This paper proposes a hybrid method, called CNOP–4 DVar, for the identification of sensitive areas in targeted observations, which takes the advantages of both the conditional nonlinear optimal perturbation(CNOP) and...This paper proposes a hybrid method, called CNOP–4 DVar, for the identification of sensitive areas in targeted observations, which takes the advantages of both the conditional nonlinear optimal perturbation(CNOP) and four-dimensional variational assimilation(4 DVar) methods. The proposed CNOP–4 DVar method is capable of capturing the most sensitive initial perturbation(IP), which causes the greatest perturbation growth at the time of verification;it can also identify sensitive areas by evaluating their assimilation effects for eliminating the most sensitive IP. To alleviate the dependence of the CNOP–4 DVar method on the adjoint model, which is inherited from the adjoint-based approach, we utilized two adjointfree methods, NLS-CNOP and NLS-4 DVar, to solve the CNOP and 4 DVar sub-problems, respectively. A comprehensive performance evaluation for the proposed CNOP–4 DVar method and its comparison with the CNOP and CNOP–ensemble transform Kalman filter(ETKF) methods based on 10 000 observing system simulation experiments on the shallow-water equation model are also provided. The experimental results show that the proposed CNOP–4 DVar method performs better than the CNOP–ETKF method and substantially better than the CNOP method.展开更多
We applied the multigrid nonlinear least-squares four-dimensional variational assimilation(MG-NLS4DVar)method in data assimilation and prediction experiments for Typhoon Haikui(2012)using the Weather Research and Fore...We applied the multigrid nonlinear least-squares four-dimensional variational assimilation(MG-NLS4DVar)method in data assimilation and prediction experiments for Typhoon Haikui(2012)using the Weather Research and Forecasting(WRF)model.Observation data included radial velocity(Vr)and reflectivity(Z)data from a single Doppler radar,quality controlled prior to assimilation.Typhoon prediction results were evaluated and compared between the NLS-4DVar and MG-NLS4DVar methods.Compared with a forecast that began with NCEP analysis data,our radar data assimilation results were clearly improved in terms of structure,intensity,track,and precipitation prediction for Typhoon Haikui(2012).The results showed that the assimilation accuracy of the NLS-4DVar method was similar to that of the MG-NLS4DVar method,but that the latter was more efficient.The assimilation of Vr alone and Z alone each improved predictions of typhoon intensity,track,and precipitation;however,the impacts of Vr data were significantly greater that those of Z data.Assimilation window-length sensitivity experiments showed that a 6-h assimilation window with 30-min assimilation intervals produced slightly better results than either a 3-h assimilation window with 15-min assimilation intervals or a 1-h assimilation window with 6-min assimilation intervals.展开更多
The multi-scale weather systems associated with a mei-yu front and the corresponding heavy precipitation during a particular heavy rainfall event that occurred on 4 5 July 2003 in east China were successfully simulate...The multi-scale weather systems associated with a mei-yu front and the corresponding heavy precipitation during a particular heavy rainfall event that occurred on 4 5 July 2003 in east China were successfully simulated through rainfall assimilation using the PSU/NCAR non-hydrostatic, mesoscale, numerical model （MM5） and its four-dimensional, variational, data assimilation （4DVAR） system. For this case, the improvement of the process via the 4DVAR rainfall assimilation into the simulation of mesoscale precipitation systems is investigated. With the rainfall assimilation, the convection is triggered at the right location and time, and the evolution and spatial distribution of the mesoscale convective systems （MCSs） are also more correctly simulated. Through the interactions between MCSs and the weather systems at different scales, including the low-level jet and mei-yu front, the simulation of the entire mei-yu weather system is significantly improved, both during the data assimilation window and the subsequent 12-h period. The results suggest that the rainfall assimilation first provides positive impact at the convective scale and the influences are then propagated upscale to the meso- and sub-synoptic scales. Through a set of sensitive experiments designed to evaluate the impact of different initial variables on the simulation of mei-yu heavy rainfall, it was found that the moisture field and meridional wind had the strongest effect during the convection initialization stage, however, after the convection was fully triggered, all of the variables at the initial condition seemed to have comparable importance.展开更多
