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.
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.
the Creative Commons Attribution 4.0 License.
Research article
| 
09 Dec 2021
Research article |
| 09 Dec 2021
| 09 Dec 2021
Inpainting radar missing data regions with deep learning
Pacific Northwest National Laboratory, Atmospheric Sciences and Global Change Division, Richland, WA, USA
Joseph C. Hardin
Pacific Northwest National Laboratory, Atmospheric Sciences and Global Change Division, Richland, WA, USA
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Cited
21 citations as recorded by crossref.
- CIDM: A comprehensive inpainting diffusion model for missing weather radar data with knowledge guidance W. Zhang et al. https://doi.org/10.1016/j.isprsjprs.2025.02.001
- Radar Observation Gap-Filling Technology Enhanced by Satellite Imager Measurements Z. Ding et al. https://doi.org/10.3390/rs18081205
- Higher-resolution data improves deep learning-based precipitation nowcasting A. Gong et al. https://doi.org/10.1016/j.atmosres.2025.108010
- Multi-resolution reconstruction of longitudinal streambed footprints using embedded sparse convolutional autoencoders Y. Yang et al. https://doi.org/10.1016/j.jhydrol.2025.132852
- Attention-driven and multi-scale feature integrated approach for earth surface temperature data reconstruction M. Zhang et al. https://doi.org/10.5194/gmd-19-73-2026
- Infilling of missing rainfall radar data with a memory-assisted deep learning approach J. Meuer et al. https://doi.org/10.5194/hess-29-3687-2025
- Super-resolution of three-dimensional temperature and velocity for building-resolving urban micrometeorology using physics-guided convolutional neural networks with image inpainting techniques Y. Yasuda et al. https://doi.org/10.1016/j.buildenv.2023.110613
- Deep Learning for Velocity and Range Estimation of Drones in Urban Occlusion Environments S. Xu et al. https://doi.org/10.1109/TNSE.2026.3658147
- Why we need to focus on developing ethical, responsible, and trustworthy artificial intelligence approaches for environmental science A. McGovern et al. https://doi.org/10.1017/eds.2022.5
- Artificial intelligence reveals past climate extremes by reconstructing historical records É. Plésiat et al. https://doi.org/10.1038/s41467-024-53464-2
- Faster chemical mapping assisted by computer vision: insights from glass and ice core samples P. Larkman et al. https://doi.org/10.1039/D5AN00325C
- Classifying thermodynamic cloud phase using machine learning models L. Goldberger et al. https://doi.org/10.5194/amt-18-5393-2025
- Mass Estimation and Protection of Network Security Using Inception‐LSTM and Bayesian Attack Behavior Correlation Y. Lei https://doi.org/10.1002/spy2.70059
- High-Resolution Precipitation Modeling in Complex Terrains Using Hybrid Interpolation Techniques: Incorporating Physiographic and MODIS Cloud Cover Influences K. Alsafadi et al. https://doi.org/10.3390/rs15092435
- Inpainting Radar Missing Data via Denoising Iterative Restoration W. Zhang et al. https://doi.org/10.1109/JSTARS.2024.3400407
- Rad3DGFM: A Deep Learning Network for Completing Data Gaps in 3D Radar Reflectivity Mosaics X. Wang et al. https://doi.org/10.1007/s00376-026-5840-z
- A CBAM-integrated deep convolutional neural network for weather radar echo correction to overcome terrain occlusion challenge H. Wang et al. https://doi.org/10.1016/j.asr.2026.01.098
- Improving the Completion of Weather Radar Missing Data with Deep Learning A. Gong et al. https://doi.org/10.3390/rs15184568
- Research on deep learning-based missing echo restoration method for weather radar mosaic data H. Guo et al. https://doi.org/10.5194/amt-19-6341-2026
- A Radar Data-Driven AI Approach for Rainfall Nowcasting: Towards Flood Preparedness in Urban Regions S. Dandekar et al. https://doi.org/10.1007/s12524-025-02143-w
- Restoration of Automotive Radar Signals Under Mutual Interference by Digital In-Painting H. Lee et al. https://doi.org/10.1109/TIM.2024.3427748
21 citations as recorded by crossref.
