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A Soil Moisture Data Assimilation System for Pakistan Using poden4dvar and CLM4.5

Journal of Meteorological Research 2019年第6期33卷 1182-1193页

作者： Tariq MAHMOOD Zhenghui XIE Binghao JIA Ammara HABIB Rashid MAHMOOD State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics(LASG) Institute of Atmospheric PhysicsChinese Academy of SciencesBeijing 100029China University of Chinese Academy of Sciences Beijing 100049China Pakistan Meteorological Department Islamabad 44000Pakistan Key Laboratory of Water Cycle and Related Land Surface Processes Institute of Geographic Science and Natural Resources ResearchChinese Academy of SciencesBeijing 100101China

Soil moisture is an important state variable for land–atmosphere *** is a vital land surface variable for research on hydrology,agriculture,climate,and drought *** current study,a soil moisture data assimilation framework has been developed by using the Community Land Model version 4.5(CLM4.5)and the proper orthogonal decomposition(POD)-based ensemble four-dimensional variational assimilation(poden4 dvar)*** experiments were conducted at four agricultural sites in Pakistan by assimilating in-situ soil moisture *** results showed that it was a reliable *** quantify further the feasibility of the data assimilation(DA)system,soil moisture observations from the top four soil-depths(0–5,5–10,10–20,and 20–30 cm)were *** evaluation results indicated that the DA system improved soil moisture *** addition,updating the soil moisture in the upper soil layers of CLM4.5 could improve soil moisture estimation in deeper soil layers[layer 7(L7,62.0 cm)and layer 8(L8,103.8 cm)].To further evaluate the DA system,observing system simulation experiments(OSSEs)were designed for Pakistan by assimilating daily *** idealized experiments produced statistical results that had higher correlation coefficients,reduced root mean square errors,and lower biases for assimilation,which showed that the DA system is able to produce and improve soil moisture estimation in Pakistan.

关键词： poden4dvar Community Land Model version 4.5 data assimilation soil moisture Pakistan

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A poden4dvar-based Radar Data Assimilation Scheme:Formulation and Preliminary Results from Real-data Experiments with Advanced Research WRF（ARW）

A PODEn4DVar-based Radar Data Assimilation Scheme:Formulatio...

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第八次全国动力气象学术会议

作者： Xiangjun Tian Jianhua Sun Feng Chen Yuanchun Zhang ICCES/LASG Institute of Atmospheric PhysicsChinese Academy of Sciences LACS Institute of Atmospheric PhysicsChinese Academy of Sciences Zhejiang Institute of Meteorological Sciences

＜正＞The Proper Orthogonal Decomposition（POD）-based ensemble four-dimensional variational assimilation method（referred to as poden4dvar） is a hybrid assimilation method that exploits the strengths of both theensemble Kalman filter（EnKF） and the standard four-dimensional variational assimilation method（4dvar）.Its feasibility and effectiveness has been demonstrated using ideal models through observing system simulation experiments

关键词： poden4dvar radar data assimilation WRF heavy rainfall

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一种计算高效且流依赖的自适应局地化方案及其在poden4dvar算法中的应用

一种计算高效且流依赖的自适应局地化方案及其在PODEn4DVar算法中...

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第32届中国气象学会年会

本文通过将稀疏化以及特征值分解技术引入到一种国际上最新提出自适应局地化方法（SENCORP）,从而发展了一种计算高效且流依赖的自适应局地化方案（SFAM）,进而将其应用一种先进的集合四维变分同化方法 poden4dvar之中。稀疏化与特征值... 详细信息

本文通过将稀疏化以及特征值分解技术引入到一种国际上最新提出自适应局地化方法（SENCORP）,从而发展了一种计算高效且流依赖的自适应局地化方案（SFAM）,进而将其应用一种先进的集合四维变分同化方法 poden4dvar之中。稀疏化与特征值分解技术的引入使得SFAM的计算代价与内存需要得以显著降低。基于Lorenz-96模式,将文中新发展的SFAM、已有的自适应的技术SENCORP以及静态局地化技术分别应用到poden4dvar方法中,比较新技术SFAM的有效性。结果表明:在Lorenz-96模式有误差的条件下,SFAM的同化效果较其他两种有适当的改善,且计算存储代价小。

关键词：稀疏流自适应修订局地化 poden4dvar

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A Local Implementation of the POD-Based Ensemble 4dvar with R-Localization

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Atmospheric and Oceanic Science Letters 2014年第1期7卷 11-16页

作者： TIAN Xiang-Jun International Center for Climate and Environment Sciences Institute of Atmospheric PhysicsChinese Academy of Sciences State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics Institute of Atmospheric PhysicsChinese Academy of Sciences