In this study we extend the dimension-reduced projection-four dimensional variational data assimilation （DRP-4DVar） approach to allow the analysis time to be tunable, so that the intervals between analysis time and ...In this study we extend the dimension-reduced projection-four dimensional variational data assimilation （DRP-4DVar） approach to allow the analysis time to be tunable, so that the intervals between analysis time and observation times can be shortened. Due to the limits of the perfect-model assumption and the tangentlinear hypothesis, the analysis-time tuning is expected to have the potential to further improve analyses and forecasts. Various sensitivity experiments using the Lorenz-96 model are conducted to test the impact of analysistime tuning on the performance of the new approach under perfect and imperfect model scenarios, respectively. Comparing three DRP-4DVar schemes having the analysis time at the start, middle, and end of the assimilation window, respectively, it is found that the scheme with the analysis time in the middle of the window outperforms the others, on the whole. Moreover, the advantage of this scheme is more pronounced when a longer assimilation window is adopted or more observations are assimilated.展开更多
In four—dimensional variational data assimilation (4DVAR) technology, how to calculate the optimal step size is always a very important and indeed difficult task. It is directly related to the computational efficienc...In four—dimensional variational data assimilation (4DVAR) technology, how to calculate the optimal step size is always a very important and indeed difficult task. It is directly related to the computational efficiency. In this research, a new method is proposed to calculate the optimal step size more effectively. Both nonlinear one—dimensional advection equation and two—dimensional inertial wave equation are used to test and compare the influence of different methods of the optimal step size calculations on the iteration steps, as well as the simulation results of 4DVAR processes. It is in evidence that the different methods have different influences. The calculating method is very important to determining whether the iteration is convergent or not and whether the convergence rate is large or small. If the calculating method of optimal step size is properly determined as demonstrated in this paper, then it can greatly enlarge the convergence rate and further greatly decrease the iteration steps. This research is meaningful since it not only makes an important improvement on 4DVAR theory, but also has useful practical application in improving the computational efficiency and saving the computational time. Key words 4DVAR - Optimal step size - Iterative convergence rate This work was supported by the National Natural Science Foundation under grants: 49735180 and 49675259, the “973 Project? CHERES(G 1998040907), the Project of Natural Science Foundation of Jiangsu Province(BK99020), and the Project Sponsored by the Scientific Research Foundation for the Returned Overseas Chinese Scholars.展开更多
The nonlinear least-squares four-dimensional variational assimilation(NLS-4DVar)method intro-duced here combines the merits of the ensemble Kalman lter and 4DVar assimilation methods.The multigrid NLS-4DVar method can...The nonlinear least-squares four-dimensional variational assimilation(NLS-4DVar)method intro-duced here combines the merits of the ensemble Kalman lter and 4DVar assimilation methods.The multigrid NLS-4DVar method can be implemented without adjoint models and also corrects small-to large-scale errors with greater accuracy.In this paper,the multigrid NLS-4DVar method is used in radar radial velocity data assimilations.Observing system simulation experiments were conducted to determine the capability and efficiency of multigrid NLS-4DVar for assimilating radar radial velocity with WRF-ARW(the Advanced Research Weather Research and Forecasting model).The results show signi cant improvement in 24-h cumulative precipitation prediction due to improved initial conditions after assimilating the radar radial velocity.Additionally,the multigrid NLS-4DVar method reduces computational cost.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
The Bering Sea circulation is derived as a variational inverse of hydrographic profiles（ temperature and salinity） , atmospheric climatologies and historical observation of ocean curents. The important result of thi...The Bering Sea circulation is derived as a variational inverse of hydrographic profiles（ temperature and salinity） , atmospheric climatologies and historical observation of ocean curents. The important result of this study is estimate of the mean climatological sea surface height （SSH） that can be used as a reference for satellite altimetry sea level anomaly data in the Bering Sea region. Numerical experiments reveal that, when combined with satellite altimetry, the obtained reference SSH effectively constrains a realistic reconstruction of the Amukta Pass circulation.展开更多
