中国农业气象 ›› 2026, Vol. 47 ›› Issue (4): 530-545.doi: 10.3969/j.issn.1000-6362.2026.04.005

• 农业生物气象栏目 • 上一篇    下一篇

基于模糊逻辑的降水回波、昆虫回波与鸟类回波的识别算法

项晓彤,包云轩,焦圣明,陶丽,张熠玚,曾娟   

  1. 1.南京信息工程大学大气科学学院,南京 210044;2.南京信息工程大学气象灾害教育部重点实验室,南京 210044;3.南京信息工程大学江苏省农业气象重点实验室,南京 210044;4.江苏省气象信息中心,南京 210018;5.农业农村部全国农业技术推广服务中心,北京 100125
  • 收稿日期:2025-03-20 出版日期:2026-04-20 发布日期:2026-04-18
  • 作者简介:项晓彤,E-mail:xxt17851203116@163.com
  • 基金资助:
    国家重点研发计划项目(2022YFD1400400)

Identification Algorithm of Precipitation Echoes, Insect Echoes and Bird Echoes Based on Fuzzy Logic

XIANG Xiao-tong, BAO Yun-xuan, JIAO Sheng-ming, TAO Li, ZHANG Yi-yang, ZENG Juan   

  1. 1. School of Atmospheric Sciences, Nanjing University of Information Science and Technology, Nanjing 210044, China; 2. Key Laboratory of Meteorology Disaster, Ministry of Education, Nanjing University of Information Science and Technology, Nanjing 210044; 3. Jiangsu Key Laboratory of Agricultural Meteorology, Nanjing University of Information Science and Technology, Nanjing 210044; 4. Jiangsu Meteorological Information Center, Nanjing 210018; 5. National Agricultural Technology Extension and Service Center, Ministry of Agriculture and Rural Affairs, Beijing 100125
  • Received:2025-03-20 Online:2026-04-20 Published:2026-04-18

摘要:

天气雷达因其时空分辨率高、探测范围大、组网覆盖广,在短时临近预报与降水量定量估计等气象业务中发挥重要作用。雷达回波包括降水、昆虫和鸟类回波,但当前气象业务中通常将昆虫、鸟类等非气象回波作为噪音剔除,造成信息浪费。本研究基于连云港双偏振天气雷达数据,利用模糊逻辑算法构建降水、昆虫与鸟类回波的识别算法,评估该算法的识别效果,并分析其在降水、昆虫、鸟类三类回波及其混合情况中的识别结果,以期为开展空中生物监测提供依据。结果表明:(1)对模糊逻辑算法的识别结果添加后处理,降水回波与生物回波的识别准确率ACCAccuracy)分别由0.900.88提升为0.950.94。(2)使用模糊逻辑算法将生物回波分类为昆虫回波与鸟类回波的结果,准确率ACC0.96,真阳性率TPRTrue positive rate)为0.94,真阴性率TNRTrue negative rate)为0.98HSS评分(Heidke skill score)达0.92。说明研究构建的雷达回波识别算法可较好地识别降水、昆虫与鸟类回波及其差异。

关键词: 模糊逻辑, 双偏振天气雷达, 回波识别, 降水回波, 昆虫与鸟类回波

Abstract:

Weather radar plays an important role in meteorological services such as nowcast and quantitative precipitation estimation due to its high spatiotemporal resolution, large detection range and wide network coverage. Radar echoes included the precipitation, insect and bird echoes, but non-meteorological echoes such as insect and bird echoes were usually removed as noise in the current meteorological operations, resulting in wasted information. In this paper, the authors constructed the identification algorithm of precipitation, insect and bird echoes based on fuzzy logic algorithm using the dual polarization weather radar data from Lianyungang, evaluated the identification effect of the algorithm, and analyzed the identification results of precipitation echoes, insect echoes, bird echoes as well as their mixed scenarios, aiming to provide a foundation for aerial biological monitoring. The results showed as follows: (1) the accuracy (ACC) of precipitation echoes and biological echoes were improved from 0.90 and 0.88 to 0.95 and 0.94, respectively, after adding post-processing to the identification results of the fuzzy logic algorithm. (2) In the results of classifying biological echoes into insect echoes and bird echoes by fuzzy logic algorithm, the ACC was 0.96, the true positive rate (TPR) was 0.94, the true negative rate (TNR) was 0.98, and the heidke skill score (HSS) achieved 0.92. The radar echo identification algorithm constructed in this paper is able to identify precipitation echoes, insect echoes, bird echoes and their differences very well.

Key words: Fuzzy logic, Dual polarization weather radar, Echoes identification, Precipitation echoes, Insect and bird echoes