基于贝叶斯分类器的多普勒天气雷达海浪回波识别和效果检验

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ICS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。

Identification and validation of sea-wave echoes collected by a Doppler weather radar based on a Bayes classifier

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.