CO_(2)is one of the most important greenhouse gases(GHGs)in the earth’s atmosphere.Since the industrial era,anthropogenic activities have emitted excessive quantities of GHGs into the atmosphere,resulting in climate ...CO_(2)is one of the most important greenhouse gases(GHGs)in the earth’s atmosphere.Since the industrial era,anthropogenic activities have emitted excessive quantities of GHGs into the atmosphere,resulting in climate warming since the 1950s and leading to an increased frequency of extreme weather and climate events.In 2020,China committed to striving for carbon neutrality by 2060.This commitment and China’s consequent actions will result in significant changes in global and regional anthropogenic carbon emissions and therefore require timely,comprehensive,and objective monitoring and verification support(MVS)systems.The MVS approach relies on the top-down assimilation and inversion of atmospheric CO_(2)concentrations,as recommended by the Intergovernmental Panel on Climate Change(IPCC)Inventory Guidelines in 2019.However,the regional high-resolution assimilation and inversion method is still in its initial stage of development.Here,we have constructed an inverse system for carbon sources and sinks at the kilometer level by coupling proper orthogonal decomposition(POD)with four-dimensional variational(4DVar)data assimilation based on the weather research and forecasting-greenhouse gas(WRF-GHG)model.Our China Carbon Monito ring and Verification Support at the Regional level(CCMVS-R)system can continuously assimilate information on atmospheric CO_(2)and other related information and realize the inversion of regional and local anthropogenic carbon emissions and natural terrestrial ecosystem carbon exchange.Atmospheric CO_(2)data were collected from six ground-based monito ring sites in Shanxi Province,China to verify the inversion effect of regio nal anthropogenic carbon emissions by setting ideal and real experiments using a two-layer nesting method(at 27 and 9 km).The uncertainty of the simulated atmospheric CO_(2)decreased significantly,with a root-mean-square error of CO_(2)concentration values between the ideal value and the simulated after assimilation was close to 0.The total anthropogenic carbon emissions in Shanxi Province in 2019 from the assimilated inversions were approximately 28.6%(17%-38%)higher than the mean of five emission inventories using the bottomup method,showing that the top-down CCMVS-R system can obtain more comprehensive information on anthropogenic carbon emissions.展开更多
Based on a cloud model and the four-dimensional variational (4DVAR) dataassimilation method developed by Sun and Crook (1997), simulated experiments of dynamical andmicrophysical retrieval from Doppler radar data were...Based on a cloud model and the four-dimensional variational (4DVAR) dataassimilation method developed by Sun and Crook (1997), simulated experiments of dynamical andmicrophysical retrieval from Doppler radar data were performed. The 4DVAR data assimilationtechnique was applied to a cloud scale model with a warm rain parameterization scheme. The 3D wind,thermodynamical, and microphysieal fields were determined by minimizing a cost function, defined bythe difference between both radar observed radial velocities and reflectivities and their modelpredictions. The adjoint of the numerical model was used to provide the gradient of the costfunction with respect to the control variables. Experiments have demonstrated that the 4DVARassimilation method is able to retrieve the detailed structure of wind, thermodynamics, andmicrophysics by using either dual-Doppler or single-Doppler information. The quality of retrievaldepends strongly on the magnitude of constraint with respect to the variables. Retrieving thetemperature field, cloud water and water vapor is more difficult than the recovery of the wind fieldand rainwater. Accurate thermodynamic retrieval requires a longer assimilation period. Theinclusion of a background term, even mean fields from a single sounding, helped reduce the retrievalerrors. Less accurate velocity fields were obtained when single-Doppler data were used. It wasfound that the retrieved velocity is sensitive to the location of the retrieval domain relative tothe radars while the other fields have very little changes. Two radar volumetric scans are generallyadequate for providing the evolution, although the use of additional volumes improves theretrieval. As the amount of the observations decreases, the performance of the retrieval isdegraded. However, the missing observations can be compensated by adding a background term to thecost function. The technique is robust to random errors in radial velocity and calibration errors inreflectivity. The boundary conditions from the dual-Doppler synthesized winds are sufficient forthe retrieval. When the retrieval is mainly controlled by the observations in the regions away fromthe boundaries, the simple boundary conditions from velocity azimuth display (VAD) analysis are alsoavailable. The microphysical retrieval is sensitive to model errors.展开更多
Effects of 4-dimension variational data assimi-lation (4DVAR) with multifold observed data on the typhoontrack forecast are studied, by using the MM5V3 model, theRTTOVS-5 model and their adjoint models. The data usedf...Effects of 4-dimension variational data assimi-lation (4DVAR) with multifold observed data on the typhoontrack forecast are studied, by using the MM5V3 model, theRTTOVS-5 model and their adjoint models. The data usedfor assimilation include large-scale background fields data,bogus data, cloud-derived wind data, satellite inverse data,and high resolution infrared radiation sounder data (HIRS).There are 5 typhoon cases are used to perform the numericalexperiments and assimilation experiments. The numericalresults show that with 4DVAR the initial fields can be greatlyimproved and the initial typhoon structure can be clearlydescribed.展开更多
