Which Polarimetric Variables Are Important for Weather/No-Weather Discrimination?
Which Polarimetric Variables Are Important for Weather/No-Weather Discrimination?
复制标题
哪些偏振变量对于天气/非天气区分很重要？
DOI：
10.1175/jtech-d-13-00205.1
复制
发表时间：
2015
影响因子：
2.2
通讯作者：
Samantha Berkseth
中科院分区：
文献类型：
--
作者：
V. Lakshmanan;Christopher D. Karstens;John Krause;Kim Elmore;A. Ryzhkov;Samantha Berkseth
摘要
近期，一种雷达数据质量控制算法已经被设计出来，用于区分天气回波和由非气象现象（如生物散射、仪器伪影和地物杂波等，拉克什马南等人）导致的回波，该算法利用距离门及其周围的极化矩值。由于该算法是通过在一个大型参考数据集上优化其权重而创建的，所以可以采用统计方法来检验在区分天气回波和非天气回波的背景下不同变量的重要性。在研究其对从天气雷达数据中识别和剔除非气象伪影能力的影响的变量中，连续排列法将差分反射率（Zdr）的方差、虚拟体扫的反射率结构以及传播路径上差分相位[差分传播相移（Kdp）]的距离导数列为最重要的变量。相同的统计框架可用于研究变量中校准误差的影响……
AbstractRecently, a radar data quality control algorithm has been devised to discriminate between weather echoes and echoes due to nonmeteorological phenomena, such as bioscatter, instrument artifacts, and ground clutter (Lakshmanan et al.), using the values of polarimetric moments at and around a range gate. Because the algorithm was created by optimizing its weights over a large reference dataset, statistical methods can be employed to examine the importance of the different variables in the context of discriminating between weather and no-weather echoes. Among the variables studied for their impact on the ability to identify and censor nonmeteorological artifacts from weather radar data, the method of successive permutations ranks the variance of Zdr, the reflectivity structure of the virtual volume scan, and the range derivative of the differential phase on propagation [PhiDP (Kdp)] as the most important. The same statistical framework can be used to study the impact of calibration errors in variables...