双月刊

ISSN 1006-9895

CN 11-1768/O4

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
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南京信息工程大学大气物理学院

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自然科学基金面上基金项目(41975027),国家重点研究发展计划重点专项(2017YFC1501401)


Research on the Statistical Characteristics of Radar Heavy Rainfall Data in Wavelet Domain and Its Relationship with Environmental Parameters
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School of Atmospheric Physics,Nanjing University of Information Science and Technology

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    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。

    Abstract:

    In order to obtain the best estimation of heavy precipitation, a priori model based on the multi-scale statistical characteristics of radar heavy precipitation data is very important. Based on the data of 180 independent precipitation events of Nanjing S-band Doppler weather radar from 2013 to 2016, this paper conducts wavelet decomposition to study the non-Gaussian edge distribution characteristics of the wavelet coefficients in the wavelet domain of the heavy precipitation radar echoes and the fractal characteristics between scales. And based on the prior statistical characteristics of heavy precipitation, a corresponding mathematical model was established. The research results show that for radar echoes with different precipitation structures presenting different shapes, their fractal parameters are not very different, and the directivity is not obvious, the wavelet coefficients of heavy precipitation can be uniformly modeled. Non-Gaussian features within intrascale can be represented by generalized Gaussian distribution, and fractal features between scales can be represented by exponential form. In order to further explain the relationship between the statistical characteristics of the heavy precipitation in the wavelet domain and the physical parameters of precipitation, the relationship between the fractal parameters of wavelet coefficients in the wavelet domain of heavy precipitation and environmental parameters is discussed. It is found that the correlation coefficient between the convective available potential energy and the fractal parameters in the environmental parameters (first-order horizontal direction) is 0.5535, and the correlation coefficient between the precipitation per hour and the fractal parameters (the mean of the second-order wavelet coefficients and fractal parameters in each direction) is 0.3848, while the correlations between other environmental parameters and fractal parameters is lower than 0.28. The statistical characteristics of heavy precipitation in the wavelet domain and the prior information with environmental parameters can be used for parametric modeling of heavy precipitation data. It has important reference value for subsequent applications such as optimal estimation of heavy precipitation, data assimilation, data downscaling, and multi-source data fusion.

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历史
  • 收稿日期:2021-05-08
  • 最后修改日期:2021-09-07
  • 录用日期:2021-11-05
  • 在线发布日期: 2021-12-09
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