The four-dimensional variational （4DVAR） data assimilation method was applied to dual-Doppler radar data about two Meiyu rainstorms observed during CHeRES （China Heavy Rain Experiment and Study）. The purpose of th...The four-dimensional variational （4DVAR） data assimilation method was applied to dual-Doppler radar data about two Meiyu rainstorms observed during CHeRES （China Heavy Rain Experiment and Study）. The purpose of this study is to examine the performance of the 4DVAR technique in retrieving rainstorm mesoscale structure and to reveal the feature of rainstorm mesoscale structure. Results demonstrated that the 4DVAR assimilation method was able to retrieve the detailed structure of wind, thermodynamics, and microphysics fields from dual-Doppler radar observations. The retrieved wind fields agreed with the dual- Doppler synthesized winds and were accurate. The distributions of the retrieved perturbation pressure, perturbation temperature, and microphysics fields were also reasonable through the examination of their physical consistency. Both of the two heavy rainfalls were caused by merging cloud processes. The wind shear and convergence lines at middle and lower levels were their primary dynamical characteristics. The convective system was often related to low-level convergence and upper-level divergence coupled with up- drafts. During its mature stage, the convective system was characterized by low pressure at lower level and high pressure at upper level, associated with warmer at middle level and colder at lower and upper levels than the environment. However, a region of cooling and high pressure occurred in the lower and middle levels compared to warming and low pressure in the upper level during its dissipating ＇.stage. The water vapor, cloud water, and rainwater corresponded to the convergence, the updraft and the intensive reflectivity, respectively.展开更多
基金partially supported by the National Key R&D Program of China (Grant No. 2016YFA0600203)the National Natural Science Foundation of China (Grant No. 41575100)
文摘This paper proposes a hybrid method, called CNOP–4 DVar, for the identification of sensitive areas in targeted observations, which takes the advantages of both the conditional nonlinear optimal perturbation(CNOP) and four-dimensional variational assimilation(4 DVar) methods. The proposed CNOP–4 DVar method is capable of capturing the most sensitive initial perturbation(IP), which causes the greatest perturbation growth at the time of verification;it can also identify sensitive areas by evaluating their assimilation effects for eliminating the most sensitive IP. To alleviate the dependence of the CNOP–4 DVar method on the adjoint model, which is inherited from the adjoint-based approach, we utilized two adjointfree methods, NLS-CNOP and NLS-4 DVar, to solve the CNOP and 4 DVar sub-problems, respectively. A comprehensive performance evaluation for the proposed CNOP–4 DVar method and its comparison with the CNOP and CNOP–ensemble transform Kalman filter(ETKF) methods based on 10 000 observing system simulation experiments on the shallow-water equation model are also provided. The experimental results show that the proposed CNOP–4 DVar method performs better than the CNOP–ETKF method and substantially better than the CNOP method.
基金partially supported by the National Key Research and Development Program of China(Grant No.2016YFA0600203)the National Natural Science Foundation of China(Grant No.41575100)the Key Research Program of Frontier Sciences,Chinese Academy of Sciences(Grant No.QYZDY-SSW-DQC012)。
文摘We applied the multigrid nonlinear least-squares four-dimensional variational assimilation(MG-NLS4DVar)method in data assimilation and prediction experiments for Typhoon Haikui(2012)using the Weather Research and Forecasting(WRF)model.Observation data included radial velocity(Vr)and reflectivity(Z)data from a single Doppler radar,quality controlled prior to assimilation.Typhoon prediction results were evaluated and compared between the NLS-4DVar and MG-NLS4DVar methods.Compared with a forecast that began with NCEP analysis data,our radar data assimilation results were clearly improved in terms of structure,intensity,track,and precipitation prediction for Typhoon Haikui(2012).The results showed that the assimilation accuracy of the NLS-4DVar method was similar to that of the MG-NLS4DVar method,but that the latter was more efficient.The assimilation of Vr alone and Z alone each improved predictions of typhoon intensity,track,and precipitation;however,the impacts of Vr data were significantly greater that those of Z data.Assimilation window-length sensitivity experiments showed that a 6-h assimilation window with 30-min assimilation intervals produced slightly better results than either a 3-h assimilation window with 15-min assimilation intervals or a 1-h assimilation window with 6-min assimilation intervals.
