The Northeast China Cold Vortex(NCCV)is a common cut-off low-pressure system in Northeast China,frequently causing localized heavy rainfall,strong winds,and thunderstorms during the early summer.In this study,the clea...The Northeast China Cold Vortex(NCCV)is a common cut-off low-pressure system in Northeast China,frequently causing localized heavy rainfall,strong winds,and thunderstorms during the early summer.In this study,the clear-sky radiance of 48 longwave channels from the FY-4B Geostationary Interferometric Infrared Sounder(GIIRS)is assimilated into the China Meteorological Administration mesoscale model(CMA-MESO)to evaluate its impact on NCCV development and its effects on rainfall forecasting.The results show that after assimilating the GIIRS radiance data,the warm center at 200 hPa and the cold center at 850 hPa of the NCCV are strengthened,and the dry intrusion at 850 hPa becomes more pronounced.This leads to a stronger NCCV intensity in the following 24 hours and brings the precipitation intensity and area closer to the observation,resulting in significant improvements compared to the experiments that do not assimilate GIIRS radiance data.Furthermore,it is found that the enhancement of the precipitation forecast is associated with the strengthening of cold air in the middle and lower troposphere,which intensifies the uplift of the warm,moist airflow.These results highlight the potential value of GIIRS data assimilation in enhancing early warnings and forecasts of extreme weather events influenced by the NCCV.展开更多
风场预报是智能网格预报的重要支撑,提高风场预报准确率,能够为风能预报提供核心保障。在综合评估2023年汛期CMA-MESO 3 km(China Meteorological Administration Mesoscale Model at 3 km resolution)模式在山西逐小时10 m风预报能力...风场预报是智能网格预报的重要支撑,提高风场预报准确率,能够为风能预报提供核心保障。在综合评估2023年汛期CMA-MESO 3 km(China Meteorological Administration Mesoscale Model at 3 km resolution)模式在山西逐小时10 m风预报能力的基础上,基于自适应Kalman滤波方法,开展针对纬向风(U)、经向风(V)的客观订正,以期建立适应山西复杂地形特征的客观预报方案,促进国产模式本地化业务应用。结果表明:①全风速预报偏大,预报误差呈“单峰型”日变化,峰值出现在18:00-20:00,正偏差主要位于忻定和太原盆地以及山西南部。②U、V预报误差与预报值呈显著正相关,需考虑不同强度预报风速误差随时效变化的特征,避免订正不足或过订正。③Kalman滤波方案(KM)订正幅度小且不稳定,订正后均方根误差R MSE削减不足6%,准确率提升不足2%。④基于动态分级的改进方案(CBKM)突破KM订正瓶颈,更准确地估计系统误差并有效订正,更好再现不同地区风速日变化,平均误差M E趋近0,R MSE削减32.8%,风向、风速预报准确率分别提升8.29%、7.92%,峰值时刻订正率达83.49%。展开更多
Vector winds play a crucial role in weather and climate,as well as the effective utilization of wind energy resources.However,limited research has been conducted on treating the wind field as a vector field in the eva...Vector winds play a crucial role in weather and climate,as well as the effective utilization of wind energy resources.However,limited research has been conducted on treating the wind field as a vector field in the evaluation of numerical weather prediction models.In this study,the authors treat vector winds as a whole by employing a vector field evaluation method,and evaluate the mesoscale model of the China Meteorological Administration(CMA-MESO)and ECMWF forecast,with reference to ERA5 reanalysis,in terms of multiple aspects of vector winds over eastern China in 2022.The results show that the ECMWF forecast is superior to CMA-MESO in predicting the spatial distribution and intensity of 10-m vector winds.Both models overestimate the wind speed in East China,and CMA-MESO overestimates the wind speed to a greater extent.The forecasting skill of the vector wind field in both models decreases with increasing lead time.The forecasting skill of CMA-MESO fluctuates more and decreases faster than that of the ECMWF forecast.There is a significant negative correlation between the model vector wind forecasting skill and terrain height.This study provides a scientific evaluation of the local application of vector wind forecasts of the CMA-MESO model and ECMWF forecast.展开更多