- CIDM: A comprehensive inpainting diffusion model for missing weather radar data with knowledge guidance W. Zhang et al. https://doi.org/10.1016/j.isprsjprs.2025.02.001
- Radar Observation Gap-Filling Technology Enhanced by Satellite Imager Measurements Z. Ding et al. https://doi.org/10.3390/rs18081205
- Higher-resolution data improves deep learning-based precipitation nowcasting A. Gong et al. https://doi.org/10.1016/j.atmosres.2025.108010
- Multi-resolution reconstruction of longitudinal streambed footprints using embedded sparse convolutional autoencoders Y. Yang et al. https://doi.org/10.1016/j.jhydrol.2025.132852
- Attention-driven and multi-scale feature integrated approach for earth surface temperature data reconstruction M. Zhang et al. https://doi.org/10.5194/gmd-19-73-2026
- Infilling of missing rainfall radar data with a memory-assisted deep learning approach J. Meuer et al. https://doi.org/10.5194/hess-29-3687-2025
- Super-resolution of three-dimensional temperature and velocity for building-resolving urban micrometeorology using physics-guided convolutional neural networks with image inpainting techniques Y. Yasuda et al. https://doi.org/10.1016/j.buildenv.2023.110613
- Deep Learning for Velocity and Range Estimation of Drones in Urban Occlusion Environments S. Xu et al. https://doi.org/10.1109/TNSE.2026.3658147
- Why we need to focus on developing ethical, responsible, and trustworthy artificial intelligence approaches for environmental science A. McGovern et al. https://doi.org/10.1017/eds.2022.5
- Artificial intelligence reveals past climate extremes by reconstructing historical records É. Plésiat et al. https://doi.org/10.1038/s41467-024-53464-2
- Faster chemical mapping assisted by computer vision: insights from glass and ice core samples P. Larkman et al. https://doi.org/10.1039/D5AN00325C
- Classifying thermodynamic cloud phase using machine learning models L. Goldberger et al. https://doi.org/10.5194/amt-18-5393-2025
- Mass Estimation and Protection of Network Security Using Inception‐LSTM and Bayesian Attack Behavior Correlation Y. Lei https://doi.org/10.1002/spy2.70059
- High-Resolution Precipitation Modeling in Complex Terrains Using Hybrid Interpolation Techniques: Incorporating Physiographic and MODIS Cloud Cover Influences K. Alsafadi et al. https://doi.org/10.3390/rs15092435
- Inpainting Radar Missing Data via Denoising Iterative Restoration W. Zhang et al. https://doi.org/10.1109/JSTARS.2024.3400407
- Rad3DGFM: A Deep Learning Network for Completing Data Gaps in 3D Radar Reflectivity Mosaics X. Wang et al. https://doi.org/10.1007/s00376-026-5840-z
- A CBAM-integrated deep convolutional neural network for weather radar echo correction to overcome terrain occlusion challenge H. Wang et al. https://doi.org/10.1016/j.asr.2026.01.098
- Improving the Completion of Weather Radar Missing Data with Deep Learning A. Gong et al. https://doi.org/10.3390/rs15184568
- Research on deep learning-based missing echo restoration method for weather radar mosaic data H. Guo et al. https://doi.org/10.5194/amt-19-6341-2026
- A Radar Data-Driven AI Approach for Rainfall Nowcasting: Towards Flood Preparedness in Urban Regions S. Dandekar et al. https://doi.org/10.1007/s12524-025-02143-w
- Restoration of Automotive Radar Signals Under Mutual Interference by Digital In-Painting H. Lee et al. https://doi.org/10.1109/TIM.2024.3427748
Saved (final revised paper)
Latest update: 01 Nov 2026
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.
Radars can suffer from missing or poor-quality data regions for several reasons: beam blockage,...
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