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 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
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
A new forecasting system-the System of Multigrid Nonlinear Least-squares Four-dimensional Variational(NLS-4DVar)Data Assimilation for Numerical Weather Prediction(SNAP)-was established by building upon the multigrid N...A new forecasting system-the System of Multigrid Nonlinear Least-squares Four-dimensional Variational(NLS-4DVar)Data Assimilation for Numerical Weather Prediction(SNAP)-was established by building upon the multigrid NLS-4DVar data assimilation scheme,the operational Gridpoint Statistical Interpolation(GSI)−based data-processing and observation operators,and the widely used Weather Research and Forecasting numerical model.Drawing upon lessons learned from the superiority of the operational GSI analysis system,for its various observation operators and the ability to assimilate multiple-source observations,SNAP adopts GSI-based data-processing and observation operator modules to compute the observation innovations.The multigrid NLS-4DVar assimilation framework is used for the analysis,which can adequately correct errors from large to small scales and accelerate iteration solutions.The analysis variables are model state variables,rather than the control variables adopted in the conventional 4DVar system.Currently,we have achieved the assimilation of conventional observations,and we will continue to improve the assimilation of radar and satellite observations in the future.SNAP was evaluated by case evaluation experiments and one-week cycling assimilation experiments.In the case evaluation experiments,two six-hour time windows were established for assimilation experiments and precipitation forecasts were verified against hourly precipitation observations from more than 2400 national observation sites.This showed that SNAP can absorb observations and improve the initial field,thereby improving the precipitation forecast.In the one-week cycling assimilation experiments,six-hourly assimilation cycles were run in one week.SNAP produced slightly lower forecast RMSEs than the GSI 4DEnVar(Four-dimensional Ensemble Variational)as a whole and the threat scores of precipitation forecasts initialized from the analysis of SNAP were higher than those obtained from the analysis of GSI 4DEnVar.展开更多
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the National Key Research and Development Program of China(Grant No.2016YFA0600203)the National Natural Science Foundation of China(Grant No.41575100)+1 种基金the Key Research Program of Frontier Sciences,Chinese Academy of Sciences(Grant No.QYZDY-SSW-DQC012)the CMA Special Public Welfare Research Fund(Grant No.GYHY201506002).
文摘A new forecasting system-the System of Multigrid Nonlinear Least-squares Four-dimensional Variational(NLS-4DVar)Data Assimilation for Numerical Weather Prediction(SNAP)-was established by building upon the multigrid NLS-4DVar data assimilation scheme,the operational Gridpoint Statistical Interpolation(GSI)−based data-processing and observation operators,and the widely used Weather Research and Forecasting numerical model.Drawing upon lessons learned from the superiority of the operational GSI analysis system,for its various observation operators and the ability to assimilate multiple-source observations,SNAP adopts GSI-based data-processing and observation operator modules to compute the observation innovations.The multigrid NLS-4DVar assimilation framework is used for the analysis,which can adequately correct errors from large to small scales and accelerate iteration solutions.The analysis variables are model state variables,rather than the control variables adopted in the conventional 4DVar system.Currently,we have achieved the assimilation of conventional observations,and we will continue to improve the assimilation of radar and satellite observations in the future.SNAP was evaluated by case evaluation experiments and one-week cycling assimilation experiments.In the case evaluation experiments,two six-hour time windows were established for assimilation experiments and precipitation forecasts were verified against hourly precipitation observations from more than 2400 national observation sites.This showed that SNAP can absorb observations and improve the initial field,thereby improving the precipitation forecast.In the one-week cycling assimilation experiments,six-hourly assimilation cycles were run in one week.SNAP produced slightly lower forecast RMSEs than the GSI 4DEnVar(Four-dimensional Ensemble Variational)as a whole and the threat scores of precipitation forecasts initialized from the analysis of SNAP were higher than those obtained from the analysis of GSI 4DEnVar.
