An Improved Tropospheric Tomographic Model Based on Artificial Neural Network

Zhang, Minghao; Zhang, Kefei; Wu, Suqin; Li, Longjiang; Zhu, Dantong; Wan, Moufeng; Sun, Peng; Shi, Jiaqi; Liu, Shangyi; Hu, Andong

doi:10.1109/jstars.2023.3278302

i

An Improved Tropospheric Tomographic Model Based on Artificial Neural Network

2023
By Zhang, Minghao ; Zhang, Kefei ; Wu, Suqin ; ...

Details

Journal Title:

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Personal Author:

Zhang, Minghao ; Zhang, Kefei ; Wu, Suqin ; Li, Longjiang ; Zhu, Dantong ; Wan, Moufeng ; Sun, Peng ; Shi, Jiaqi ; Liu, Shangyi ; Hu, Andong
NOAA Program & Office:

CIRES (Cooperative Institute for Research in Environmental Sciences)
Description:

—Global navigation satellite systems (GNSS) tropo- spheric tomography can be used to build a three-dimensional water vapor ﬁeld. In traditional tomography, the signals crossing from the four sides of the tomographic region are not utilized. To make the best use of these valuable side-crossing signals, an improved tomographic model based on back propagation artiﬁcial neural network (BP-ANN) is proposed. In the new tomographic model, the
Keywords:

Atmospheric Science Computers In Earth Sciences Computers In Earth Sciences Computers In Earth Sciences
Source:

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 16, 4801-4819
DOI:

https://doi.org/10.1109/jstars.2023.3278302
ISSN:

1939-1404 ; 2151-1535
Format:

PDF
Publisher:

Institute of Electrical and Electronics Engineers (IEEE)
Document Type:

Journal Article
License:

https://creativecommons.org/licenses/by/4.0/
Rights Information:

CC BY
Compliance:

Library
Main Document Checksum:

urn:sha256:f7b3ed0c3395eae7115ef970e107fc8f9d78b289ba7ef3a374af2d8a5e8d314b
Download URL:

https://repository.library.noaa.gov/view/noaa/55191/noaa_55191_DS1.pdf
File Type:

[PDF - 7.72 MB ]

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