and control this *** the perspective of model simulation,the primary source of uncertainty is the high heterogeneity of parameters,state variables,and near-surface atmospheric *** the perspective of observation,we first utilize the concept of representativeness error to unify the errors caused by scale *** representativeness error also originates mainly from spatial *** the aim of controlling and reducing uncertainties,here we demonstrate the significance of integrating modeling and observations as they are complementary and propose to treat complex land-surface systems with a stochastic *** addition,through the description of two modern methods of data assimilation,we delineate how data assimilation characterizes and controls uncertainties by maximally integrating modeling and observational information,thereby enhancing the predictability and observability of the *** suggest that the next-generation modeling should depict the statistical distribution of dynamic systems and that the observations should capture spatial heterogeneity and quantify the representativeness error of observations.