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
利用2020年夏季(6~8月)CMA-MESO模式逐日08:00(北京时,下同)起报的12~36 h逐时降水预报数据和地面—卫星—雷达三源融合逐时降水产品,着眼于小时尺度降水特征,细致评估了CMA-MESO对四川盆地及周边地区的降水预报性能。结果表明,CMA-MES...利用2020年夏季(6~8月)CMA-MESO模式逐日08:00(北京时,下同)起报的12~36 h逐时降水预报数据和地面—卫星—雷达三源融合逐时降水产品,着眼于小时尺度降水特征,细致评估了CMA-MESO对四川盆地及周边地区的降水预报性能。结果表明,CMA-MESO较好把握了夏季降水的空间分布特征,即小时平均降水量和降水频率的大值区位于四川盆地西部、北部和东部的高海拔山区,而降水强度大值区主要位于山脉迎风坡一侧,但CMA-MESO预报的降水量和频率大值区位置较观测偏南。CMA-MESO合理描述了研究区域内降水量和频率峰值时间位相自西向东逐步滞后的特征,能够把握区域平均的降水量和频率清晨主峰、傍晚次峰的双峰形态以及降水强度的单峰特征,但预报的降水日变化位相超前于观测。CMA-MESO预报的逐时降水量均大于观测,明显的降水量预报正偏差发生于夜间(21:00至次日03:00)和午后至傍晚(14:00~20:00),分别由一般性降水(0.1~10 mm h-1)预报偏差和强降水(≥10 mm h-1)预报偏差主导,其偏差大值区分别位于青藏高原东南缘至四川盆地西部和四川盆地以东、以南地区,模式对热力和动力场的预报偏差结合地形的影响是降水量预报偏差的成因。展开更多
Assimilation of surface observations including 2-m temperature(T_(2m))in numerical weather prediction(NWP)models remains a challenging problem owing to differences between the elevation of model terrain and that of ac...Assimilation of surface observations including 2-m temperature(T_(2m))in numerical weather prediction(NWP)models remains a challenging problem owing to differences between the elevation of model terrain and that of actual observation stations.NWP results can be improved only if surface observations are assimilated appropriately.In this study,a T_(2m)data assimilation scheme that carefully considers misrepresentation of model and station terrain was established by using the three-dimensional variational data assimilation(3DVAR)system of the China Meteorological Administration mesoscale model(CMA-MESO).The corresponding forward observation operator,tangent linear operator,and adjoint operator for the T_(2m)observations under three terrain mismatch treatments were developed.The T_(2m)data were assimilated in the same method as that adopted for temperature sounding data with additional representative errors,when station terrain was 100 m higher than model terrain;otherwise,the T_(2m)data were assimilated by using the surface similarity theory assimilation operator.Furthermore,if station terrain was lower than model terrain,additional representative errors were stipulated and corrected.Test of a rainfall case showed that the observation innovation and analysis residuals both exhibited Gaussian distribution and that the analysis increment was reasonable.Moreover,it was found that on completion of the data assimilation cycle,T_(2m)data assimilation obviously influenced the temperature,wind,and relative humidity fields throughout the troposphere,with the greatest impact evident in the lower layers,and that both the area and the intensity of rainfall were better forecasted,especially for the first 12hours.Long-term continuous experiments for 2–28 February and 5–20 July 2020,further verified that T_(2m)data assimilation reduced deviations not only in T_(2m)but also in 10-m wind forecasts.More importantly,the precipitation equitable threat scores were improved over the two experimental periods.In summary,this study confirmed that the T_(2m)data assimilation scheme that we implemented in the kilometer-scale CMA-MESO 3DVAR system is effective.展开更多
