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Based on the GRAPES-MESO hybrid En-3 DVAR(Ensemble three-dimension hybrid data assimilation for Global/Regional Assimilation and Prediction system) constructed by China Meteorological Administration, a 7-day simulation(from 10 July 2015 to 16 July 2015) is conducted for horizontal localization scales. 48 h forecasts have been designed for each test, and seven different horizontal localization scales of 250, 500, 750, 1000, 1250, 1500 and 1750 km are set. The 7-day simulation results show that the optimal horizontal localization scales over the Tibetan Plateau and the plain area are 1500 km and 1000 km, respectively. As a result, based on the GRAPES-MESO hybrid En-3 DVAR, a topography-dependent horizontal localization scale scheme(hereinafter referred to as GRAPES-MESO hybrid En-3 DVAR-TD-HLS) has been constructed. The data assimilation and forecast experiments have been implemented by GRAPES-MESO hybrid En-3 DVAR, 3 DVAR and GRAPES-MESO hybrid En-3 DVAR-TD-HLS, and then the analysis and forecast field of these three systems are compared. The results show that the analysis field and forecast field within 30 h of GRAPES-MESO hybrid En-3 DVAR-TD-HLS are better than those of the other two data assimilation systems. Particularly in the analysis field, the root mean square error(RMSE) of u_wind and v_wind in the entire vertical levels is significantly less than that of the other two systems. The time series of total RMSE indicate, in the 6-30 h forecast range, that the forecast result of En-3 DVAR-TD-HLS is better than that of the other two systems, but the En-3 DVAR and 3 DVAR are equivalent in terms of their forecast skills. The 36-48 h forecasts of three data assimilation systems have similar forecast skill.  相似文献   
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利用WRF v3.6.1模式,采用Thompson云微物理参数化方案对北京奥运会期间的一次降水过程进行了模拟,通过3组数值试验对比分析了高、中、低云凝结核浓度对降水的影响。数值试验结果显示:(1)在云凝结核浓度较低的情况下,云凝结核浓度增加使24 h累积降水量增加,且增加的幅度相对较低;在云凝结浓度较高的情况下,云凝结核浓度增加使24 h累积降水量减少,且减少的幅度相对较高。(2)从地面雨强分布来看,不同的云凝结核浓度对暴雨、大雨、中雨、小雨的影响均体现在降水强度上,对降水位相的影响不显著。(3)云凝结核浓度的变化对低云量的影响与其对降水的影响相一致,故低云量是影响降水的一个重要因素。  相似文献   
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Based on the GRAPES-MESO hybrid En-3DVAR (Ensemble three-dimension hybrid data assimilation for Global/Regional Assimilation and Prediction system) constructed by China Meteorological Administration, a 7-day simulation (from 10 July 2015 to 16 July 2015) is conducted for horizontal localization scales. 48h forecasts have been designed for each test, and seven different horizontal localization scales of 250, 500, 750, 1000, 1250, 1500 and 1750 km are set. The 7-day simulation results show that the optimal horizontal localization scales over the Tibetan Plateau and the plain area are 1500 km and 1000 km, respectively. As a result, based on the GRAPES-MESO hybrid En-3DVAR, a topography-dependent horizontal localization scale scheme (hereinafter referred to as GRAPES-MESO hybrid En-3DVAR-TD-HLS) has been constructed. The data assimilation and forecast experiments have been implemented by GRAPES-MESO hybrid