在CMA-GFS(CMA Global Forecast System)全球四维变分资料同化系统(4DVar)基础上,参照BDA(Bogus Data Assimilation)方法,建立了一个全球模式台风初始化方案。该方案通过4DVar同化窗口吸收诊断处理后的1 h间隔台风中心定位及中心气压信...在CMA-GFS(CMA Global Forecast System)全球四维变分资料同化系统(4DVar)基础上,参照BDA(Bogus Data Assimilation)方法,建立了一个全球模式台风初始化方案。该方案通过4DVar同化窗口吸收诊断处理后的1 h间隔台风中心定位及中心气压信息,利用模式动力物理约束产生台风环流。同时,考虑到模式对台风的分辨能力,中心气压数据误差采用动态调整技术。2016年西北太平洋22个台风的试验表明,新方案不仅可以促进初始场中台风环流的生成,还可以显著减小CMAGFS全球预报系统的台风路径和强度预报平均误差,具有业务应用前景。展开更多
Observations of accumulated precipitation are extremely valuable for effectively improving rainfall analysis and forecast. It is, however, difficult to use such observations directly through sequential assimilation me...Observations of accumulated precipitation are extremely valuable for effectively improving rainfall analysis and forecast. It is, however, difficult to use such observations directly through sequential assimilation methods, such as three-dimensional variational data assimilation or an Ensemble Kalman Filter. In this study, the authors illustrate a new approach that makes effective use of precipitation data to improve rainfall forecast. The new method directly obtains an optimal solution in a reduced space by fitting observations with historical time series generated by the model; it also avoids the implementation of tangent linear model and its adjoint. A lot of historical samples are produced as the ensemble of precipitation observations with the fully nonlinear forecast model. The results show that the new approach is capable of extracting information from precipitation observations to improve the analysis and forecast. This method provides comparable performance with the standard fourdimensional variational data assimilation at a much lower computational cost.展开更多
This paper discusses an important issue related to filter divergence in the dimension-reduced projection,four-dimensional variational data assimilation(DRP-4-DVar) approach.Idealized experiments with the Lorenz-96 mod...This paper discusses an important issue related to filter divergence in the dimension-reduced projection,four-dimensional variational data assimilation(DRP-4-DVar) approach.Idealized experiments with the Lorenz-96 model over a period of 200 days showed that the amplitudes of the root mean square errors(RMSEs) reached the same levels as those of the state variables after approximately 100 days because of the accumulation of sampling errors following the cycle of assimilation.Strategies to reduce sampling errors are critical to ensure the quality of ensemble-based assimilation.Numerical experiments showed that localization and reducing observational errors can alleviate,but cannot completely overcome,the filter divergence in the DRP-4-DVar approach,while the method of perturbing observations and the inflation technique can efficiently eliminate the filter divergence problem.展开更多
A typhoon bogus data assimilation scheme (BDA) using dimension-reduced projection four-dimen-sional variational data assimilation (DRP-4-DVar),called DRP-BDA for short,is built in the Advanced Regional Eta Model (AREM...A typhoon bogus data assimilation scheme (BDA) using dimension-reduced projection four-dimen-sional variational data assimilation (DRP-4-DVar),called DRP-BDA for short,is built in the Advanced Regional Eta Model (AREM).As an adjoint-free approach,DRP-BDA saves time,and only several minutes are taken for the full BDA process.To evaluate its performance,the DRP-BDA is applied to a case study on a landfall ty-phoon,Fengshen (2008),from the Northwestern Pacific Ocean to Guangdong province,in which the bogus sea level pressure (SLP) is assimilated as a kind of observa-tion.The results show that a more realistic typhoon with correct center position,stronger warm core vortex,and more reasonable wind fields is reproduced in the analyzed initial condition through the new approach.Compared with the control run (CTRL) initialized with NCEP Final (FNL) Global Tropospheric Analyses,the DRP-BDA leads to an evidently positive impact on typhoon track forecasting and a small positive impact on typhoon inten-sity forecasting.Furthermore,the forecast landfall time conforms to the observed landfall time,and the forecast track error at the 36th hour is 32 km,which is much less than that of the CTRL (450 km).展开更多
