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[J]. 气象学报, (0):-, doi:[doi]
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
投稿时间:2021-11-26  修订日期:2022-06-06
DOI:
中文关键词:  多普勒天气雷达,速度退模糊,径向速度,质量控制,零速度线
英文关键词:Doppler weather Radar,Velocity dealiasing,Radial velocity,Quality control,Zero velocity line
基金项目:浙江省气象科技计划项目(2021QN20、2020ZD16)
作者单位邮编
韩颂雨 浙江省气象信息网络中心 310000
刘永生 浙江省气象信息网络中心 310000
骆阳 浙江省气象信息网络中心 310000
杨明 浙江省气象信息网络中心 310000
罗昌荣 海峡气象开放实验室 310000
摘要点击次数: 18
全文下载次数: 23
中文摘要:
      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的问题、受杂点干扰影响的问题以及大多算法往往将径向直线作为初始参考的问题,提出了一种新的自动退模糊方法:(1)通过查找零速交界点插值得到两条零速曲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)对于未能确定零速线的情况,使用上层记录的零速线信息或搜索符合条件的径向直线零速线。(3)对于仍未能确定零速线的情况,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11个个例过程3407个速度模糊的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%。利用零速曲线确定正负分区、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,对此该方法比业务方法更有效,如2018年3月4日冰雹个例,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。综合考虑上层零速线信息及图像中有助于确定零速线的相关信息,经严格把关和检验,确保零速曲线的准确性,有益于退速度模糊的成功处理。
英文摘要:
      Radial velocity ambiguity limits the wide application of radar velocity data. Aiming at the ambiguity of isolated echo or echo isolated by distance?ambiguity, clutter interference and taking 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 the positive and negative velocity region; After positive and negative zoning, identify the clutter interference area and non clutter interference area; For the clutter interference area, judge whether it meets multiple conditions with ambiguity characteristics point by point. For the non clutter interference area, identify the ambiguity boundary to delineate the ambiguity area block for dealiasing, and finishing the remaining points dealiasing. (2) If the zero velocity curves cannot be determined, use the zero velocity curves information recorded in the upper layer or search for the qualified radial linear zero velocity line.(3) If the zero speed line is still not determined, judge whether it meets multiple conditions with ambiguity characteristics point by point.The algorithm is verified by using 3407 velocity ambiguity volume scan data of 11 case processes such as squall line, hail and strong typhoon 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, identify the ambiguity area blocks and consider the extended neighborhood search in point by point judgment is 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 on March 4, 2018, the accuracy is 10% higher than the operational method.Using the method of judging 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. Comprehensively consider the information of the upper zero velocity line and the relevant information in the image that is helpful to determine the zero velocity curves, and strictly checking and testing to ensure the accuracy of the zero velocity curve, which is conducive to the successful processing of velocity dealiasing.
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