This study explores the use of the hierarchical ensemble filter to determine the localized influence of observations in the Weather Research and Forecasting ensemble square root filtering(WRF-EnSRF)assimilation system...This study explores the use of the hierarchical ensemble filter to determine the localized influence of observations in the Weather Research and Forecasting ensemble square root filtering(WRF-EnSRF)assimilation system.With error correlations between observations and background field state variables considered,the adaptive localization approach is applied to conduct a series of ideal storm-scale data assimilation experiments using simulated Doppler radar data.Comparisons between adaptive and empirical localization methods are made,and the feasibility of adaptive localization for storm-scale ensemble Kalman filter assimilation is demonstrated.Unlike empirical localization,which relies on prior knowledge of distance between observations and background field,the hierarchical ensemble filter provides continuously updating localization influence weights adaptively.The adaptive scheme improves assimilation quality during rapid storm development and enhances assimilation of reflectivity observations.The characteristics of both the observation type and the storm development stage should be considered when identifying the most appropriate localization method.Ultimately,combining empirical and adaptive methods can optimize assimilation quality.展开更多
How to obtain fast-growth errors, which is comparable to the actual forecast growth error, is a crucial problem in ensemble forecast （EF）. The method, Breeding of Growth Modes （BGM）, which has been used to generat...How to obtain fast-growth errors, which is comparable to the actual forecast growth error, is a crucial problem in ensemble forecast （EF）. The method, Breeding of Growth Modes （BGM）, which has been used to generate perturbations for medium-range EF at NCEP, simulates the development of fast-growth errors in the analysis cycle, and is a reasonable choice in capturing growing errors modes, especially for extreme weather by BGM. An ideal supercell storm, simulated by Weather Research Forecast model （WRF）, occurred in central Oklahoma on 20 May 1977. This simulation was used to study the application of BGM methods in the meso-scale strong convective Ensemble Prediction System （EPS）. We compared the forecasting skills of EPS by different pertubation methods, like Monte-Carlo and BGM. The results show that the ensemble average forecast based on Monte-Carlo with statistics meaning is superior to the single-deterministic prediction, but a less dynamic process of the method leads to a smaller spread than expected. The fast-growth errors of BGM are comparable to the actual short-range forecast error and a more appropriate ensemble spread. Considering evaluation indexes and scores, the forecast skills of EPS by BGM is higher than Monte-Carlo＇s. Furthermore, various breeding cycles have different effects on precipitation and non-precipitation fields, confirmation of reasonable cycles need consider balance between variables.展开更多
为研究透水沥青路面长期使用过程中的整体滞蓄雨水能力,文章以颍上县纬八路为例,建立研究道路的暴雨洪水管理模型(storm water management model,SWMM),通过设计短历时降雨雨型剖析了路面在丰水、平水、枯水年3种情景下的滞蓄雨水能力,...为研究透水沥青路面长期使用过程中的整体滞蓄雨水能力,文章以颍上县纬八路为例,建立研究道路的暴雨洪水管理模型(storm water management model,SWMM),通过设计短历时降雨雨型剖析了路面在丰水、平水、枯水年3种情景下的滞蓄雨水能力,并对路面在设计使用年限内的综合滞蓄雨效果进行模拟分析。结果表明:路面在平水年以及枯水年下年径流总量控制率分别达到72.14%和75.37%,表现出较强的雨水滞蓄能力;丰水年下路面的雨水滞蓄能力略低,除了特丰水年外,丰水年间的年径流总量控制率仅略低于目标年径流总量控制率,在3%以内;路面在设计使用年限内总平均年径流总量控制率为66.34%,符合海绵城市建设要求。展开更多
利用自主构建的基于风暴尺度的WRF-EnSRF(weather research and forecasting ensemble square root filter)系统同化实际多普勒雷达资料,检验该同化系统在包括飑线、超级单体风暴和多单体风暴3个不同结构类型的强对流天气过程的同化效果...利用自主构建的基于风暴尺度的WRF-EnSRF(weather research and forecasting ensemble square root filter)系统同化实际多普勒雷达资料,检验该同化系统在包括飑线、超级单体风暴和多单体风暴3个不同结构类型的强对流天气过程的同化效果,并考察了初始场扰动时不同强度的位温和水汽扰动对集合离散度以及同化效果的影响。结果表明,在3个个例中该同化系统均表现出有效的同化能力,各分析结果均比较合理,径向速度和反射率因子分析的增量均方差在经过24min同化后分别下降到3～5m/s和10dBz,并维持至60min同化结束。预报场集合离散度和同化效果对热力场的扰动强度比较敏感,适当增加初始扰动时位温和水汽的扰动强度有利于提高集合离散度和改善径向速度的分析效果。展开更多
