The Efficiency of Data Assimilation
The Efficiency of Data Assimilation
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DOI：
10.1029/2017wr020991
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发表时间：
2018-09
影响因子：
5.4
通讯作者：
G. Nearing;S. Yatheendradas;W. Crow;X. Zhan;Jicheng Liu;Fan Chen
中科院分区：
文献类型：
--
作者：
G. Nearing;S. Yatheendradas;W. Crow;X. Zhan;Jicheng Liu;Fan Chen
数据同化是贝叶斯定理的应用，以观测值为动力系统模型的状态。贝叶斯定理的任何真实的世界应用都是近似的，因此，我们不能期望数据同化将保留来自模型和观测的所有可用信息。我们概述了一个框架，用于测量模型，观测和评估数据中的信息，使我们能够量化信息损失的方式（必然不完美）数据同化。这有助于定量分析改进（通常是昂贵的）遥感观测系统与改进数据同化设计和实施之间的权衡。我们证明了这种方法在以前发表的应用集成卡尔曼滤波器同化遥感土壤水分反演地球先进微波散射辐射计（AMSR-E）到诺亚陆面模型。
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 trade‐offs between improving (usually expensive) remote sensing observing systems versus 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 Advanced Microwave Scattering Radiometer for Earth (AMSR‐E) into the Noah land surface model.