韩颂雨,刘永生,骆阳,杨明,罗昌荣. 2022. 适用S波段多普勒天气雷达的径向速度自动退模糊方法. 气象学报,80(5):791-805. DOI: 10.11676/qxxb2022.059
引用本文: 韩颂雨,刘永生,骆阳,杨明,罗昌荣. 2022. 适用S波段多普勒天气雷达的径向速度自动退模糊方法. 气象学报,80(5):791-805. DOI: 10.11676/qxxb2022.059
Han Songyu, Liu Yongsheng, Luo Yang, Yang Ming, Luo Changrong. 2022. Automatic radial velocity dealiasing algorithm for S-band Doppler weather radar. Acta Meteorologica Sinica, 80(5):791-805. DOI: 10.11676/qxxb2022.059
Citation: Han Songyu, Liu Yongsheng, Luo Yang, Yang Ming, Luo Changrong. 2022. Automatic radial velocity dealiasing algorithm for S-band Doppler weather radar. Acta Meteorologica Sinica, 80(5):791-805. DOI: 10.11676/qxxb2022.059

适用S波段多普勒天气雷达的径向速度自动退模糊方法

Automatic radial velocity dealiasing algorithm for S-band Doppler weather radar

  • 摘要: 径向速度模糊问题限制了雷达速度资料的广泛应用,针对退速度模糊中孤立回波或被距离折叠隔离的回波出现模糊、受杂点干扰影响以及大多算法往往将径向直线作为初始参考等问题,提出了一种新的自动退模糊方法:(1)通过查找0速交界点插值得到两条0速线以进行正、负速度区域的大致分区;正、负分区后,分区域段识别杂点干扰区与非杂点干扰区;对杂点干扰区,逐点判断其是否满足模糊特征条件,对非杂点干扰区,识别模糊边界以圈定模糊区域块进行退模糊,并做遗留点的逐点扫尾退模糊处理。(2)对于未能确定0速线的情况,使用上层记录的0速线信息或搜索符合条件的径向直线0速线。(3)对于仍未能确定0速线的情况,用逐点判断的方法退模糊。利用S波段雷达观测的飑线、冰雹、强台风等事件11个个例3407个速度模糊体扫资料对该算法进行了验证,总体速度退模糊准确率高于98%。利用0速线确定正负分区、识别模糊区域块以及在逐点判断中考虑扩展邻域搜索,有助于孤立回波及被距离折叠隔离回波的退模糊处理,该方法比业务方法更有效, 2018年3月4日冰雹个例的速度退模糊准确率高于业务方法10%。对杂点干扰区使用逐点判断方法可正确退去模糊区,使其免受杂点影响。综合考虑上层0速线信息及图像中有助于确定0速线的相关信息,经严格把关和检验,确保0速线的准确性,有益于退速度模糊处理。

     

    Abstract: Radial velocity ambiguity limits the application of radar velocity data. To address the issues of the ambiguity of isolated echo or echo isolated by distance ambiguity and clutter interference as well as the problem in traditional method that takes radial straight line as initial reference in velocity dealiasing algorithm, a new automated Doppler radar velocity dealiasing algorithm is proposed. (1) Two zero velocity curves are obtained by finding the zero velocity junction point to roughly partition positive and negative velocity regions. After the positive and negative zoning, the clutter interference area and non clutter interference area are identified. For the clutter interference area, whether it meets multiple conditions with ambiguity characteristics is determined point by point. For the non clutter interference area, the ambiguity boundary is determined to delineate the ambiguity area block for dealiasing, which is conducted at the remaining points. (2) If the zero velocity curves cannot be determined, either the zero velocity curves information recorded in the upper layer is used or the qualified radial linear zero velocity line is searched. (3) If the zero speed line still cannot be determined, whether it meets multiple conditions with ambiguity characteristics will then be determined point by point. The algorithm is verified by using 3407 velocity ambiguity volume scan data of 11 cases such as squall lines, hails and strong typhoons observed by S-band radar, and the overall accuracy is higher than 98%. Using the zero velocity curves to determine the positive and negative zones, identifying the ambiguity area blocks and considering the extended neighborhood search in point by point judgment are conducive to the dealiasing processing of isolated echoes and echoes isolated by distance ambiguity. This method is more effective than the operational method. For the hail case that occurred on 4 March 2018, the accuracy is 10% higher than that by the operational method. Using the method of determining whether it meets multiple ambiguity feature conditions point by point for the clutter interference area, the ambiguity points can be successfully removed without being affected by the clutter. Comprehensive consideration of the upper zero velocity line and relevant information in the image that is helpful to determine the zero velocity curves and strict examination and test can ensure the accuracy of the zero velocity curve, which is conducive to the successful processing of velocity dealiasing.

     

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