双月刊

ISSN 1006-9895

CN 11-1768/O4

利用改进的GoogLeNet深度学习模型识别COSMIC-2掩星信号中的反射信号
作者:
作者单位:

国防科技大学气象海洋学院

作者简介:

通讯作者:

基金项目:

国家自然科学基金41475021


Identify the reflected signal in the COSMIC-2 occultation signal using the improved GoogLeNet deep learning model
Author:
Affiliation:

Identify the reflected signal in the COSMIC-2 occultation signal using the improved GoogLeNet deep learning model

Fund Project:

  • 摘要
  • |
  • 图/表
  • |
  • 访问统计
  • |
  • 参考文献
  • |
  • 相似文献
  • |
  • 引证文献
  • |
  • 资源附件
    摘要:

    GPS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(Im-GNet)深度学习模型,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日至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 12小时预报值(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。

    Abstract:

    GPS occultation detection technology, as an advanced atmospheric detection method, 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 by the reflected signals on the earth"s surface. Identifying and separating the reflected signals in the occultation detection signal helps to assimilate the occultation data into the numerical weather prediction system, which has important significance. This paper proposes a deep learning model based on improved GoogLenet (Im-GNet) and applies it to COSMIC-2 occultation detection data to identify reflected signals. This article selects the COSMIC-2 occultation data (conPhs file) from January 1 to 9, 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 Im-GNet model test reached 96.4%, which is significantly higher than the result of support vector machine (SVM) method. This paper 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 hour forcast files (avnPrf file) shows that the quality of the occultation event data with reflection signals is better and the atmospheric information contained is richer.

    参考文献
    相似文献
    引证文献
引用本文
分享
文章指标
  • 点击次数:
  • 下载次数:
  • HTML阅读次数:
  • 引用次数:
历史
  • 收稿日期:2021-06-08
  • 最后修改日期:2022-02-03
  • 录用日期:2022-03-21
  • 在线发布日期: 2022-03-22
  • 出版日期: