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雷达强降水数据小波域统计特征及其与环境参数的关系研究

杨春生 寇蕾蕾 蒋银丰 陈垚 毛赢 王振会

杨春生, 寇蕾蕾, 蒋银丰, 等. 2022. 雷达强降水数据小波域统计特征及其与环境参数的关系研究[J]. 大气科学, 46(6): 1425−1436 doi: 10.3878/j.issn.1006-9895.2109.21080
引用本文: 杨春生, 寇蕾蕾, 蒋银丰, 等. 2022. 雷达强降水数据小波域统计特征及其与环境参数的关系研究[J]. 大气科学, 46(6): 1425−1436 doi: 10.3878/j.issn.1006-9895.2109.21080
YANG Chunsheng, KOU Leilei, JIANG Yinfeng, et al. 2022. Statistical Characteristics of Radar Heavy Precipitation Data in the Wavelet Domain and Its Relationship with Environmental Parameters [J]. Chinese Journal of Atmospheric Sciences (in Chinese), 46(6): 1425−1436 doi: 10.3878/j.issn.1006-9895.2109.21080
Citation: YANG Chunsheng, KOU Leilei, JIANG Yinfeng, et al. 2022. Statistical Characteristics of Radar Heavy Precipitation Data in the Wavelet Domain and Its Relationship with Environmental Parameters [J]. Chinese Journal of Atmospheric Sciences (in Chinese), 46(6): 1425−1436 doi: 10.3878/j.issn.1006-9895.2109.21080

雷达强降水数据小波域统计特征及其与环境参数的关系研究

doi: 10.3878/j.issn.1006-9895.2109.21080
基金项目: 国家自然科学基金项目41975027,国家重点研究发展计划重点专项2017YFC1501401
详细信息
    作者简介:

    杨春生,男,1995年出生,硕士研究生,主要从事雷达数据处理与应用研究。E-mail: ychun95@163.com

    通讯作者:

    寇蕾蕾,E-mail: cassie320@163.com

  • 中图分类号: P412

Statistical Characteristics of Radar Heavy Precipitation Data in the Wavelet Domain and Its Relationship with Environmental Parameters

Funds: National Natural Science Foundation of China (Grant 41975027),Key Special Projects of the National Key Research and Development Program of China (Grant 2017YFC1501401)
  • 摘要: 为了得到最优的强降水估计,基于雷达强降水数据多尺度统计特性建立的先验模型显得非常重要。本文基于南京市S波段多普勒天气雷达2013~2016年共180次独立降水事件数据进行小波分解,研究强降水雷达回波数据小波域小波系数尺度内非高斯边缘分布特征以及尺度间分形特征,并基于强降水的先验统计特征建立相应的数学模型。研究结果表明:对于不同降水结构呈现不同形态的雷达回波来说,它们的分形参数差别并不大,方向性不明显,可对强降水小波系数统一建模,其尺度内的非高斯特征可用广义高斯分布表示,尺度间的分形特征可用指数形式表示。为进一步说明强降水小波域统计特征与降水物理参数的关系,讨论了强降水小波域小波系数分形参数与环境参数的关系,发现环境参数中的对流有效位能与分形参数(一阶水平向)相关系数为0.5535、每小时降水量与分形参数(二阶各方向小波系数分形参数的平均)相关系数为0.3848,而其它环境参数与分形参数相关系数低于0.28。强降水小波域统计特征及其与环境参数的先验信息可用于强降水数据的参数化建模,并对后续的强降水最优估计、数据同化、数据降尺度、多源数据融合等应用具有重要的参考价值。
  • 图  1  2015年6月30日00时16分(协调世界时,下同)(a)南京降水个例回波强度、(b)一级分解得到的水平子带小波系数以及(c,d)标准偏差归一化后水平子带小波系数的概率统计分布 [P,lg (P)]。雷达强降水反射率因子图像的小波域小波系数概率分布(Hist)比常规高斯分布(Gaussian)两边的值更多/更大,图(d)中的实线是拟合的广义高斯分布(Fit GG)

    Figure  1.  (a) Radar reflectivity image of the precipitation case in Nanjing at 0016 UTC June 30, 2015, (b) wavelet coefficients for horizontal sub-band of the reflectivity image in (a), and (c, d) the statistical probability distributions of the horizontal sub-band wavelet coefficients normalized by the standard deviation [P, lg (P)] at one level of decomposition. The wavelet coefficient probability distribution (Hist) in the wavelet domain of the radar heavy precipitation reflectance image has a heavier tail than the Gaussian distribution (Gaussian). The solid line in (d) is the fitted generalized Gaussian distribution

    图  2  强降水回波场分解得到的(a)水平向、(b)垂直向和(c)对角向子带小波系数重尾参数α分布直方图

    Figure  2.  Heavy-tailed parameter α distribution histogram of (a) horizontal, (b) vertical, and (c) diagonal sub-band wavelet coefficients obtained from the decomposition of the heavy precipitation field

