Articles | Volume 19, issue 19
https://doi.org/10.5194/amt-19-6341-2026
https://doi.org/10.5194/amt-19-6341-2026
Research article
 | 
06 Oct 2026
Research article |  | 06 Oct 2026

Research on deep learning-based missing echo restoration method for weather radar mosaic data

Husong Guo, Muyun Du, Xiangyu Fan, Cuihong Wu, Anwei Lai, and Hedi Ma

Cited articles

Ayzel, G., Heistermann, M., and Winterrath, T.: Optical flow models as an open benchmark for radar-based precipitation nowcasting (rainymotion v0.1), Geosci. Model Dev., 12, 1387–1402, https://doi.org/10.5194/gmd-12-1387-2019, 2019. 
Bechini, R. and Chandrasekar, V.: An enhanced optical flow technique for radar nowcasting of precipitation and winds, J. Atmos. Ocean. Tech., 34, 2637–2658, https://doi.org/10.1175/JTECH-D-17-0110.1, 2017. 
Chai, T. and Draxler, R. R.: Root mean square error (RMSE) or mean absolute error (MAE)? – Arguments against avoiding RMSE in the literature, Geosci. Model Dev., 7, 1247–1250, https://doi.org/10.5194/gmd-7-1247-2014, 2014. 
Chang, Y. and Luo, B.: Bidirectional convolutional LSTM neural network for remote sensing image super-resolution, Remote Sens., 11, 2333, https://doi.org/10.3390/rs11202333, 2019. 
Esbrí, L., Rigo, T., Llasat, M. C., Biondi, R., Federico, S., Gluchshenko, O., Kerschbaum, M., Lagasio, M., Mazzarella, V., and Milelli, M.: Application of severe weather nowcasting to case studies in air traffic management, Atmosphere, 14, 1238, https://doi.org/10.3390/atmos14081238, 2023. 
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Short summary
Radar mosaic data is crucial for accurate and timely disaster weather warnings. However, missing regional data – caused by hardware failures or delayed file transfers, severely limits its quantitative use. Existing methods either struggle with complex and diverse missing patterns; or rely on known missing masks. To address this, We propose a deep learning–based radar echo restoration method that requires no explicit missing-data prior and delivers reliable, real-time performance.
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