基金This research was supported by the National Natural Science Foundation of China under Grant Nos. 40325014, 40333031SRFDP, TRAP0YT, FANEDD 11999, and under the support of The Key Scientific and Technological Project of the Ministry of Education The State Key Basic Research Program （Grant No. 2004CB18300）.
文摘The multi-scale weather systems associated with a mei-yu front and the corresponding heavy precipitation during a particular heavy rainfall event that occurred on 4 5 July 2003 in east China were successfully simulated through rainfall assimilation using the PSU/NCAR non-hydrostatic, mesoscale, numerical model （MM5） and its four-dimensional, variational, data assimilation （4DVAR） system. For this case, the improvement of the process via the 4DVAR rainfall assimilation into the simulation of mesoscale precipitation systems is investigated. With the rainfall assimilation, the convection is triggered at the right location and time, and the evolution and spatial distribution of the mesoscale convective systems （MCSs） are also more correctly simulated. Through the interactions between MCSs and the weather systems at different scales, including the low-level jet and mei-yu front, the simulation of the entire mei-yu weather system is significantly improved, both during the data assimilation window and the subsequent 12-h period. The results suggest that the rainfall assimilation first provides positive impact at the convective scale and the influences are then propagated upscale to the meso- and sub-synoptic scales. Through a set of sensitive experiments designed to evaluate the impact of different initial variables on the simulation of mei-yu heavy rainfall, it was found that the moisture field and meridional wind had the strongest effect during the convection initialization stage, however, after the convection was fully triggered, all of the variables at the initial condition seemed to have comparable importance.
基金supported by the Special Project of the Meteorological Sector program Grant No GYHY(QX) 200906011the 973 project (Grant No 2004CB418304)
文摘In this study we extend the dimension-reduced projection-four dimensional variational data assimilation （DRP-4DVar） approach to allow the analysis time to be tunable, so that the intervals between analysis time and observation times can be shortened. Due to the limits of the perfect-model assumption and the tangentlinear hypothesis, the analysis-time tuning is expected to have the potential to further improve analyses and forecasts. Various sensitivity experiments using the Lorenz-96 model are conducted to test the impact of analysistime tuning on the performance of the new approach under perfect and imperfect model scenarios, respectively. Comparing three DRP-4DVar schemes having the analysis time at the start, middle, and end of the assimilation window, respectively, it is found that the scheme with the analysis time in the middle of the window outperforms the others, on the whole. Moreover, the advantage of this scheme is more pronounced when a longer assimilation window is adopted or more observations are assimilated.
文摘In four—dimensional variational data assimilation (4DVAR) technology, how to calculate the optimal step size is always a very important and indeed difficult task. It is directly related to the computational efficiency. In this research, a new method is proposed to calculate the optimal step size more effectively. Both nonlinear one—dimensional advection equation and two—dimensional inertial wave equation are used to test and compare the influence of different methods of the optimal step size calculations on the iteration steps, as well as the simulation results of 4DVAR processes. It is in evidence that the different methods have different influences. The calculating method is very important to determining whether the iteration is convergent or not and whether the convergence rate is large or small. If the calculating method of optimal step size is properly determined as demonstrated in this paper, then it can greatly enlarge the convergence rate and further greatly decrease the iteration steps. This research is meaningful since it not only makes an important improvement on 4DVAR theory, but also has useful practical application in improving the computational efficiency and saving the computational time. Key words 4DVAR - Optimal step size - Iterative convergence rate This work was supported by the National Natural Science Foundation under grants: 49735180 and 49675259, the “973 Project? CHERES(G 1998040907), the Project of Natural Science Foundation of Jiangsu Province(BK99020), and the Project Sponsored by the Scientific Research Foundation for the Returned Overseas Chinese Scholars.
