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随机物理倾向扰动方案在西部山地对流尺度集合预报中的研究*
王明欢, 李俊, 熊洁, 赖安伟, 孙玉婷, 许建玉
(中国气象局武汉暴雨研究所 暴雨监测预警湖北省重点实验室)
A Study of Stochastically Perturbed Parametrization Tendencies on West China Mountains Convective-scale Ensemble Forecast
WANG Minghuan, LI Jun, XIONG Jie, LAI Anwei, SUN Yuting, XU Jianyu
(Hubei Key Laboratory for Heavy Rain Monitoring and Warning Research,Institute of Heavy Rain,China Meteorological Administration)
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投稿时间:2020-08-13    修订日期:2021-06-07
中文摘要: 为了研究随机物理倾向扰动(Stochastically Perturbed Parametrization Tendencies,SPPT)方法在复杂地形条件下对流尺度集合预报中的影响,针对SPPT随机扰动场的时间尺度、空间尺度和格点标准差三个参数进行敏感性试验,分析扰动变化规律,探讨其预报效果。结果表明:空间尺度90 km、时间尺度3 h和格点标准差0.525参数构造的SPPT试验随机扰动场结构对西部山地对流尺度集合预报整体效果较好,集合成员可信度更高,对该试验不同层次高空要素(纬向风场、温度场和湿度场)和近地面要素(10 m风和2 m温度)的离散度增长较快,考虑预报误差的离散度/RMSE也好于其他试验。虽然最优配置试验的3 h累积降水的集合平均相对于其他参数试验没有明显在各个量级上都有所提高,但在≥10 mm、≥25 mm和≥50 mm的降水等级的ETS评分接近或者高于控制试验,概率预报技巧较好。综合来看,空间尺度参数的选取比时间尺度对离散度的影响更加明显,增加扰动振幅对离散度的增加也起到积极的作用,同时可以提高不同量级降水的概率预报技巧。
Abstract:Abstract: To investigate the influence of Stochastically Perturbed Parametrization Tendencies (SPPT) on convective Scale Ensemble Prediction under complex topography conditions, sensitivity experiments were conducted on three parameters of sppt random disturbance field, including time scale, spatial scale and grid standard deviation, to explore its prediction effect. The results show that in the 90 km spatial scale of, the 3 h time scale and the 0.525 grid standard deviation in SPPT are best in this case. The spreads of upper-air physical quantities (zonal wind field, temperature field and humidity field) and surface layer physical quantities (10 m wind and 2 m temperature) increase rapidly. The spread/ RMSE considering the prediction error is better than other experiments. Although the ensemble mean of 3 h accumulated precipitation is not significantly improved in all grade compared with other experiments, the ETS scores of precipitation grades ≥10 mm, ≥25 mm and ≥50 mm are close to or higher than those of the control experiment, and the probability prediction skills are better. On the whole, the influence on spread of spatial scale parameter is more obvious than that of time scale, and the increase of perturbation amplitude also plays a positive role in the increase of spread. At the same time, it can improve the probability prediction skills of precipitation of different magnitudes.. Key words: Ensemble forecast, convective scale, uncertainty, Stochastically Perturbed Parameterization Tendencies, SPPT
文章编号:202008130297     中图分类号:P456    文献标志码:
基金项目:国家重点研发计划项目(2018YFC1507200)和政府间国际科技创新合作重点专项(2016YFE0109400)共同资助
Author NameAffiliationAddress
WANG Minghuan Hubei Key Laboratory for Heavy Rain Monitoring and Warning Research,Institute of Heavy Rain,China Meteorological Administration 湖北省武汉市东湖新技术开发区金融港二路6号
LI Jun  湖北省武汉市东湖新技术开发区金融港二路6号
XIONG Jie  
LAI Anwei  
SUN Yuting  
XU Jianyu  
引用文本:
王明欢,李俊,熊洁,赖安伟,孙玉婷,许建玉,0.A Study of Stochastically Perturbed Parametrization Tendencies on West China Mountains Convective-scale Ensemble Forecast[J].Meteor Mon,():-.
WANG Minghuan,LI Jun,XIONG Jie,LAI Anwei,SUN Yuting,XU Jianyu,0.A Study of Stochastically Perturbed Parametrization Tendencies on West China Mountains Convective-scale Ensemble Forecast[J].Meteor Mon,():-.