Based on the GRAPES（Global/Regional Assimilation and Prediction System） regional ensemble prediction system and 3DVAR（three-dimensional variational） data assimilation system,which are implemented operationally at ...Based on the GRAPES（Global/Regional Assimilation and Prediction System） regional ensemble prediction system and 3DVAR（three-dimensional variational） data assimilation system,which are implemented operationally at the Numerical Weather Prediction Center of the China Meteorological Administration,an ensemble-based 3DVAR（En-3DVAR） hybrid data assimilation system for GRAPES-Meso（the regional mesoscale numerical prediction system of GRAPES） was developed by using the extended control variable technique to implement a hybrid background error covariance that combines the climatological covariance and ensemble-estimated covariance.Considering the problems of the ensemble-based data assimilation part of the system,including the reduction in the degree of geostrophic balance between variables,and the non-smooth analysis increment and its obviously smaller size compared with the 3DVAR data assimilation,corresponding measures were taken to optimize and ameliorate the system.Accordingly,a single pressure observation ensemble-based data assimilation experiment was conducted to ensure that the ensemble-based data assimilation part of the system is correct and reasonable.A number of localization-scale sensitivity tests of the ensemble-based data assimilation were also conducted to determine the most appropriate localization scale.Then,a number of hybrid data assimilation experiments were carried out.The results showed that it was most appropriate to set the weight factor of the ensemble-estimated covariance in the experiments to be 0.8.Compared with the 3DVAR data assimilation,the geopotential height forecast of the hybrid data assimilation experiments improved very little,but the wind forecast improved slightly at each forecast time,especially over 300 hPa.Overall,the hybrid data assimilation demonstrates some advantages over the3 DVAR data assimilation.展开更多
针对夏季黄淮地区一次飑线过程,利用WRF(Weather Research and Forecasting)模式及其Hybrid ETKF-3DVAR同化系统,考察不同生成方案的样本对同化地面观测的影响。集合样本创建方式包括3类:扰动初始背景场的方案(RCV)、使用不同的物理参...针对夏季黄淮地区一次飑线过程,利用WRF(Weather Research and Forecasting)模式及其Hybrid ETKF-3DVAR同化系统,考察不同生成方案的样本对同化地面观测的影响。集合样本创建方式包括3类:扰动初始背景场的方案(RCV)、使用不同的物理参数化方案(PPMP)以及前两者集成方案(BLE)。基于增量场分析,同化地面观测主要调整850 h Pa以下水平风和水汽混合比的空间结构,其中RCV方案侧重于改变水平风的空间分布,PPMP方案侧重于改变水汽混合比的空间结构,BLE方案兼具二者特征。同化地面观测可以间接改善6 h降水预报,其中PPMP试验的降水预报最好,尤其是对降水位置和强度的预报。对比雷达回波观测,RCV试验和BLE试验对弓状回波模拟得较好,BLE试验的模拟较多体现RCV特征。PPMP试验和RCV试验还可改变冷池的位置和强度,同时影响飑线出现和消亡时间,相对而言,PPMP试验影响更大。展开更多
A fine heavy rain forecast plays an important role in the accurate flood forecast, the urban rainstorm watedogging and the secondary hydrological disaster preventions. To improve the heavy rain forecast skills, a hybr...A fine heavy rain forecast plays an important role in the accurate flood forecast, the urban rainstorm watedogging and the secondary hydrological disaster preventions. To improve the heavy rain forecast skills, a hybrid Breeding Growing Mode （BGM）- three-dimensional variational （3DVAR） Data Assimilation （DA） scheme is designed on running the Advanced Research WRF （ARW WRF） model using the Advanced Microwave Sounder Unit A （AMSU-A） satellite radiance data. Results show that： the BGM ense- mble prediction method can provide an effective background field and a flow dependent background error covariance for the BGM- 3DVAR scheme. The BGM-3DVAR scheme adds some effective mesoscale information with similar scales as the heavy rain clu- sters to the initial field in the heavy rain area, which improves the heavy rain forecast significantly, while the 3DVAR scheme adds information with relatively larger scales than the heavy rain clusters to the initial field outside of the heavy rain area, which does not help the heavy rain forecast improvement. Sensitive experiments demonstrate that the flow dependent background error covariance and the ensemble mean background field are both the key factors for adding effective mesoscale information to the heavy rain area, and they are both essential for improving the heavy rain forecasts.展开更多