基金supported by the National Key Research and Development Program of China [grant number2016YFA0600203]the National Natural Science Foundation of China [grant number 41575100]the Key Research Program of Frontier Sciences,Chinese Academy of Sciences[grant number QYZDY-SSW-DQC012]
文摘The nonlinear least-squares four-dimensional variational assimilation(NLS-4DVar)method intro-duced here combines the merits of the ensemble Kalman lter and 4DVar assimilation methods.The multigrid NLS-4DVar method can be implemented without adjoint models and also corrects small-to large-scale errors with greater accuracy.In this paper,the multigrid NLS-4DVar method is used in radar radial velocity data assimilations.Observing system simulation experiments were conducted to determine the capability and efficiency of multigrid NLS-4DVar for assimilating radar radial velocity with WRF-ARW(the Advanced Research Weather Research and Forecasting model).The results show signi cant improvement in 24-h cumulative precipitation prediction due to improved initial conditions after assimilating the radar radial velocity.Additionally,the multigrid NLS-4DVar method reduces computational cost.
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.
基金supported by North Pacific Research Board(NPRB),project No 828,contribution No 204AMSTEC,Japan,through the sponsorship of IARC+1 种基金The study was also supported by the NSF Award 0629311 and RFFI Grant 06-05-96065Nikolai Maximenko was partly supported by NASA through membership in its Ocean Surface Topography Science Team.
文摘The Bering Sea circulation is derived as a variational inverse of hydrographic profiles（ temperature and salinity） , atmospheric climatologies and historical observation of ocean curents. The important result of this study is estimate of the mean climatological sea surface height （SSH） that can be used as a reference for satellite altimetry sea level anomaly data in the Bering Sea region. Numerical experiments reveal that, when combined with satellite altimetry, the obtained reference SSH effectively constrains a realistic reconstruction of the Amukta Pass circulation.
基金supported by the General Project of Top-Design of Multi-Scale Nature-Social ModelsData Support and Decision Support System for NSFC Carbon Neutrality Major Project(42341202)the Basic Scientific Research Fund of the Chinese Academy of Meteorological Sciences(2021Z014)。
文摘CO_(2)is one of the most important greenhouse gases(GHGs)in the earth’s atmosphere.Since the industrial era,anthropogenic activities have emitted excessive quantities of GHGs into the atmosphere,resulting in climate warming since the 1950s and leading to an increased frequency of extreme weather and climate events.In 2020,China committed to striving for carbon neutrality by 2060.This commitment and China’s consequent actions will result in significant changes in global and regional anthropogenic carbon emissions and therefore require timely,comprehensive,and objective monitoring and verification support(MVS)systems.The MVS approach relies on the top-down assimilation and inversion of atmospheric CO_(2)concentrations,as recommended by the Intergovernmental Panel on Climate Change(IPCC)Inventory Guidelines in 2019.However,the regional high-resolution assimilation and inversion method is still in its initial stage of development.Here,we have constructed an inverse system for carbon sources and sinks at the kilometer level by coupling proper orthogonal decomposition(POD)with four-dimensional variational(4DVar)data assimilation based on the weather research and forecasting-greenhouse gas(WRF-GHG)model.Our China Carbon Monito ring and Verification Support at the Regional level(CCMVS-R)system can continuously assimilate information on atmospheric CO_(2)and other related information and realize the inversion of regional and local anthropogenic carbon emissions and natural terrestrial ecosystem carbon exchange.Atmospheric CO_(2)data were collected from six ground-based monito ring sites in Shanxi Province,China to verify the inversion effect of regio nal anthropogenic carbon emissions by setting ideal and real experiments using a two-layer nesting method(at 27 and 9 km).The uncertainty of the simulated atmospheric CO_(2)decreased significantly,with a root-mean-square error of CO_(2)concentration values between the ideal value and the simulated after assimilation was close to 0.The total anthropogenic carbon emissions in Shanxi Province in 2019 from the assimilated inversions were approximately 28.6%(17%-38%)higher than the mean of five emission inventories using the bottomup method,showing that the top-down CCMVS-R system can obtain more comprehensive information on anthropogenic carbon emissions.
