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

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 2 ) data, was evaluated. Atmospheric 3D CO 2 concentrations and CO 2 surface fluxes (CFs) from 2010...

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Published inAtmospheric and oceanic science letters = Daqi-he-haiyang-kexue-kuaibao Vol. 9; no. 2; pp. 107 - 113
Main Authors Han, Rui, Tian, Xiang-Jun, Fu, Yu, Cai, Zhao-Nan
Format Journal Article
LanguageEnglish
Published Beijing Taylor & Francis 03.03.2016
KeAi Publishing Communications Ltd
Subjects
atmospheric CO
Carbon dioxide
Coefficients
concentration
Data
Data assimilation
Data collection
Gases
GEOS-Chem
GOSAT
Greenhouse effect
Greenhouse gases
PODEn4DVar
Satellites
Tan-Tracker
二氧化碳
同化试验
大气CO2浓度
摩尔分数
数据同化
温室气体
观测卫星
跟踪系统
Africa
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Summary: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 2 ) data, was evaluated. Atmospheric 3D CO 2 concentrations and CO 2 surface fluxes (CFs) from 2010 were simulated using a global chemistry transport model (GEOS-Chem). The Tan-Tracker system used the simulated CO 2 concentrations and fluxes as a background field and assimilated the GOSAT column average dry-air mole fraction of CO 2 ( ) data to optimize CO 2 concentrations and CFs in the same assimilation window. Monthly simulated ( ) and assimilated ( ) data retrieved at different satellite scan positions were compared with GOSAT-observed ( ) data. The average RMSE between the monthly and data was significantly (30%) lower than the average RMSE between and . 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 and and those of the and was only small. In general, the Tan-Tracker system performed very well after assimilating the GOSAT data.
Bibliography: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 evaluated.Atmospheric 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 window.Monthly 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）data.The 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 Australia.The 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 small.In general,the Tan-Tracker system performed very well after assimilating the GOSAT data.
11-5693/P
Tan-Tracker; GEOS-Chem; GOSAT; PODEn4DVar; atmospheric CO2 concentration
ISSN:1674-2834
2376-6123
DOI:10.1080/16742834.2016.1133070