A dual-resolution（DR） version of a regional ensemble Kalman filter（EnKF）-3D ensemble variational（3DEnVar） coupled hybrid data assimilation system is implemented as a prototype for the operational Rapid Refresh f...A dual-resolution（DR） version of a regional ensemble Kalman filter（EnKF）-3D ensemble variational（3DEnVar） coupled hybrid data assimilation system is implemented as a prototype for the operational Rapid Refresh forecasting system. The DR 3DEnVar system combines a high-resolution（HR） deterministic background forecast with lower-resolution（LR） EnKF ensemble perturbations used for flow-dependent background error covariance to produce a HR analysis. The computational cost is substantially reduced by running the ensemble forecasts and EnKF analyses at LR. The DR 3DEnVar system is tested with 3-h cycles over a 9-day period using a 40/13-km grid spacing combination. The HR forecasts from the DR hybrid analyses are compared with forecasts launched from HR Gridpoint Statistical Interpolation（GSI） 3D variational（3DVar）analyses, and single LR hybrid analyses interpolated to the HR grid. With the DR 3DEnVar system, a 90% weight for the ensemble covariance yields the lowest forecast errors and the DR hybrid system clearly outperforms the HR GSI 3DVar.Humidity and wind forecasts are also better than those launched from interpolated LR hybrid analyses, but the temperature forecasts are slightly worse. The humidity forecasts are improved most. For precipitation forecasts, the DR 3DEnVar always outperforms HR GSI 3DVar. It also outperforms the LR 3DEnVar, except for the initial forecast period and lower thresholds.展开更多
The partial cycle(PC)strategy has been used in many rapid refresh cycle systems(RRC)for regional short-range weather forecasting.Since the strategy periodically reinitializes the regional model(RM)from the global mode...The partial cycle(PC)strategy has been used in many rapid refresh cycle systems(RRC)for regional short-range weather forecasting.Since the strategy periodically reinitializes the regional model(RM)from the global model(GM)forecasts to correct the large-scale drift,it has replaced the traditional full cycle(FC)strategy in many RRC systems.However,the extra spin-up in the PC strategy increases the computer burden on RRC and generates discontinuous smallscale systems among cycles.This study returns to the FC strategy but with initial fields generated by dynamic blending(DB)and data assimilation(DA).The DB ingests the time-varied large-scale information from the GM to the RM to generate less-biased background fields.Then the DA is performed.We applied the new FC strategy in a series of 7-day batch forecasts with the 3-hour cycle in July 2018,and February,April,and October 2019 over China using a Weather Research and Forecast(WRF)model-based RRC.A comparison shows that the new FC strategy results in less model bias than the PC strategy in most state variables and improves the forecast skills for moderate and light precipitation.The new FC strategy also allows the model to reach a balanced state earlier and gives favorable forecast continuity between adjacent cycles.Hence,this new FC strategy has potential to be applied in RRC forecast systems to replace the currently used PC strategy.展开更多
利用SMS-WARR快速更新同化,对三种观测资料(雷达、探空、AMDAR)进行敏感性试验。并且通过对2011年7月31日发生在上海的局地强对流过程的模拟,分析不同类型观测资料的影响。结果表明:雷达资料通过云分析优化了初始场中的水物质含量,使云...利用SMS-WARR快速更新同化,对三种观测资料(雷达、探空、AMDAR)进行敏感性试验。并且通过对2011年7月31日发生在上海的局地强对流过程的模拟,分析不同类型观测资料的影响。结果表明:雷达资料通过云分析优化了初始场中的水物质含量,使云的分布更合理,调整了中小尺度对流系统的结构和强度,降水落区和强度预报得到改进;探空资料在试验中仅使用了一次,但可持续影响后续时刻的模拟,500 h Pa、700 h Pa和850 h Pa高度场和风场均不同程度受到影响,相应的地面温度也发生了变化;同化AMDAR资料后,形势场得到了修正,分析场更加接近实况,改善了降水预报。展开更多
基金supported by the National Natural Science Foundation of China (Grant Nos.41730965,41775099 and 2017YFC1502104)PAPD (the Priority Academic Program Development of Jiangsu Higher Education Institutions)
文摘A dual-resolution（DR） version of a regional ensemble Kalman filter（EnKF）-3D ensemble variational（3DEnVar） coupled hybrid data assimilation system is implemented as a prototype for the operational Rapid Refresh forecasting system. The DR 3DEnVar system combines a high-resolution（HR） deterministic background forecast with lower-resolution（LR） EnKF ensemble perturbations used for flow-dependent background error covariance to produce a HR analysis. The computational cost is substantially reduced by running the ensemble forecasts and EnKF analyses at LR. The DR 3DEnVar system is tested with 3-h cycles over a 9-day period using a 40/13-km grid spacing combination. The HR forecasts from the DR hybrid analyses are compared with forecasts launched from HR Gridpoint Statistical Interpolation（GSI） 3D variational（3DVar）analyses, and single LR hybrid analyses interpolated to the HR grid. With the DR 3DEnVar system, a 90% weight for the ensemble covariance yields the lowest forecast errors and the DR hybrid system clearly outperforms the HR GSI 3DVar.Humidity and wind forecasts are also better than those launched from interpolated LR hybrid analyses, but the temperature forecasts are slightly worse. The humidity forecasts are improved most. For precipitation forecasts, the DR 3DEnVar always outperforms HR GSI 3DVar. It also outperforms the LR 3DEnVar, except for the initial forecast period and lower thresholds.
基金the two anonymous reviewers.This work is supported by the National Key R&D Program of China(2018YFC1506803,2019YFB2102901)National Natural Science Foundation of China(Grant 41705135,41790474).
文摘The partial cycle(PC)strategy has been used in many rapid refresh cycle systems(RRC)for regional short-range weather forecasting.Since the strategy periodically reinitializes the regional model(RM)from the global model(GM)forecasts to correct the large-scale drift,it has replaced the traditional full cycle(FC)strategy in many RRC systems.However,the extra spin-up in the PC strategy increases the computer burden on RRC and generates discontinuous smallscale systems among cycles.This study returns to the FC strategy but with initial fields generated by dynamic blending(DB)and data assimilation(DA).The DB ingests the time-varied large-scale information from the GM to the RM to generate less-biased background fields.Then the DA is performed.We applied the new FC strategy in a series of 7-day batch forecasts with the 3-hour cycle in July 2018,and February,April,and October 2019 over China using a Weather Research and Forecast(WRF)model-based RRC.A comparison shows that the new FC strategy results in less model bias than the PC strategy in most state variables and improves the forecast skills for moderate and light precipitation.The new FC strategy also allows the model to reach a balanced state earlier and gives favorable forecast continuity between adjacent cycles.Hence,this new FC strategy has potential to be applied in RRC forecast systems to replace the currently used PC strategy.
文摘利用SMS-WARR快速更新同化,对三种观测资料(雷达、探空、AMDAR)进行敏感性试验。并且通过对2011年7月31日发生在上海的局地强对流过程的模拟,分析不同类型观测资料的影响。结果表明:雷达资料通过云分析优化了初始场中的水物质含量,使云的分布更合理,调整了中小尺度对流系统的结构和强度,降水落区和强度预报得到改进;探空资料在试验中仅使用了一次,但可持续影响后续时刻的模拟,500 h Pa、700 h Pa和850 h Pa高度场和风场均不同程度受到影响,相应的地面温度也发生了变化;同化AMDAR资料后,形势场得到了修正,分析场更加接近实况,改善了降水预报。
