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Application and Characteristic Analysis of the Moist Singular Vector in GRAPES-GEPS 被引量:3
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作者 Jing WANG Bin WANG +3 位作者 Juanjuan LIU Yongzhu LIU Jing CHEN Zhenhua HUO 《Advances in Atmospheric Sciences》 SCIE CAS CSCD 2020年第11期1164-1178,共15页
The singular vector(SV)initial perturbation method can capture the fastest-growing initial perturbation in a tangent linear model(TLM).Based on the global tangent linear and adjoint model of GRAPES-GEPS(Global/Regiona... The singular vector(SV)initial perturbation method can capture the fastest-growing initial perturbation in a tangent linear model(TLM).Based on the global tangent linear and adjoint model of GRAPES-GEPS(Global/Regional Assimilation and Prediction System-Global Ensemble Prediction System),some experiments were carried out to analyze the structure of the moist SVs from the perspectives of the energy norm,energy spectrum,and vertical structure.The conclusions are as follows:The evolution of the SVs is synchronous with that of the atmospheric circulation,which is flowdependent.The moist and dry SVs are located in unstable regions at mid-to-high latitudes,but the moist SVs are wider,can contain more small-and medium-scale information,and have more energy than the dry SVs.From the energy spectrum analysis,the energy growth caused by the moist SVs is reflected in the relatively small-scale weather system.In addition,moist SVs can generate perturbations associated with large-scale condensation and precipitation,which is not true for dry SVs.For the ensemble forecasts,the average anomaly correlation coefficient of large-scale circulation is better for the forecast based on moist SVs in the Northern Hemisphere,and the low-level variables forecasted by the moist SVs are also improved,especially in the first 72 h.In addition,the moist SVs respond better to short-term precipitation according to statistical precipitation scores based on 10 cases.The inclusion of the large-scale condensation process in the calculation of SVs can improve the short-term weather prediction effectively. 展开更多
关键词 moist singular vector GRAPES-GEPS adjoint model ensemble prediction
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GRAPES-GEPS全球集合预报系统湿奇异向量的时空尺度敏感性研究 被引量:2
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作者 王静 刘娟娟 +2 位作者 王斌 陈静 刘永柱 《大气科学》 CSCD 北大核心 2021年第4期874-888,共15页
湿奇异向量(Moist Singular Vectors,简称MSVs)是包含了湿物理切线性过程计算得到的奇异向量。研究MSVs对最优化时间间隔(optimization time interval,简称OTI)及模式水平分辨率的敏感性对提高集合预报效果至关重要。本文基于中国气象... 湿奇异向量(Moist Singular Vectors,简称MSVs)是包含了湿物理切线性过程计算得到的奇异向量。研究MSVs对最优化时间间隔(optimization time interval,简称OTI)及模式水平分辨率的敏感性对提高集合预报效果至关重要。本文基于中国气象局数值预报中心自主研发的全球/区域同化和预报系统(Global/Regional Assimilation and Prediction System,简称GRAPES)——全球集合预报系统(Global ensemble prediction system,简称GEPS)业务版本研究了4组不同时空尺度(不同OTI和水平分辨率)下的MSVs,从能量模、能量谱、空间剖面等方面分析热带外MSVs特征,并从等压面变量评分、降水评分、降水概率预报等方面评估不同初值的集合预报效果。结果表明:提高MSVs水平分辨率可使其扰动具有较大的增长率,缩短OTI后MSVs能量向上传播的趋势更明显,并可以在中尺度范围产生较大SVs扰动。不同OTI下初始MSVs相似性较低,结构差异较大。从集合预报的结果来看,OTI为24 h试验的集合扰动能量增长较大,集合离散度在预报的0~96 h有明显提升,特别是2 m温度,且近地面要素的outlier评分也有明显改进。进一步分析发现,提高水平分辨率和缩短OTI的MSVs能够提高降水概率预报,而降水评分显示,同一水平分辨率下,OTI越短评分越好,但是提高MSVs的水平分辨率并不一定会提升小雨到中雨量级的降水评分。 展开更多
关键词 湿奇异向量 最优时间间隔 集合预报 GRAPES-GEPS全球集合预报系统
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