基金This work is supported by the National Key Program of Science and Technology of China (2001BA610A).
文摘Based on a cloud model and the four-dimensional variational (4DVAR) dataassimilation method developed by Sun and Crook (1997), simulated experiments of dynamical andmicrophysical retrieval from Doppler radar data were performed. The 4DVAR data assimilationtechnique was applied to a cloud scale model with a warm rain parameterization scheme. The 3D wind,thermodynamical, and microphysieal fields were determined by minimizing a cost function, defined bythe difference between both radar observed radial velocities and reflectivities and their modelpredictions. The adjoint of the numerical model was used to provide the gradient of the costfunction with respect to the control variables. Experiments have demonstrated that the 4DVARassimilation method is able to retrieve the detailed structure of wind, thermodynamics, andmicrophysics by using either dual-Doppler or single-Doppler information. The quality of retrievaldepends strongly on the magnitude of constraint with respect to the variables. Retrieving thetemperature field, cloud water and water vapor is more difficult than the recovery of the wind fieldand rainwater. Accurate thermodynamic retrieval requires a longer assimilation period. Theinclusion of a background term, even mean fields from a single sounding, helped reduce the retrievalerrors. Less accurate velocity fields were obtained when single-Doppler data were used. It wasfound that the retrieved velocity is sensitive to the location of the retrieval domain relative tothe radars while the other fields have very little changes. Two radar volumetric scans are generallyadequate for providing the evolution, although the use of additional volumes improves theretrieval. As the amount of the observations decreases, the performance of the retrieval isdegraded. However, the missing observations can be compensated by adding a background term to thecost function. The technique is robust to random errors in radial velocity and calibration errors inreflectivity. The boundary conditions from the dual-Doppler synthesized winds are sufficient forthe retrieval. When the retrieval is mainly controlled by the observations in the regions away fromthe boundaries, the simple boundary conditions from velocity azimuth display (VAD) analysis are alsoavailable. The microphysical retrieval is sensitive to model errors.
文摘Effects of 4-dimension variational data assimi-lation (4DVAR) with multifold observed data on the typhoontrack forecast are studied, by using the MM5V3 model, theRTTOVS-5 model and their adjoint models. The data usedfor assimilation include large-scale background fields data,bogus data, cloud-derived wind data, satellite inverse data,and high resolution infrared radiation sounder data (HIRS).There are 5 typhoon cases are used to perform the numericalexperiments and assimilation experiments. The numericalresults show that with 4DVAR the initial fields can be greatlyimproved and the initial typhoon structure can be clearlydescribed.
基金Supported by the National Key Program for Developing Basic Sciences "Research on the Formation Mechanism and Prediction Theory of Hazardous Weather over China" (2001BA610A).
文摘The four-dimensional variational （4DVAR） data assimilation method was applied to dual-Doppler radar data about two Meiyu rainstorms observed during CHeRES （China Heavy Rain Experiment and Study）. The purpose of this study is to examine the performance of the 4DVAR technique in retrieving rainstorm mesoscale structure and to reveal the feature of rainstorm mesoscale structure. Results demonstrated that the 4DVAR assimilation method was able to retrieve the detailed structure of wind, thermodynamics, and microphysics fields from dual-Doppler radar observations. The retrieved wind fields agreed with the dual- Doppler synthesized winds and were accurate. The distributions of the retrieved perturbation pressure, perturbation temperature, and microphysics fields were also reasonable through the examination of their physical consistency. Both of the two heavy rainfalls were caused by merging cloud processes. The wind shear and convergence lines at middle and lower levels were their primary dynamical characteristics. The convective system was often related to low-level convergence and upper-level divergence coupled with up- drafts. During its mature stage, the convective system was characterized by low pressure at lower level and high pressure at upper level, associated with warmer at middle level and colder at lower and upper levels than the environment. However, a region of cooling and high pressure occurred in the lower and middle levels compared to warming and low pressure in the upper level during its dissipating ＇.stage. The water vapor, cloud water, and rainwater corresponded to the convergence, the updraft and the intensive reflectivity, respectively.