基金Liaoning Meteorological Bureau Scientific Research Program(202103*)Bohai Regional Science and Technology Collaborative Innovation Fund(QYXM201607)。
文摘This study explores the use of the hierarchical ensemble filter to determine the localized influence of observations in the Weather Research and Forecasting ensemble square root filtering(WRF-EnSRF)assimilation system.With error correlations between observations and background field state variables considered,the adaptive localization approach is applied to conduct a series of ideal storm-scale data assimilation experiments using simulated Doppler radar data.Comparisons between adaptive and empirical localization methods are made,and the feasibility of adaptive localization for storm-scale ensemble Kalman filter assimilation is demonstrated.Unlike empirical localization,which relies on prior knowledge of distance between observations and background field,the hierarchical ensemble filter provides continuously updating localization influence weights adaptively.The adaptive scheme improves assimilation quality during rapid storm development and enhances assimilation of reflectivity observations.The characteristics of both the observation type and the storm development stage should be considered when identifying the most appropriate localization method.Ultimately,combining empirical and adaptive methods can optimize assimilation quality.
基金supported jointly by the Nature Science Foundation of China (Project No:40875068)Public-Welfare Meteorological Research Foundation (ProjectNo:GYHY200806029)
文摘How to obtain fast-growth errors, which is comparable to the actual forecast growth error, is a crucial problem in ensemble forecast （EF）. The method, Breeding of Growth Modes （BGM）, which has been used to generate perturbations for medium-range EF at NCEP, simulates the development of fast-growth errors in the analysis cycle, and is a reasonable choice in capturing growing errors modes, especially for extreme weather by BGM. An ideal supercell storm, simulated by Weather Research Forecast model （WRF）, occurred in central Oklahoma on 20 May 1977. This simulation was used to study the application of BGM methods in the meso-scale strong convective Ensemble Prediction System （EPS）. We compared the forecasting skills of EPS by different pertubation methods, like Monte-Carlo and BGM. The results show that the ensemble average forecast based on Monte-Carlo with statistics meaning is superior to the single-deterministic prediction, but a less dynamic process of the method leads to a smaller spread than expected. The fast-growth errors of BGM are comparable to the actual short-range forecast error and a more appropriate ensemble spread. Considering evaluation indexes and scores, the forecast skills of EPS by BGM is higher than Monte-Carlo＇s. Furthermore, various breeding cycles have different effects on precipitation and non-precipitation fields, confirmation of reasonable cycles need consider balance between variables.
文摘为研究透水沥青路面长期使用过程中的整体滞蓄雨水能力,文章以颍上县纬八路为例,建立研究道路的暴雨洪水管理模型(storm water management model,SWMM),通过设计短历时降雨雨型剖析了路面在丰水、平水、枯水年3种情景下的滞蓄雨水能力,并对路面在设计使用年限内的综合滞蓄雨效果进行模拟分析。结果表明:路面在平水年以及枯水年下年径流总量控制率分别达到72.14%和75.37%,表现出较强的雨水滞蓄能力;丰水年下路面的雨水滞蓄能力略低,除了特丰水年外,丰水年间的年径流总量控制率仅略低于目标年径流总量控制率,在3%以内;路面在设计使用年限内总平均年径流总量控制率为66.34%,符合海绵城市建设要求。
文摘利用自主构建的基于风暴尺度的WRF-EnSRF(weather research and forecasting ensemble square root filter)系统同化实际多普勒雷达资料,检验该同化系统在包括飑线、超级单体风暴和多单体风暴3个不同结构类型的强对流天气过程的同化效果,并考察了初始场扰动时不同强度的位温和水汽扰动对集合离散度以及同化效果的影响。结果表明,在3个个例中该同化系统均表现出有效的同化能力,各分析结果均比较合理,径向速度和反射率因子分析的增量均方差在经过24min同化后分别下降到3～5m/s和10dBz,并维持至60min同化结束。预报场集合离散度和同化效果对热力场的扰动强度比较敏感,适当增加初始扰动时位温和水汽的扰动强度有利于提高集合离散度和改善径向速度的分析效果。
