The Efficiency of Data Assimilation

Nearing, Grey ; Yatheendradas, Soni [Point of Contact] ; Crow, Wade ; Zhan, Xiwu ; Liu, Jicheng ; Chen, Fan

关键词: ALGORITHMS;    ASSIMILATION;    BAYES THEOREM;    DATA RETRIEVAL;    DYNAMICAL SYSTEMS;    KALMAN FILTERS;    MATHEMATICAL MODELS;    REMOTE SENSING;    SOIL MOISTURE;    QUANTITATIVE ANALYSIS;   

RP-ID  :  GSFC-E-DAA-TN58898

学科分类：地球科学(综合)

美国|英语

来源: NASA Technical Reports Server

【 摘 要 】

Data assimilation is the application of Bayes' theorem to condition the states of a dynamical systems model on observations. Any real-world application of Bayes' theorem is approximate, and therefore we cannot expect that data assimilation will preserve all of the information available from models and observations. We outline a framework for measuring information in models, observations, and evaluation data in a way that allows us to quantify information loss during (necessarily imperfect) data assimilation. This facilitates quantitative analysis of tradeoffs between improving (usually expensive) remote sensing observing systems vs. improving data assimilation design and implementation. We demonstrate this methodology on a previously published application of the Ensemble Kalman Filter used to assimilate remote sensing soil moisture retrievals from AMSR-E into the Noah land surface model.