    图  3  (a)强对流与(b)弱对流一级分解后水平子带小波系数标准偏差归一化的概率统计分布(P对比。雷达强降水反射率因子图像的小波域小波系数概率分布(Hist)比常规高斯分布(Gaussian)两边的值更多/更大,实线是拟合的广义高斯分布(Fit GG)

    Figure  3.  Comparison of the statistical probability distributions (P) of the horizontal sub-band coefficients normalized by the standard deviation after first-order decomposition of (a) strong convection and (b) weak convection. The wavelet coefficient probability distribution (Hist) in the wavelet domain of the radar heavy precipitation reflectance image has a heavier tail than the Gaussian distribution (Gaussian). The solid line is the fitted generalized Gaussian distribution (Fit GG)

    图  4  (a)单体状(2014年7月15日20时15分)和(b)块状(2014年7月24日13时01分)强对流降水回波场,水平向子带小波系数(c)一阶矩(一阶矩改成绝对值的均值)和(d)二阶矩(二阶矩改成绝对值的方差)随尺度的变化关系。图(c)和(d)中20140715、20140724分别表示2014年7月15日20时15分时刻和2014年7月24日13时01分时刻

    Figure  4.  Heavy convective precipitation field of (a) monolithic (2015 UTC July 15, 2014) and (b) massive (1301 UTC July 24, 2014) echo, and the (c) average and (d) variance of the absolute value of the wavelet coefficient in the horizontal sub-band with scale. 20140715 and 20140724 respectively represent the time at 2015 UTC July 15, 2014 and at 1301 UTC July 24, 2014 in (c) and (d)

    图  5  2015年6月29日19时21分(a)南京降水个例回波强度,(b,c)此次强降水过程分形参数(一阶矩$ {\tau }_{1} $、二阶矩$ {\tau }_{2} $)与重尾性参数α的时序变化

    Figure  5.  (a) Radar reflectivity image of the precipitation case in Nanjing at 1921 UTC June 29, 2015;sequence diagram of fractal parameters of (b) first moment $ {\tau }_{1} $ and (c) second moment $ {\tau }_{2} $ and heavy-tailed parameters α in the process of heavy precipitation

    图  6  (a)2015 年 8 月 7 日 08 时 54 分南京降水个例回波强度,(b)此次强对流过程分形参数、(c)对流有效未能、(d)K指数、(f)对流抑制能、(g)总指数、(h)风速的时序变化

    Figure  6.  (a) Radar reflectivity image of the precipitation case in Nanjing at 0854 UTC August 7, 2015, and (b) the sequence diagrams of fractal parameter, (c) CAPE, (d) K index, (f) CIN, (g) TT and (h) WS (wind speed)

    图  7  强对流降水小波域分形参数(左栏:均值,右栏:方差)与环境参数的散点图

    Figure  7.  Scatter plot of heavy convective precipitation wavelet domain fractal parameters (left: mean, right: variance) and (a–h) environmental parameters

    表  1  不同类型雷达回波其分形参数值的范围

    Table  1.   The range of fractal parameters for different types of radar echoes

    $ {\tau }_{1} $$ {\tau }_{2} $
    HVDHVD
    单体状1.10~1.301.10~1.301.09~1.272.10~2.352.10~2.332.08~2.30
    块状1.25~1.351.25~1.351.23~1.352.25~2.422.24~2.402.22~2.35
    离散状1.08~1.271.09~1.291.03~1.252.05~2.302.08~2.322.07~2.30
    线状1.15~1.371.15~1.371.12~1.322.18~2.482.18~2.482.17~2.46
    注:H:水平向,V:垂直向,D:对角向
    下载: 导出CSV

    表  2  各方向小波系数分形参数与各环境参数的相关系数表

    Table  2.   Correlation coefficients of wavelet coefficient fractal parameters in various directions and various environmental parameters

    相关系数
    $ {\tau }_{1} $$ {\tau }_{2} $
    HVD$ {{\bar\tau }_{1}} $HVD${ {\bar\tau }_{2} }$
    CAPE0.55350.42910.39640.49900.44370.34230.23950.4115
    PRE0.31360.25760.33880.33360.34660.28430.29730.3848
    CIN0.20130.24430.15530.22020.14840.26670.03740.1790
    WS−0.0499−0.02470.0441−0.0079−0.0591−0.03960.13690.0338
    K0.1359−0.0033−0.10460.00040.0548−0.0262−0.1302−0.0594
    TT0.22210.2121−0.05600.13070.21710.2602−0.19300.0820
    注:CAPE:对流有效位能,PRE:Total precipitation,CIN:对流抑制能,WS:地表10 m高度风速,K:K指数,TT:总指数
    下载: 导出CSV
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出版历程
  • 收稿日期:  2021-05-08
  • 录用日期:  2021-11-05
  • 网络出版日期:  2021-12-09
  • 刊出日期:  2022-11-24

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