摘要
GPS(Global Positioning System)掩星探测技术作为一种先进的大气探测手段,已广泛用于数值天气预报、气候和空间天气研究。掩星探测存在的问题之一是容易受到地球表面反射信号的干扰,识别和分离掩星探测信号中的反射信号有助于将掩星数据同化到数值天气预报系统中去,具有重要意义。本文提出一种基于改进的GoogLeNet深度学习模型(Im-GNet),并应用于COSMIC-2掩星探测数据来识别反射信号。本文选择了2020年1月1~9日的COSMIC-2掩星数据(conPhs文件),进行质量控制后,利用无线电全息方法得到掩星信号的无线电全息功率谱密度图像,然后训练得到Im-GNet深度学习模型,Im-GNet模型测试的准确率达到了96.4%,显著高于支持向量机(SVM)方法的结果。本文还分析了反射信号对掩星数据的影响,掩星事件的地理分布以及掩星反演数据(atmPrf文件)与NCEP再分析资料的12 h预报值(avnPrf文件)的折射率比较表明:有反射信号的掩星事件数据质量更好,所包含的大气信息更丰富。
As an advanced atmospheric detection method,GPS(Global Positioning System)occultation detection technology has been widely used in numerical weather forecasting,climate,and space weather research.One of the problems in occultation detection is that it is easily interfered with by the reflected signals on the surface of the earth.Identifying and separating the reflected signals in the occultation detection signal helps assimilate the occultation data into the numerical weather prediction system,which has considerable importance.This study proposes a deep learning model based on improved GoogLenet(Im-GNet)model and applies it to COSMIC-2 occultation detection data to identify reflected signals.This study selects the COSMIC-2 occultation data(conPhs file)from 1 January to 9 January 2020.After quality control,the radio holography method is used to obtain the spatial spectrum image of the occultation signal,and the Im-GNet deep learning model is trained.The accuracy rate of the Im-GNet model test reached 96.4%,which is significantly higher than the result of the support vector machine method.This study also analyzes the impact of reflected signals on occultation data.The geographic distribution of occultation events and the refractivity comparison between the occultation inversion data(atmPrf file)and the NCEP(National Centers for Environmental Prediction)12-h forecast files(avnPrf file)shows that the quality of the occultation event data with reflection signals is better,and the atmospheric information contained is richer.
作者
罗文杰
项杰
杜华栋
Wenjie LUO;Jie XIANG;Huadong DU(College of Meteorology and oceanography,National University of Defense Technology,Changsha 410008)
出处
《大气科学》
CSCD
北大核心
2023年第3期631-641,共11页
Chinese Journal of Atmospheric Sciences
基金
国家自然科学基金项目41475021。
