鲁中地区分类强对流天气环境参量特征分析

Characteristics of environmental parameters for classified severe convective weather in central area of Shandong Province

  • 摘要: 将山东中部地区16 a暖季(4-9月)106次伴随瞬时风力不低于8级的强对流个例划分为雷暴大风、冰雹雷暴大风和强降水混合型等3种类型,利用常规探空资料和地面观测资料,通过箱须图的形式分别讨论3种类型对应的一系列关键环境参数的分布特征和预报阈值。进一步,又将上述106次个例中的特强对流个例,包括产生25 m/s以上瞬时大风的特强雷暴大风个例、产生不小于20 mm直径冰雹的特强冰雹个例以及50 mm/h或以上强度的特强短时强降水个例提取出来构成一个子集,讨论其关键环境参数分布特征和预报阈值,并与全部对流个例的相应关键环境参数进行比较。最后,对鲁中地区强对流系统的触发机制进行了简要阐述和讨论。结果表明:(1)雷暴大风型、冰雹雷暴大风型和强降水混合型对应的850和500 hPa温差的最低阈值为25℃; 3种类型对应的地面露点最低阈值分别为13、16和24℃; 相应的大气可降水量最低阈值分别为20、24和32 mm; 相应对流有效位能的最低阈值分别为300、900和1300 J/kg; 相应的0-6 km风垂直切变最低阈值分别为12.0、12.5和8.0 m/s。(2)通过地面露点、大气可降水量以及暖云层厚度等关键参数的分布特征可以将上述3种类型的前两种与第3种类型即强降水混合型进行一定程度的区分,但要通过各个关键参数的分布特征区分前两种强对流天气是困难的。(3)对于伴随冰雹的强对流天气,适宜的融化层高度为3.0-3.9 km; (4)特强雷暴大风、特强冰雹和特强短时强降水等3种特强对流类型与全部强对流个例的3种类型相比,其条件不稳定度明显增大,体现为850和500 hPa温差的增大、水汽条件有所加强、对流有效位能明显增大,3种类型特强对流天气对应的对流有效位能最低阈值分别为1000、1100和2000 J/kg; 相应的0-6 km风垂直切变最低阈值分别为16、12和11 m/s,即特强雷暴大风型和特强短时强降水型的风垂直切变阈值明显增大。上述工作构成了山东中部伴随雷暴大风的强对流天气短时预报的一个基础,结合各类强对流天气发生的气候概率,可以通过决策树或模糊逻辑方法制作成适合于地、市气象台的分类强对流天气短时预报系统。

     

    Abstract: In this paper, 106 strong convective cases accompanied with instantaneous winds at or exceeding the scale 8 in the central region of Shandong Province in 16 years are classified into three types:Thunderstorm, hail thunderstorm and mixed type of heavy rainfall. Using conventional sounding data and ground observation data, distributions of several key environmental parameters and thresholds corresponding to the three types are discussed respectively by the form of box and whisker plots. Furthermore, significantly severe convection cases among the 106 cases, including significant thunderstorm and strong wind cases with instantaneous wind speed exceeding 25 m/s, significant hail cases with the diameter of hails equal to or larger than 20 mm, and significant short-term heavy rainfall cases with the intensity about 50 mm/h or above, are extracted to form a subset. Distributions and forecast thresholds for significantly severe convection cases mentioned above are discussed and compared with the corresponding key environmental parameters for the total cases. Finally, the triggering mechanism for the strong convective system is briefly described and discussed. Results suggest that the minimum threshold of temperature difference between 850 and 500 hPa is 25℃ for the development of thunderstorms, hail thunderstorms and mixed type of heavy rainfall, while their corresponding minimum thresholds of ground dew point temperature are 13, 16 and 24℃, the atmospheric precipitation thresholds are 20, 24 and 32 mm, the thresholds of CAPE are 300, 900 and 1300 J/kg, thresholds of 0-6 km wind vector difference are 12, 12.5 and 8 m/s, respectively. The distributions of ground dew point temperature, atmospheric precipitation and warm cloud thickness can be used to distinguish the above mentioned three types of strong convective weather, but it is difficult to distinguish thunderstorms and hail thunderstorms through the distributions of these key parameters. For the strong convective weather associated with hail, the appropriate melting layer height is 3.0-3.9 km. Compared with the three types of all strong convection cases, the conditional instability for significant thunderstorms, significant hail thunderstorms and significant mixed type of heavy rainfall significantly increases, which is reflected in the fact that the temperature difference between 850 and 500 hPa, the water vapor condition, and the CAPE all increase. Corresponding to the above mentioned three types, the minimum thresholds of CAPE are 1000, 1100 and 2000 J/kg, and the 0-6 km wind vector shears are 16, 12 and 11 m/s, respectively. The thresholds of vertical wind shear for significant thunderstorms and significant mixed type of heavy rainfall increase obviously. The above work forms a basis for the short-term forecast of strong convective weather. Combined with the climatological occurrence probability, a short-term forecasting system for classified strong convective weather can be established for regional meteorological stations by means of decision tree or fuzzy logic method.

     

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