Construction and Experiment of Hierarchical Bayesian Network in Data Assimilation

35th International Symposium on Remote Sensing of Environment

地球科学;生态环境科学

Gudu, B.R.^1,3 ; Qin, S.X.^2,3 ; Ma, J.W.^2

Institute of Remote Sensing Application, Chinese Academy of Sciences, Beijing, China^1

Center for Earth Observation and Digital Earth, Chinese Academy of Sciences, Beijing, China^2

University of Chinese Academy of Sciences, Beijing, China^3

关键词: Conditional probabilities;    Data assimilation;    Ground observations;    Hierarchical Bayesian networks;    Hydrological modeling;    Nonstationary process;    Spatial and temporal distribution;    Variable infiltration capacity models;   

Others  :  https://iopscience.iop.org/article/10.1088/1755-1315/17/1/012129/pdf
DOI  :  10.1088/1755-1315/17/1/012129

学科分类：环境科学（综合）

来源: IOP

【 摘 要 】

A Hierarchical Bayesian Network Algorithm (HBN) is developed for data assimilation and tested with an instance of soil moisture assimilation from hydrological model and ground observations. In this work, data assimilation separates into data level, process level and parameter level, and conditional probability models are defined for each level. The data model mainly deals with the scale differences between multiple data, while the process model is designed to take account of non-stationary process. Soil moisture from Soil Moisture Experiment in 2003 and Variable Infiltration Capacity Model is sequentially assimilated with HBN. The result shows that the assimilation with HBN provides spatial and temporal distribution information of soil moisture and the assimilation result agrees well with the ground observations.