基于深度学习的天气雷达异常数据识别技术  

Indentification of Weather Radar Abnormal Data Based on Deep Learning

作　　者：张林[1,2] 吴蕾[1] 李峰[1] 李雁[1,3] 施丽娟 孙康远[2] Zhang Lin;Wu Lei;Li Feng;Li Yan;Shi Lijuan;Sun Kangyuan(CMA Meteorological Observation Center,Beijing 100081;Key Laboratory of Transportation Meteorology of CMA,Nanjing Joint Institute for Atmospheric Sciences,Nanjing 210041;Chinese Academy of Meteorological Sciences,Beijing 100081)

机构地区：[1]中国气象局气象探测中心,北京100081 [2]南京气象科技创新研究院、中国气象局交通气象重点实验室,南京210041 [3]中国气象科学研究院,北京100081

出　　处：《应用气象学报》2023年第6期694-705,共12页Journal of Applied Meteorological Science

基　　金：国家重点研发计划(2022YFC3090602,2018YFC1506103);南京气象科技创新研究院北极阁开放研究基金(BJG202203);中国气象科学研究院基本科研业务费专项资金(2021Z003)。

摘　　要：天气雷达基数据中因观测设备故障或标定问题而产生的异常数据,直接影响天气雷达数据质量、定量估测降水及天气系统的分析和判断。目前在中国气象局气象探测中心实时业务中,通过人工勘误环节对异常数据进行处理。针对2020—2022年业务中勘误较多的、大面积故障异常和易与降水数据混合的局部电磁干扰或故障的两类异常数据,分别构建和训练R-ResNet和R-LinkNet两种模型,提取雷达硬件故障、电磁干扰等特征,实现异常数据的识别和处理。评估结果表明:两种模型在提取异常数据特征方面均具有很强的学习能力,R-ResNet在分类判识异常数据与正常数据的准确率超过99%,R-LinkNet在分离电磁干扰杂波和降水回波的准确率超过98%。两种模型可用于实时业务中监控和勘误电磁干扰、故障等异常数据,实现异常数据的自动勘误处理。The world's largest weather radar observation network which consists of 236 weather radars is built up in China.The quality control of weather radar data becomes an indispensable part in operation as data grow.In real-time operation of CMA Meterological Observation Center,the abnormal data caused by radar hardware fault or calibration problem usually leads to a bad image,and the problem directly affects the quality of weather radar data,quantitative estimation of precipitation and analysis of the weather system.At present,the abnormal data are processed by artificial corrigendum in real-time operation,so that it does not affect the application of follow-up data.In recent years,artificial intelligence technology has developed rapidly,and deep learning algorithms are used to build convolutional neural network models to extract image features from abnormal data such as radar hardware failures and electromagnetic interference.According to characteristics of the abnormal data,two kinds of abnormal dataset are established.The first kind is labeled data for those samples with whole abnormal picture or precipitation.The second kind is the electromagnetic interference in fixed direction,which is easily mixed with precipitation,and it is used to label the samples for pixels.Based on the convolution pre-training network model(ResNet),the R-ResNet model is built with the first kind of abnormal data.The model evaluation shows that the R-ResNet model achieves more than 99%accuracy in both training and test datasets,and the prediction results of the model in test datasets are all consistent with the label data.For the second kind of abnormal data,based on the image semantic segmentation network model LinkNet,a group of encoders and decoders are extended,and a hop-link structure is added,and ResNet50 is used as the encoder structure.The R-LinkNet model is constructed and the accuracy is over 98%on the training and test datasets,with intersection over union of 83.4%and 83.2%,respectively.Two models can be used to monitor the abno

关 键 词：深度学习 神经网络 异常数据 模型 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程] P412.25[自动化与计算机技术—控制科学与工程]