En-3DVAR, 3DVAR and GRAPES-MESO hybrid En-3DVAR-TD-HLS, and then the analysis and forecast field of these three systems are compared. The results show that the analysis field and forecast field within 30h of GRAPES-MESO hybrid En-3DVAR-TD-HLS are better than those of the other two data assimilation systems. Particularly in the analysis field, the root mean square error (RMSE) of u_wind and v_wind in the entire vertical levels is significantly less than that of the other two systems. The time series of total RMSE indicate, in the 6-30h forecast range, that the forecast result of En-3DVAR-TD-HLS is better than that of the other two systems, but the En-3DVAR and 3DVAR are equivalent in terms of their forecast skills. The 36-48h forecasts of three data assimilation systems have similar forecast skill.  相似文献   
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GRAPES区域集合预报模式的初值扰动增长特征   总被引:3,自引:1,他引:3  
基于GRAPES-REPS(Global and Regional Assimilation and Prediction Enhanced System-Regional Ensemble Prediction System)区域集合预报模式和集合变换卡尔曼滤波(Ensemble Transform Kalman Filter,ETKF)初值扰动方法,对2015年6月1~15日10 km与15 km水平分辨率分别进行集合预报试验,通过分析ETKF初值扰动分量、初值扰动方差准确率、动能谱、扰动能量演变、日变化及集合离散度、均方根误差等特征,揭示GRAPES-REPS区域集合预报ETKF初值扰动结构及增长特征。结果表明:(1)ETKF初值扰动方案产生的扰动能够保持所有正交、不相关方向的误差方差,且ETKF分量α参数值及放大因子具有较好的稳定性。(2)ETKF初值扰动方法生成的扰动场以大尺度扰动为主,扰动结构及能量具有随流型依赖特征,低层以内能扰动为主,高层以动能扰动为主,且集合扰动可以有效捕捉预报误差的结构。(3)GRAPES区域集合预报初值扰动总能量和集合离散度随预报时效的延长均呈发展趋势,但离散度增长率小于均方根误差增长率,即集合预报总体存在集合离散度不足的问题。(4)水平分辨率提高可以增加中高层大尺度扰动波谱能量,明显改进等压面及近地面风场及温度场的集合预报效果。值得指出的是,GRAPES-REPS区域集合预报低层内能扰动能量存在明显的日变化特征,特别是青藏高原地区更加显著,需要进一步研究青藏高原初值扰动结构的合理性。  相似文献   
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西南低涡是形成于青藏高原东侧的特殊天气系统,国内学者目前对于西南低涡的识别没有统一的标准。通过分析西南低涡的主要特征,结合高度场、涡度场、风场,设计了一种适应于西南低涡的HVW识别方法,将其应用于2014年6—8月GRAPES-MESO高分辨率格点分析资料,对比与西南低涡天气图实况的差异。通过对西南低涡的识别、低涡生成和消亡时间、低涡中心位置以及低涡中心强度这几方面的具体分析,得到以下几点结论:1)HVW识别方法能够有效识别出高精度格点资料中的西南低涡过程,与格点实况的吻合率达到87.5%;对于天气图和格点资料都能够再现的西南低涡个例,HVW识别方法的准确度能够达到90.9%,说明HVW识别方法能够有效捕捉西南低涡。2)以天气图实况资料为西南低涡生命时长检验标准,HVW识别方法能够合理分析低涡的生成和消亡时间。3)对西南低涡中心位置偏差进行分析发现,HVW识别的西南低涡中心位置不仅位于西南低涡气压低值附近,更位于风场辐合中心。4)对西南低涡中心强度的评估发现,格点实况与HVW识别方法分析的西南低涡强度差异几乎可以忽略,充分说明了HVW识别方法包含了格点实况的高度场信息,也说明该识别方法的西南低涡中心强度可以用来代替格点实况结果。通过对2014年6—8月西南低涡过程的具体分析,验证了HVW逐步循环定位方法的可行性、合理性以及准确性。  相似文献   
6.
2019年,数值预报中心开发了以GRAPES全球模式为驱动场,集合变换卡尔曼滤波为初值扰动方法,随机物理过程倾向项为模式扰动方法的10 km水平分辨率GRAPES-REPS V3.0区域集合预报模式,并投入业务运行。基于该模式,作者开展了2019年7~9月夏季降水不确定性的集合预报实时试验,并从统计检验和个例分析角度,与GRAPES-REPS V2.0和ECMWF全球集合预报模式进行对比,由此对GRAPES-REPS V3.0区域集合预报模式的降水预报能力给予客观评价,并分析了引起中尺度强降水预报不确定性的物理机制,研究结论可为诊断集合预报模式及改进集合预报方法提供依据。结果表明:(1)GRAPES-REPS V3.0区域集合预报系统的降水ETS评分在所有预报时效和量级内均优于GRAPES-REPS V2.0区域集合预报模式,降水成员具有明显等同性,且概率预报技巧FSS评分较高,GRAPES-REPS V3.0区域集合预报模式降水预报效果全面优于GRAPES-REPS V2.0区域集合预报模式。(2)GRAPES-REPS V3.0区域集合预报的集合平均降水BIAS评分及小雨和暴雨ETS评分均明显优于ECMWF全球集合预报系统,降水概率预报与ECMWF降水概率具有一定可比性。(3)个例分析结果表明,不同集合预报模式通过刻画中尺度特征物理量不确定性来捕捉降水预报不确定性,初始时刻,GRAPES-REPS V3.0区域集合预报模式和ECMWF全球集合预报模式环流形势分布较为相似,随预报时效演变,GRAPES-REPS V3.0区域集合预报模式对中尺度动力、热力场捕捉更为准确,相应地对降水落区与量级预报较好,概率预报技巧较优。(4)与ECMWF全球集合预报模式相比,GRAPES区域集合预报模式集合成员能很好地预报降水发生、发展、消亡整个过程,故GRAPES-REPS V3.0区域集合预报系统对中国汛期降水具有较强的预报能力。  相似文献   
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