基金Supported by the National Natural Science Foundation of China(91437113 and 41275111)China Meteorological Administration Special Public Welfare Research Fund(GYHY201506005)
文摘Based on the GRAPES（Global/Regional Assimilation and Prediction System） regional ensemble prediction system and 3DVAR（three-dimensional variational） data assimilation system,which are implemented operationally at the Numerical Weather Prediction Center of the China Meteorological Administration,an ensemble-based 3DVAR（En-3DVAR） hybrid data assimilation system for GRAPES-Meso（the regional mesoscale numerical prediction system of GRAPES） was developed by using the extended control variable technique to implement a hybrid background error covariance that combines the climatological covariance and ensemble-estimated covariance.Considering the problems of the ensemble-based data assimilation part of the system,including the reduction in the degree of geostrophic balance between variables,and the non-smooth analysis increment and its obviously smaller size compared with the 3DVAR data assimilation,corresponding measures were taken to optimize and ameliorate the system.Accordingly,a single pressure observation ensemble-based data assimilation experiment was conducted to ensure that the ensemble-based data assimilation part of the system is correct and reasonable.A number of localization-scale sensitivity tests of the ensemble-based data assimilation were also conducted to determine the most appropriate localization scale.Then,a number of hybrid data assimilation experiments were carried out.The results showed that it was most appropriate to set the weight factor of the ensemble-estimated covariance in the experiments to be 0.8.Compared with the 3DVAR data assimilation,the geopotential height forecast of the hybrid data assimilation experiments improved very little,but the wind forecast improved slightly at each forecast time,especially over 300 hPa.Overall,the hybrid data assimilation demonstrates some advantages over the3 DVAR data assimilation.
文摘针对夏季黄淮地区一次飑线过程,利用WRF(Weather Research and Forecasting)模式及其Hybrid ETKF-3DVAR同化系统,考察不同生成方案的样本对同化地面观测的影响。集合样本创建方式包括3类:扰动初始背景场的方案(RCV)、使用不同的物理参数化方案(PPMP)以及前两者集成方案(BLE)。基于增量场分析,同化地面观测主要调整850 h Pa以下水平风和水汽混合比的空间结构,其中RCV方案侧重于改变水平风的空间分布,PPMP方案侧重于改变水汽混合比的空间结构,BLE方案兼具二者特征。同化地面观测可以间接改善6 h降水预报,其中PPMP试验的降水预报最好,尤其是对降水位置和强度的预报。对比雷达回波观测,RCV试验和BLE试验对弓状回波模拟得较好,BLE试验的模拟较多体现RCV特征。PPMP试验和RCV试验还可改变冷池的位置和强度,同时影响飑线出现和消亡时间,相对而言,PPMP试验影响更大。
基金supported by the National Natural Science Foundation of China (Grant No. 40975031)the National Science Foundation for Young Scientists of China (Grant No.41205074)
文摘A fine heavy rain forecast plays an important role in the accurate flood forecast, the urban rainstorm watedogging and the secondary hydrological disaster preventions. To improve the heavy rain forecast skills, a hybrid Breeding Growing Mode （BGM）- three-dimensional variational （3DVAR） Data Assimilation （DA） scheme is designed on running the Advanced Research WRF （ARW WRF） model using the Advanced Microwave Sounder Unit A （AMSU-A） satellite radiance data. Results show that： the BGM ense- mble prediction method can provide an effective background field and a flow dependent background error covariance for the BGM- 3DVAR scheme. The BGM-3DVAR scheme adds some effective mesoscale information with similar scales as the heavy rain clu- sters to the initial field in the heavy rain area, which improves the heavy rain forecast significantly, while the 3DVAR scheme adds information with relatively larger scales than the heavy rain clusters to the initial field outside of the heavy rain area, which does not help the heavy rain forecast improvement. Sensitive experiments demonstrate that the flow dependent background error covariance and the ensemble mean background field are both the key factors for adding effective mesoscale information to the heavy rain area, and they are both essential for improving the heavy rain forecasts.
