利用广义回归神经网络融合天顶对流层湿延迟

黎峻宇, 李浩杰, 姚宜斌, 刘立龙, 张豹, 黄良珂

黎峻宇, 李浩杰, 姚宜斌, 刘立龙, 张豹, 黄良珂. 利用广义回归神经网络融合天顶对流层湿延迟[J]. 武汉大学学报 ( 信息科学版). DOI: 10.13203/j.whugis20220193
引用本文: 黎峻宇, 李浩杰, 姚宜斌, 刘立龙, 张豹, 黄良珂. 利用广义回归神经网络融合天顶对流层湿延迟[J]. 武汉大学学报 ( 信息科学版). DOI: 10.13203/j.whugis20220193
LI Junyu, LI Haojie, YAO Yibin, LIU Lilong, ZHANG Bao, HUANG Liangke. Zenith Wet Delay Fusion Based on A Generalized Regression Neural Network[J]. Geomatics and Information Science of Wuhan University. DOI: 10.13203/j.whugis20220193
Citation: LI Junyu, LI Haojie, YAO Yibin, LIU Lilong, ZHANG Bao, HUANG Liangke. Zenith Wet Delay Fusion Based on A Generalized Regression Neural Network[J]. Geomatics and Information Science of Wuhan University. DOI: 10.13203/j.whugis20220193

利用广义回归神经网络融合天顶对流层湿延迟

基金项目: 

广西自然科学基金(2020GXNSFBA297145);国家自然科学基金(42064002)

详细信息
    作者简介:

    黎峻宇,博士,讲师,主要从事GNSS近地空间环境监测的研究。yl_lijunyu@163.com

  • 中图分类号: P228

Zenith Wet Delay Fusion Based on A Generalized Regression Neural Network

Funds: 

the Guangxi Natural Science Foundation of China (2020GXNSFBA297145)

  • Abstract:

      Objectives:   Accurate modeling of zenith wet delay (ZWD) is beneficial to improving the accuracy of GNSS navigation and positioning and GNSS meteorological applications. There are many data sources for ZWD modeling. However, there are problems such as multi-source heterogeneity, unequal precision, and even severe system deviation, making it challenging to utilize multi-source ZWD data comprehensively.   Methods:   To address this issue, a generalized regression neural network (GRNN) with strong nonlinear approximation capability and the high-quality radiosonde (RS) ZWD is used to modify the relatively low-quality GRAPES_MESO (GRAPES) ZWD and ERA5 ZWD. And then, the modified GRAPES/ERA5 ZWD and RS ZWD are combined to realize the unbiased fusion of multi-source ZWD, and 1032 ZWD fusion images are obtained.   Results:   Validated by the RS ZWD, there are different degrees of systematic biases between GRAPES/ERA5 ZWD and RS ZWD in 2016-2017. After modification, the overall Bias of GRAPES and ERA5 ZWD are close to 0, and the RMS is decreased by 22.6% and 16.0%, respectively. The spatiotemporal variations of GRAPES/ERA5 ZWD Bias, STD, and RMS are better attenuated by modification.   Conclusions:   The fused ZWD has high accuracy and stability and can provide a reliable ZWD data source for GNSS applications.

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出版历程
  • 收稿日期:  2022-05-30
  • 网络出版日期:  2022-09-19

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