The purpose of this paper is to provide a robust and flexible implementation of a proper orthogonal decomposition-based ensemble four-dimensional variational assimilation method(poden4dvar) through *** R-localization,the implementation of the local poden4dvar analysis can be coded for parallelization with enhanced assimilation *** feasibility and effectiveness of the poden4dvar local implementation with R-localization are demonstrated in a two-dimensional shallow-water equation model with simulated observations(OSSEs) in comparison with the original version of the poden4dvar with B-localization and that without *** performance of the poden4dvar with localization shows a significant improvement over the scheme with no localization,particularly under the imperfect model ***,the R-localization scheme is capable of outperforming the Blocalization case to a certain ***,the assimilation experiments also demonstrate that poden4dvar with R-localization is most efficient due to its easy parallel implementation.

关键词： poden4dvar R-localization local implementation

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维普期刊数据库

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Real-data assimilation experiment with a joint data assimilation system: assimilating carbon dioxide mole fraction measurements from the Greenhouse gases Observing Satellite

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Atmospheric and Oceanic Science Letters 2016年第2期9卷 107-113页

作者： HAN Rui TIAN Xiang-Jun FU Yu CAI Zhao-Nan Institute of Atmospheric Sciences Chengdu University of Information Technology/Plateau Atmosphere and Environment Key Laboratory of Sichuan Province International Center for Climate and Environment Sciences Chinese Academy of Sciences Climate Change Research Center Chinese Academy of Sciences Key Laboratory of Middle Atmosphere and Global Environment Observation Chinese Academy of Sciences

The performance of a joint data assimilation system（Tan-Tracker）,which is based on the poden4 dvar assimilation method,in assimilating Greenhouse gases Observing SATellite（GOSAT） carbon dioxide（CO2） data,was *** 3D CO2 concentrations and CO2 surface fluxes（CFs） from2010 were simulated using a global chemistry transport model（GEOS-Chem）.TheTan-Tracker system used the simulated CO2 concentrations and fluxes as a background field and assimilated the GOSAT column average dry-air mole fraction of CO2（X（CO2）） data to optimize CO2 concentrations and CFs in the same assimilation *** simulated X（CO2）（X（CO2）Sim）） and assimilated X（CO2）（X（CO2）,TT） data retrieved at different satellite scan positions were compared with GOSAT-observed X（CO2）（X（CO2）,obs）*** average RMSE between the monthly X（CO2）,TT and X（CO2）,Obs data was significantly（30%） lower than the average RMSE between X（CO2）,Sim and X（CO2）,Obs）.Specifically,reductions in error were found for the positions of northern Africa（the Sahara）,the Indian peninsula,southern Africa,southern North America,and western *** difference between the correlation coefficients of the X（CO2）,Sim）and X（CO2）,Obs and those of the X（CO2）Π）,TT and X（CO2）,Obs was only *** general,the Tan-Tracker system performed very well after assimilating the GOSAT data.

关键词： Tan-Tracker GEOS-Chem GOSAT poden4dvar atmospheric CO2 concentration

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Real Data Assimilation Experiment of Tan-Tracker:Assimilating GOSAT CO2 Mole Fraction Measurements

Real Data Assimilation Experiment of Tan-Tracker:Assimilatin...

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第35届中国气象学会年会

This work was partially supported by the National High Technology Research and Development Program of China(Grant No. 2013 AA122002), the National Natural Science Foundation of China(41575100, 91437220), the Knowledge Innovation Program of the Chinese Academy of Sciences(Grant No. KZCX2-EW-QN207) and the Special Fund for Meteorological Scientific Research in Public Interest(GYHY201506002). The performance of a joint data assimilation system(Tan-Tracker), which is based on the poden4dvar assimilation method, in assimilating Greenhouse gases Observing SATellite(GOSAT) carbon dioxide(CO)data, was evaluated. Atmospheric 3D CO concentrations and CO surface fluxes(CFs) from 2010 were simulated using a global chemistry transport model(GEOS-Chem). The Tan-Tracker system used the simulated CO concentrations and fluxes as a background field and assimilated the GOSAT column average dry-air mole fraction of CO( X) data to optimize CO concentrations and CFs in the same assimilation window. Monthly simulated X( X) and assimilated X,(X) data retrieved at different satellite scan positions were compared with GOSAT-observed X(X) data. The average RMSE between the monthly X and X data was significantly(30%) lower than the average RMSE between X and X. Specifically, reductions in error were found for the positions of northern Africa(the Sahara), the Indian peninsula, southern Africa,southern North America, and western Australia. The difference between the correlation coefficients of the X and X and those of the X and X was only small. In general, the Tan-Tracker system performed very well after assimilating the GOSAT data.

关键词： Tan-Tracker GEOS-Chem GOSAT poden4dvar Atmospheric CO2 concentration

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