Numerical models play an important role in convective-scale forecasting,and dual-polarization radar observations can provide detailed microphysical data.In this study,we implement a direct assimilation operator for du...Numerical models play an important role in convective-scale forecasting,and dual-polarization radar observations can provide detailed microphysical data.In this study,we implement a direct assimilation operator for dual-polarization radar data using the hydrometeor background error covariance(HBEC)in the China Meteorological Administration MESO-scale weather forecasting system(CMA-MESO,formerly GRAPES-MESO)and conducted assimilation and forecasting experiments with X-band and S-band dual-polarization radar data on two cases.The results indicate that the direct assimilation of dual-polarization radar data enhanced the microphysical fields and the thermodynamic structure of convective systems to some extent based on the HBEC,thereby improving precipitation forecasts.Among the sensitivity tests of microphysical parameterization schemes,including the LIUMA scheme,the THOMPSON scheme,and the WSM6scheme(WRF Single-Moment 6-class),we find that the greatest improvement in the equivalent potential temperature,relative humidity,wind,and accumulated precipitation forecasts occurred in the experiment using the WSM6 scheme,as the distribution of solid precipitation particles was closer to the hydrometeor classification algorithm from the dualpolarization radar observations in our cases.展开更多
重构GRAPES(Global/Regional Assimilation and Prediction System)全球、区域一体化变分同化系统中的极小化控制变量,提升中、小尺度同化分析能力,为中国气象局业务区域数值预报系统CMA-MESO提供千米尺度适用的同化方案。新方案用纬向...重构GRAPES(Global/Regional Assimilation and Prediction System)全球、区域一体化变分同化系统中的极小化控制变量,提升中、小尺度同化分析能力,为中国气象局业务区域数值预报系统CMA-MESO提供千米尺度适用的同化方案。新方案用纬向风速(u)和经向风速(v)替代原有流函数和势函数作为新的风场控制变量,采用温度和地面气压(T,ps)替代原有非平衡无量纲气压作为新的质量场控制变量,同时不再考虑准地转平衡约束,而是采用连续方程弱约束保证分析平衡。背景误差参数统计和数值试验结果表明,采用重构后的极小化控制变量,观测信息传播更加局地,分析结构更加合理,避免了原方案在中、小尺度应用时存在的虚假相关问题。连续方程弱约束的引入,限制了同化分析中辐合、辐散的不合理增长,帮助新方案在分析更加局地的同时保证分析平衡。为期1个月的连续同化循环和预报试验结果表明,新方案可以减小风场和质量场分析误差,CMAMESO系统地面降水和10 m风场的预报评分显著提升。展开更多
基金sponsored by the National Natural Science Foundation of China(Grant No.42275171)the Basic Research Operating Expenses of the Institute of Meteorological Sciences,CMA(Grant No.2023Z019)+3 种基金the National Key Research and Development Program of China(Grant No.2022YFF0801304)the China Meteorological Administration Youth Innovation Team Fund(Grant No.CMA2024QN05)a Liaoning Provincial Meteorological Bureau Project(Grant No.D202201)Shenyang Institute of Atmospheric Environment Projects(Grant Nos.2022SYIAEJY13 and 2018SYIAEZD5).
文摘The Northeast China Cold Vortex(NCCV)is a common cut-off low-pressure system in Northeast China,frequently causing localized heavy rainfall,strong winds,and thunderstorms during the early summer.In this study,the clear-sky radiance of 48 longwave channels from the FY-4B Geostationary Interferometric Infrared Sounder(GIIRS)is assimilated into the China Meteorological Administration mesoscale model(CMA-MESO)to evaluate its impact on NCCV development and its effects on rainfall forecasting.The results show that after assimilating the GIIRS radiance data,the warm center at 200 hPa and the cold center at 850 hPa of the NCCV are strengthened,and the dry intrusion at 850 hPa becomes more pronounced.This leads to a stronger NCCV intensity in the following 24 hours and brings the precipitation intensity and area closer to the observation,resulting in significant improvements compared to the experiments that do not assimilate GIIRS radiance data.Furthermore,it is found that the enhancement of the precipitation forecast is associated with the strengthening of cold air in the middle and lower troposphere,which intensifies the uplift of the warm,moist airflow.These results highlight the potential value of GIIRS data assimilation in enhancing early warnings and forecasts of extreme weather events influenced by the NCCV.
