doi:  10.3878/j.issn.1006-9895.1810.18177
一种基于NWP诊断自适应的北京地区对流性降水临近集合预报新方法

A new method of adaptive convective precipitation ensemble nowcasting Based on NWP diagnosis Over Beijing
摘要点击 92  全文点击 12  投稿时间:2018-06-15  修订日期:2018-09-23
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基金:  :国家重点基础研究发展计划(2013CB430105),国家自然科学(41675059, 41405059, 41375066 and U1333130).Supported by: the National Basic ResearchProgram of China(2013CB430105), the National Natural Science Foundation of China (41675059, 41405059, 41375066 and U1333130).
中文关键词:  RMAPS-IN,对流发展集合概率,模糊逻辑算法,APEN
英文关键词:  RMAPS-IN, ensemble  convection evolution  probability ,fuzzy  logical, APEN
     
作者中文名作者英文名单位
王国荣wang guo rong中国科学院大学;中国科学院大气物理研究所云降水与强风暴重点实验室;北京市气象台;北京市气象台
平凡Ping Fan中国科学院大学
引用:王国荣,平凡.2019.一种基于NWP诊断自适应的北京地区对流性降水临近集合预报新方法[J].大气科学
Citation:wang guo rong,Ping Fan.2019.A new method of adaptive convective precipitation ensemble nowcasting Based on NWP diagnosis Over Beijing[J].Chinese Journal of Atmospheric Sciences (in Chinese)
中文摘要:
      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NWP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实验,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 to 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 NWP diagnosis for Beijing area is developed (APEN). Based on the precipitation extrapolation , APEN use the convective diagnostic factor provided by the RMAPS-IN to calculate the probability of convection evolution (initiation, growth, and dissipation) by a fuzzy logic algorithm, and get a 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 was tested to make precipitation nowcasting with two cases(one is with strong rainfall and the other is with weak rainfall) in Beijing. The results show: the ensemble convective evolution probability which based on the RMAPS-IN multi-diagnosis factors, can reflect the trend of convective systems in both strong and weak weather conditions. Based on the expert experience model ,adjustments of convective precipitation under the three states (convection initiation, growth, and dissipation) can reasonably represent the impact of precipitation by evolution of convective systems. A comparison of precipitation nowcasting by APEN and RMAPS-IN( a 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 the observation. In particular, the precipitation intensity forecast after considering the effects of convective evolution is significantly better than RMAPS-IN. The experimental comparison shows APEN"s advantages and application potential in convective precipitation forecasting.
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