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一种基于数值模式诊断自适应的北京地区对流性降水临近集合预报新方法
作者:
作者单位:

1.中国科学院大气物理研究所云降水与强风暴重点实验室,北京100029;2.中国科学院大学,北京100049;3.北京市气象台,北京100089

作者简介:

王国荣,男,1981年出生,博士研究生,主要从事短时天气预报技术研究。E-mail: wguor@sina.com

通讯作者:

平凡,E-mail: pingf@mail.iap.ac.cn

基金项目:

国家重点基础研究发展计划项目2013CB430105,国家自然科学基金项目41675059、41405059、41375066、U1333130


A New Method of Adaptive Convective Precipitation Ensemble Nowcasting Based on the Numerical Model Diagnosis Over Beijing
Author:
Affiliation:

1.Laboratory of Cloud-Precipitation Physics and Severe Storms (LACS), Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029;2.University of Chinese Academy of Sciences, Beijing 100049;3.Beijing Meteorological Observatory, Beijing 100089

Fund Project:

National Basic Research Program of China Grant 2013CB430105;National Natural Science Foundation of China Grants 41675059 41405059 41375066 U1333130National Basic Research Program of China (Grant 2013CB430105), National Natural Science Foundation of China (Grants 41675059, 41405059, 41375066, U1333130)

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    摘要:

    局地触发及组织化发展中尺度系统的生消演变是影响对流性降水临近预报的核心和关键。本文结合雷达外推预报、专家系统以及快速循环更新的高分辨数值模式系统,发展和构造了一种适合北京地区的基于数值模式预报诊断自适应的对流性降水临近集合预报新方法(APEN)。APEN基于降水外推预报结果,采用模糊逻辑算法,利用北京市气象局快速循环更新同化系统(RMAPS-IN)提供的对流诊断因子,计算对流系统发展演变(新生、增加和减弱)概率;在此基础上,扰动诊断因子阈值和权重,形成对流发展的集合概率预报;最后综合专家经验,根据对流集合概率,在降水外推预报基础上进行对流性降水调整。应用APEN,针对北京两次强弱降水过程,进行了降水的临近预报试验,结果表明:基于RMAPS-IN多种诊断因子的对流发展集合概率在强弱两种天气背景下,都能较好的反映对流系统在临近时段的发展趋势;基于专家经验模型的三种对流发展状态(对流新生、增加和减弱)下的降水调整,能合理的表征对流系统发展演变对降水的影响。APEN降水预报和RMAPS-IN的业务预报的对比显示:无论是系统性对流过程还是局地激发对流过程,APEN预报的降水落区和强度都更接近于实况,尤其是考虑对流发展演变影响的降水强度预报明显优于RMAPS-IN,APEN在北京地区对流性降水的临近预报中有明显的优势和应用潜力。

    Abstract:

    The locally triggered or organizational development of mesoscale convective systems is the core and key for the accuracy of convective precipitation nowcasting. In this paper, combined with radar extrapolation prediction technology, expert system, and high-resolution numerical model system, a new adaptive convective precipitation ensemble nowcasting method combined with diagnosis from numerical weather prediction mode for Beijing area is developed Based on the precipitation extrapolation, APEN (adaptive precipitation ensemble nowcasting) uses convective diagnostic factors provided by the RMAPS-IN (Rapid-refresh Multi-scale Analysis and Prediction System — Integration) to calculate the probability of convection evolution (initiation, growth, and dissipation) by a fuzzy logic algorithm, and get an ensemble probability by disturbing diagnosis factor thresholds and weights. Then, based on the expert experience, the adjustment of convective precipitation based on the extrapolation of precipitation is carried out. APEN has been tested to make precipitation nowcasting with two cases (one is with heavy rainfall and the other is with weak rainfall) in Beijing. The results show that the ensemble convective evolution probability, which is based on the RMAPS-IN multi-diagnosis factors, can reflect the trend of the convective system in both strong and weak weather conditions. Based on the expert experience model, adjustments of convective precipitation under three states (convection initiation, growth, and dissipation) can reasonably represent the impact of precipitation by evolution of convective systems. Comparison of precipitation nowcasting by APEN and RMAPS-IN (an operational system of Beijing Meteorological Bureau) shows that, regardless of whether the convective system is stimulated by systemic convection activity or locally triggered, the precipitation area and precipitation intensity predicted by APEN are closer to observations. In particular, the precipitation intensity forecast after considering the effect of convective evolution is much better than RMAPS-IN. The experimental comparison demonstrates APEN’s advantages and application potential in convective precipitation forecasting.

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引用本文

王国荣,平凡,翟亮.一种基于数值模式诊断自适应的北京地区对流性降水临近集合预报新方法.大气科学,2019,43(4):895~914 WANG Guorong,PING Fan,and ZHAI Liang.A New Method of Adaptive Convective Precipitation Ensemble Nowcasting Based on the Numerical Model Diagnosis Over Beijing.Chinese Journal of Atmospheric Sciences (in Chinese),2019,43(4):895~914

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  • 收稿日期:2018-06-15
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  • 在线发布日期: 2019-08-08
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