沈妍琰, 黄兴友, 黄书荣, 沈艳秋, 陈晓颖
南京信息工程大学 大气物理学院, 江苏 南京 210044

摘要:
为了提升雷达数据质量，减少海浪回波对临近预报和数值天气预报模式的雷达数据同化的不利影响，因此需要对海浪回波进行识别和去除。识别算法主要为统计获得先验概率，分析海浪和降水回波特征分布得到似然函数，再经过贝叶斯分类器来达到识别的目的。在本次算法识别过程中65个样本数据试验的临界成功指数I_CS达到了0.692，结果表明利用贝叶斯分类器对海浪回波的识别，具有较好的识别效果，能一定程度降低海浪回波误判为降水回波的错误，提高雷达数据质量。
关键词:海浪回波回波识别贝叶斯分类器似然函数先验概率
DOI：10.11759/hykx20191011001
分类号:P406
基金项目:国家重点研发计划（2018YFC1506102）

Identification and validation of sea-wave echoes collected by a Doppler weather radar based on a Bayes classifier
SHEN Yan-yan, HUANG Xing-you, HUANG Shu-rong, SHEN Yan-qiu, CHEN Xiao-ying
Nanjing University of Information Science and Technology, Nanjing 210044, China
Abstract:
Weather radar data quality is usually degraded due to the presence of sea-wave echoes in coastal areas. To deduce problems caused by non-precipitation sea-wave echoes in nowcasting and numerical weather models while assimilating radar measurements, sea-wave echoes need to be identified and removed. The key of a Bayes classifier for the classification of precipitation and sea-wave echoes is the prior probability and likelihood function based on statistics. An experiment with 65 samples shows that the Critical Successful Index I_cs is 0.692, which implies that the Bayes classifier works well in identifying sea-wave echoes. The classification of sea-wave echoes with the Bayes classifier can also mitigate the chance of being regarded as precipitation echoes.
Key words:sea-wave echoecho identificationBayes classifierlikelihood functionprior probability





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