基于多观测算子的双通微波陆面数据同化研究

项目名称： 基于多观测算子的双通微波陆面数据同化研究

项目编号： No.41305066

项目类型： 青年科学基金项目

立项/批准年度： 2014

项目学科： 天文学、地球科学

项目作者： 贾炳浩

作者单位： 中国科学院大气物理研究所

项目金额： 25万元

中文摘要： 基于土壤湿度等陆表变量模拟亮温的微波辐射传输模型，作为观测算子在陆面数据同化系统中起着重要作用。本项目拟在已有研究基础上，以陆面过程模式CLM4为预报算子，基于贝叶斯模型平均方法融合多种辐射传输模型，建立基于多种观测算子且能同时优化模型参数和状态变量的双通微波陆面数据同化系统以减少观测算子和模型参数引起的不确定性。利用AMSR-E微波亮温和基于观测的大气强迫场驱动拟发展的同化系统，以中国区域为研究对象进行敏感性试验，探讨在不同气候和陆表下垫面条件下如何选取模型参数和贝叶斯权重等，然后通过与地面观测的土壤湿度等资料对比分析，验证该同化系统的效果，给出相应的误差分析，并生成一套精度较高的高时空分辨率土壤湿度数据集。该项研究可为干旱监测、数值天气预报、短期气候预测等提供更精确的土壤湿度数据集，对降低社会和经济损失具有重要意义和价值。

中文关键词： 土壤湿度；陆面数据同化；辐射传输模型；微波遥感；贝叶斯模型平均

英文摘要： The microwave radiative transfer model, which is taken as an observation operator to estimate the brightness temperatures from the land surface variables (e.g., soil moisture), plays an important role in the land data assimilation system. The aim of this project is to develop a dual-pass microwave land data assimilation system with multi-observation operators (LDAS-MO) based on our previous work, which uses the land surface model Community Land Model version 4 (CLM4) as the forecast operator. The LDAS-MO will use Bayesian Model Averaging (BMA) approach to incorporate multiple radiative transfer models to reduce the uncertainties of the observation operators；and a dual-pass land data assimilation framework, which simultaneously optimizes the state variables and model parameters using an optimization method and an assimilation algorithm, will also be incorporated to reduce the uncertainties induced by the model parameters. Numerical assimilation experiments based on AMSR-E microwave brightness temperature data will be designed to investigate the sensitivities of the calibration of model parameters and BMA weights under different climate and land surface conditions. In situ soil moisture measurements will be used to evaluate the performance of the LDAS-MO and its corresponding statistical analysis results will also

英文关键词： soil moisture；land data assimilation；radiative transfer model；microwave remote sensing；Bayesian model averaging