文摘在CMA-GFS(CMA Global Forecast System)全球四维变分资料同化系统(4DVar)基础上,参照BDA(Bogus Data Assimilation)方法,建立了一个全球模式台风初始化方案。该方案通过4DVar同化窗口吸收诊断处理后的1 h间隔台风中心定位及中心气压信息,利用模式动力物理约束产生台风环流。同时,考虑到模式对台风的分辨能力,中心气压数据误差采用动态调整技术。2016年西北太平洋22个台风的试验表明,新方案不仅可以促进初始场中台风环流的生成,还可以显著减小CMAGFS全球预报系统的台风路径和强度预报平均误差,具有业务应用前景。
基金the Ministry of Finance of China and China Meteorological Administration for the Special Project of Meteorological Sector (Grant No. GYHY(QX)2007-615)the National Basic Research Program of China (Grant No. 2005CB321703)
文摘Observations of accumulated precipitation are extremely valuable for effectively improving rainfall analysis and forecast. It is, however, difficult to use such observations directly through sequential assimilation methods, such as three-dimensional variational data assimilation or an Ensemble Kalman Filter. In this study, the authors illustrate a new approach that makes effective use of precipitation data to improve rainfall forecast. The new method directly obtains an optimal solution in a reduced space by fitting observations with historical time series generated by the model; it also avoids the implementation of tangent linear model and its adjoint. A lot of historical samples are produced as the ensemble of precipitation observations with the fully nonlinear forecast model. The results show that the new approach is capable of extracting information from precipitation observations to improve the analysis and forecast. This method provides comparable performance with the standard fourdimensional variational data assimilation at a much lower computational cost.
基金the National Basic Research Program of China (973 Program) (Grant No. 2010CB951604)the National High Technology Research and Development Program of China (863 Program) (Grant No. 2010AA012304)+1 种基金the China Meteorological Administration for the R&D Special Fund for Public Welfare Industry (Meteorology) (Grant No. GYHY(QX)200906009)the LASG free exploration fund
文摘This paper discusses an important issue related to filter divergence in the dimension-reduced projection,four-dimensional variational data assimilation(DRP-4-DVar) approach.Idealized experiments with the Lorenz-96 model over a period of 200 days showed that the amplitudes of the root mean square errors(RMSEs) reached the same levels as those of the state variables after approximately 100 days because of the accumulation of sampling errors following the cycle of assimilation.Strategies to reduce sampling errors are critical to ensure the quality of ensemble-based assimilation.Numerical experiments showed that localization and reducing observational errors can alleviate,but cannot completely overcome,the filter divergence in the DRP-4-DVar approach,while the method of perturbing observations and the inflation technique can efficiently eliminate the filter divergence problem.
基金the Ministry of Finance of Chinathe China Meteorological Administration for the Special Project of Meteorological Sector (Grant No.GYHYQX200906009)the National Natural Science Foundation of China for the Innovation Group Project (Grant No.40821092)
文摘A typhoon bogus data assimilation scheme (BDA) using dimension-reduced projection four-dimen-sional variational data assimilation (DRP-4-DVar),called DRP-BDA for short,is built in the Advanced Regional Eta Model (AREM).As an adjoint-free approach,DRP-BDA saves time,and only several minutes are taken for the full BDA process.To evaluate its performance,the DRP-BDA is applied to a case study on a landfall ty-phoon,Fengshen (2008),from the Northwestern Pacific Ocean to Guangdong province,in which the bogus sea level pressure (SLP) is assimilated as a kind of observa-tion.The results show that a more realistic typhoon with correct center position,stronger warm core vortex,and more reasonable wind fields is reproduced in the analyzed initial condition through the new approach.Compared with the control run (CTRL) initialized with NCEP Final (FNL) Global Tropospheric Analyses,the DRP-BDA leads to an evidently positive impact on typhoon track forecasting and a small positive impact on typhoon inten-sity forecasting.Furthermore,the forecast landfall time conforms to the observed landfall time,and the forecast track error at the 36th hour is 32 km,which is much less than that of the CTRL (450 km).
