Atmospheric Measurement Techniques
Atmospheric Measurement Techniques
AMT
Articles | Volume 14, issue 12
Articles | Volume 14, issue 12
https://doi.org/10.5194/amt-14-7729-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-14-7729-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 14, issue 12
Research article
 | 
09 Dec 2021
Research article |  | 09 Dec 2021

Inpainting radar missing data regions with deep learning

Andrew Geiss and Joseph C. Hardin

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Latest update: 12 Nov 2024
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Short summary
Radars can suffer from missing or poor-quality data regions for several reasons: beam blockage, instrument failure, and near-ground blind zones, etc. Here, we demonstrate how deep convolutional neural networks can be used for filling in radar-missing data regions and that they can significantly outperform conventional approaches in terms of realism and accuracy.
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