文摘风场预报是智能网格预报的重要支撑,提高风场预报准确率,能够为风能预报提供核心保障。在综合评估2023年汛期CMA-MESO 3 km(China Meteorological Administration Mesoscale Model at 3 km resolution)模式在山西逐小时10 m风预报能力的基础上,基于自适应Kalman滤波方法,开展针对纬向风(U)、经向风(V)的客观订正,以期建立适应山西复杂地形特征的客观预报方案,促进国产模式本地化业务应用。结果表明:①全风速预报偏大,预报误差呈“单峰型”日变化,峰值出现在18:00-20:00,正偏差主要位于忻定和太原盆地以及山西南部。②U、V预报误差与预报值呈显著正相关,需考虑不同强度预报风速误差随时效变化的特征,避免订正不足或过订正。③Kalman滤波方案(KM)订正幅度小且不稳定,订正后均方根误差R MSE削减不足6%,准确率提升不足2%。④基于动态分级的改进方案(CBKM)突破KM订正瓶颈,更准确地估计系统误差并有效订正,更好再现不同地区风速日变化,平均误差M E趋近0,R MSE削减32.8%,风向、风速预报准确率分别提升8.29%、7.92%,峰值时刻订正率达83.49%。
基金primarily supported by the National Key R&D Program of China[grant number 2021YFC3000904]the Jiangsu Provincial Key Technology R&D Program[grant number BE2022851]National Natural Science Foundation of China[grant number 42405035]。
文摘Vector winds play a crucial role in weather and climate,as well as the effective utilization of wind energy resources.However,limited research has been conducted on treating the wind field as a vector field in the evaluation of numerical weather prediction models.In this study,the authors treat vector winds as a whole by employing a vector field evaluation method,and evaluate the mesoscale model of the China Meteorological Administration(CMA-MESO)and ECMWF forecast,with reference to ERA5 reanalysis,in terms of multiple aspects of vector winds over eastern China in 2022.The results show that the ECMWF forecast is superior to CMA-MESO in predicting the spatial distribution and intensity of 10-m vector winds.Both models overestimate the wind speed in East China,and CMA-MESO overestimates the wind speed to a greater extent.The forecasting skill of the vector wind field in both models decreases with increasing lead time.The forecasting skill of CMA-MESO fluctuates more and decreases faster than that of the ECMWF forecast.There is a significant negative correlation between the model vector wind forecasting skill and terrain height.This study provides a scientific evaluation of the local application of vector wind forecasts of the CMA-MESO model and ECMWF forecast.
基金sponsored by the National Natural Science Foundation of China(U2442601 and U2442218)the High Performance Computing Platform of Nanjing University of Information Science&Technology(NUIST)for their support of this work。
文摘Numerical models play an important role in convective-scale forecasting,and dual-polarization radar observations can provide detailed microphysical data.In this study,we implement a direct assimilation operator for dual-polarization radar data using the hydrometeor background error covariance(HBEC)in the China Meteorological Administration MESO-scale weather forecasting system(CMA-MESO,formerly GRAPES-MESO)and conducted assimilation and forecasting experiments with X-band and S-band dual-polarization radar data on two cases.The results indicate that the direct assimilation of dual-polarization radar data enhanced the microphysical fields and the thermodynamic structure of convective systems to some extent based on the HBEC,thereby improving precipitation forecasts.Among the sensitivity tests of microphysical parameterization schemes,including the LIUMA scheme,the THOMPSON scheme,and the WSM6scheme(WRF Single-Moment 6-class),we find that the greatest improvement in the equivalent potential temperature,relative humidity,wind,and accumulated precipitation forecasts occurred in the experiment using the WSM6 scheme,as the distribution of solid precipitation particles was closer to the hydrometeor classification algorithm from the dualpolarization radar observations in our cases.
