| 一种根据湿对流区域时空分布调整的BGM扰动改进方案 |
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| 引用本文： | 曹萍萍,闵锦忠,陈耀登.一种根据湿对流区域时空分布调整的BGM扰动改进方案[J].大气科学学报,2018,41(6):850-860. |
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| 作者姓名： | 曹萍萍 闵锦忠 陈耀登 |
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| 作者单位： | 南京信息工程大学 气象灾害教育部重点实验室/气象灾害预报预警与评估协同创新中心, 江苏 南京 210044;四川省气象台, 四川 成都 610072,南京信息工程大学 气象灾害教育部重点实验室/气象灾害预报预警与评估协同创新中心, 江苏 南京 210044,南京信息工程大学 气象灾害教育部重点实验室/气象灾害预报预警与评估协同创新中心, 江苏 南京 210044 |
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| 基金项目： | 国家重点基础研究发展计划（973计划）项目（2013CB430102）；国家自然科学基金资助项目（40975068；41205082）；江苏省高校自然科学重大基础研究项目（11KJA170001）；江苏高校优势学科建设工程资助项目（PAPD） |
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| 摘 要： | 在增长模繁殖法(Breeding of the Growing Mode,BGM)的动态繁殖过程中,尺度化因子的选择极为关键。利用WRF(Weather Research and Forecasting)模式,在分析飑线系统数值模拟误差增长机制的基础上,根据飑线发展过程中湿对流区域误差更容易快速增长的特点,提出了一种根据湿对流区域时空分布调整的BGM初始扰动改进方案。该方案通过在动态繁殖过程中对小扰动的水平结构进行调整,加强湿对流区域扰动,捕获到增长最快方向上的小扰动并将其作为初始扰动。试验结果表明:根据降水量调整的改进方案相比其他方案扰动能量较大,各集合成员之间差异也较大,集合平均预报误差较小;对强降水范围的模拟相对理想,暴雨的降水评分较高;对风廓线及水汽场的预报更接近于实况,较好地改善了集合预报效果。
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| 关 键 词： | 中小尺度 集合预报 BGM 飑线 |
| 收稿时间： | 2013/5/28 0:00:00 |
| 修稿时间： | 2013/9/12 0:00:00 |
An improved breeding of the growing mode based on spatio-temporal distribution of moist convection zone |
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| CAO Pingping,MIN Jinzhong and CHEN Yaodeng.An improved breeding of the growing mode based on spatio-temporal distribution of moist convection zone[J].大气科学学报,2018,41(6):850-860. |
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| Authors: | CAO Pingping MIN Jinzhong and CHEN Yaodeng |
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| Institution: | Key Laboratory of Meteorological Disaster, Ministry of Education(KLME)/Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters(CIC-FEMD), Nanjing University of Information Science & Technology, Nanjing 210044, China;Sichuan Meteorological Observatory, Chengdu 610072, China,Key Laboratory of Meteorological Disaster, Ministry of Education(KLME)/Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters(CIC-FEMD), Nanjing University of Information Science & Technology, Nanjing 210044, China and Key Laboratory of Meteorological Disaster, Ministry of Education(KLME)/Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters(CIC-FEMD), Nanjing University of Information Science & Technology, Nanjing 210044, China |
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| Abstract: | For the breeding of the growing mode (BGM),how to choose the scale-down factor in the process of dynamic propagation is very important because it is directly related to the forecast effect.Using the Weather Research and forecasting (WRF) meso-scale numerical prediction model,on the basis of analyzing the error growth mechanism of various physical quantities during the numerical simulation of the squall line system,this paper finds that almost all the physical quantity errors in the moist convection zone grows faster than in the other places,so it puts forward an improved Breeding of the Growing Mode that obtains an initial perturbation scheme based on spatial and temporal distributions of moist convection zone.The scheme adjusts the horizontal structure of small perturbation in the process of dynamic propagation,which reinforces the disturbance of moist convection zone,and it can capture the small perturbation on the direction of the fastest growing way.After that,the small perturbation can be used as the initial disturbance.Based on the results of multiple experiments with different perturbations,the following conclusions are obtained:Compared to the other schemes,the improved scheme,which makes the initial disturbance based on the amount of precipitation,has larger disturbance energy,each member in the ensemble system performances more differently,and the ensemble mean forecast has smaller error than the other schemes.The horizontal distribution and precipitation intensity of heavy precipitation are simulated by the improved scheme based on the amount of precipitation,showing that the method forecasts the heavy precipitation range well and its rainfall score of rainstorm is higher.Meanwhile,using the improved method,the forecasts of meridional and zonal wind profiles and water vapor field are closer to the observations,which improve the ensemble forecast effects. |
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| Keywords: | meso-and small-scale ensemble forecast breeding of the growing mode squall line |
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