Soil moisture is an important state variable for land–atmosphere interactions.It is a vital land surface variable for research on hydrology,agriculture,climate,and drought monitoring.In current study,a soil moisture ...Soil moisture is an important state variable for land–atmosphere interactions.It is a vital land surface variable for research on hydrology,agriculture,climate,and drought monitoring.In 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)algorithm.Assimilation experiments were conducted at four agricultural sites in Pakistan by assimilating in-situ soil moisture observations.The results showed that it was a reliable system.To 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 assimilated.The evaluation results indicated that the DA system improved soil moisture estimation.In 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 observations.These 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.展开更多
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 eva...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.展开更多
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 Rlocalization.With ...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 Rlocalization.With R-localization,the implementation of the local PODEn4DVar analysis can be coded for parallelization with enhanced assimilation precision.The 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 localization.The performance of the PODEn4DVar with localization shows a significant improvement over the scheme with no localization,particularly under the imperfect model scenario.Moreover,the R-localization scheme is capable of outperforming the Blocalization case to a certain extent.Further,the assimilation experiments also demonstrate that PODEn4DVar with R-localization is most efficient due to its easy parallel implementation.展开更多
基金Supported by the National Key Basic Research and Development Program of China(2018YFC1506602)National Natural Science Foundation of China(41830967)Key Research Program of Frontier Sciences,Chinese Academy of Sciences(QYZDY-SSWDQC012).
文摘Soil moisture is an important state variable for land–atmosphere interactions.It is a vital land surface variable for research on hydrology,agriculture,climate,and drought monitoring.In 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)algorithm.Assimilation experiments were conducted at four agricultural sites in Pakistan by assimilating in-situ soil moisture observations.The results showed that it was a reliable system.To 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 assimilated.The evaluation results indicated that the DA system improved soil moisture estimation.In 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 observations.These 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.
基金partially supported by the National High Technology Research and Development Program of China[grant number 2013AA122002]the National Natural Science Foundation of China[grant numbers 41575100 and 91437220]+1 种基金the Knowledge Innovation Program of the Chinese Academy of Sciences[grant number KZCX2-EW-QN207]the Special Fund for Meteorological Scientific Research in Public Interest[grant number GYHY201506002]
文摘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.
基金supported by the National Natural Science Foundation of China (Grant No.41075076)the National High Technology Research and Development Program of China (Grant No.2013AA122002)+1 种基金the Knowledge Innovation Program of the Chinese Academy of Sciences (Grant No.KZCX2- EW-QN207)and the National Basic Research Program of China (Grant Nos.2010CB428403 and 2009CB421407)
文摘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 Rlocalization.With R-localization,the implementation of the local PODEn4DVar analysis can be coded for parallelization with enhanced assimilation precision.The 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 localization.The performance of the PODEn4DVar with localization shows a significant improvement over the scheme with no localization,particularly under the imperfect model scenario.Moreover,the R-localization scheme is capable of outperforming the Blocalization case to a certain extent.Further,the assimilation experiments also demonstrate that PODEn4DVar with R-localization is most efficient due to its easy parallel implementation.