利用加密自动气象观测站资料、ERA5再分析资料和欧洲中心ECMWF(European Centre for Medium-Range Weather Forecasts)模式、中国气象局CMA-MESO(China Meteorological Administration Mesoscale Model)模式产品,对2020年7月17—18日江...利用加密自动气象观测站资料、ERA5再分析资料和欧洲中心ECMWF(European Centre for Medium-Range Weather Forecasts)模式、中国气象局CMA-MESO(China Meteorological Administration Mesoscale Model)模式产品,对2020年7月17—18日江淮地区一次特大暴雨过程的预报效果进行检验与分析,并对数值模式降水预报出现偏差的可能原因进行了讨论。结果表明:低涡切变和低层急流的共同影响,为强降水提供了充沛的水汽和有利的动力条件。高空干冷空气叠加在低层暖区之上形成的位势不稳定层结和垂直风切变为强降水的发生提供了不稳定条件。17日20时—19日08时CMA-MESO模式逐12 h暴雨、大暴雨以及暴雨以上量级降水的TS评分均优于ECMWF模式,但2种模式对18日08—20时暖区降水的预报结果均较差。CMA-MESO模式预报的降水区域和实况区域重叠面积的比例均显著高于ECMWF模式,预报形态也与实况更为接近。模式对冷空气强度预报偏弱造成了冷切辐合偏北,对中层湿舌的位置预报偏北,水汽强度预报偏弱,与强降水落区预报偏北相对应,可能是降水预报出现明显偏差的原因。展开更多
文摘利用2020年夏季(6~8月)CMA-MESO模式逐日08:00(北京时,下同)起报的12~36 h逐时降水预报数据和地面—卫星—雷达三源融合逐时降水产品,着眼于小时尺度降水特征,细致评估了CMA-MESO对四川盆地及周边地区的降水预报性能。结果表明,CMA-MESO较好把握了夏季降水的空间分布特征,即小时平均降水量和降水频率的大值区位于四川盆地西部、北部和东部的高海拔山区,而降水强度大值区主要位于山脉迎风坡一侧,但CMA-MESO预报的降水量和频率大值区位置较观测偏南。CMA-MESO合理描述了研究区域内降水量和频率峰值时间位相自西向东逐步滞后的特征,能够把握区域平均的降水量和频率清晨主峰、傍晚次峰的双峰形态以及降水强度的单峰特征,但预报的降水日变化位相超前于观测。CMA-MESO预报的逐时降水量均大于观测,明显的降水量预报正偏差发生于夜间(21:00至次日03:00)和午后至傍晚(14:00~20:00),分别由一般性降水(0.1~10 mm h-1)预报偏差和强降水(≥10 mm h-1)预报偏差主导,其偏差大值区分别位于青藏高原东南缘至四川盆地西部和四川盆地以东、以南地区,模式对热力和动力场的预报偏差结合地形的影响是降水量预报偏差的成因。
基金Supported by the National Key Research and Development Program of China(2018YFF0300103)。
文摘Assimilation of surface observations including 2-m temperature(T_(2m))in numerical weather prediction(NWP)models remains a challenging problem owing to differences between the elevation of model terrain and that of actual observation stations.NWP results can be improved only if surface observations are assimilated appropriately.In this study,a T_(2m)data assimilation scheme that carefully considers misrepresentation of model and station terrain was established by using the three-dimensional variational data assimilation(3DVAR)system of the China Meteorological Administration mesoscale model(CMA-MESO).The corresponding forward observation operator,tangent linear operator,and adjoint operator for the T_(2m)observations under three terrain mismatch treatments were developed.The T_(2m)data were assimilated in the same method as that adopted for temperature sounding data with additional representative errors,when station terrain was 100 m higher than model terrain;otherwise,the T_(2m)data were assimilated by using the surface similarity theory assimilation operator.Furthermore,if station terrain was lower than model terrain,additional representative errors were stipulated and corrected.Test of a rainfall case showed that the observation innovation and analysis residuals both exhibited Gaussian distribution and that the analysis increment was reasonable.Moreover,it was found that on completion of the data assimilation cycle,T_(2m)data assimilation obviously influenced the temperature,wind,and relative humidity fields throughout the troposphere,with the greatest impact evident in the lower layers,and that both the area and the intensity of rainfall were better forecasted,especially for the first 12hours.Long-term continuous experiments for 2–28 February and 5–20 July 2020,further verified that T_(2m)data assimilation reduced deviations not only in T_(2m)but also in 10-m wind forecasts.More importantly,the precipitation equitable threat scores were improved over the two experimental periods.In summary,this study confirmed that the T_(2m)data assimilation scheme that we implemented in the kilometer-scale CMA-MESO 3DVAR system is effective.
